Findings of the Association for Computational Linguistics: EMNLP 2025

Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, Violet Peng (Editors)

Anthology ID:: 2025.findings-emnlp
Month:: November
Year:: 2025
Address:: Suzhou, China
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
URL:: https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp/
DOI:: 10.18653/v1/2025.findings-emnlp
ISBN:: 979-8-89176-335-7
Bib Export formats:: BibTeX

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Findings of the Association for Computational Linguistics: EMNLP 2025
Christos Christodoulopoulos | Tanmoy Chakraborty | Carolyn Rose | Violet Peng

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Automating Alternative Generation in Decision-Making
Yevhen Kostiuk | Clara Seyfried | Chris Reed

In decision making, generating alternative solutions is crucial for solving a problem. However, cognitive biases can impede this process by constraining individual decision makers’ creativity. To address this issue, we introduce a new task for automatically generating alternatives, inspired by the process of human “brainstorming”. We define alternative options based on atomic action components and present a dataset of 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. We also introduce new metrics to assess the quality of generated components, including distinctiveness, creativity, upvote-weighted, crowd intersection, and final commit intersection scores. As a baseline, we evaluated the large language models (LLMs) LLaMa3:8b, LLaMa3.1:8b, and Gemma 2:9b on the alternative component generation task. On the one hand, models demonstrated high creativity (ability to generate options beyond what Reddit users suggested) and performed well at proposing distinct alternatives. A subset of generated components was manually evaluated and found overall useful. This indicates that LLMs might be used to extend lists of alternative options, helping decision makers consider a problem from different perspectives. On the other hand, LLMs’ outputs often failed to align with human suggestions, implying that they still tend to miss important components.

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Bias Analysis and Mitigation through Protected Attribute Detection and Regard Classification
Takuma Udagawa | Yang Zhao | Hiroshi Kanayama | Bishwaranjan Bhattacharjee

Large language models (LLMs) acquire general linguistic knowledge from massive-scale pretraining. However, pretraining data mainly comprised of web-crawled texts contain undesirable social biases which can be perpetuated or even amplified by LLMs. In this study, we propose an efficient yet effective annotation pipeline to investigate social biases in the pretraining corpora. Our pipeline consists of protected attribute detection to identify diverse demographics, followed by regard classification to analyze the language polarity towards each attribute. Through our experiments, we demonstrate the effect of our bias analysis and mitigation measures, focusing on Common Crawl as the most representative pretraining corpus.

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Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
Chenming Tang | Zhixiang Wang | Hao Sun | Yunfang Wu

With the help of in-context learning (ICL), large language models (LLMs) have achieved impressive performance across various tasks. However, the function of descriptive instructions during ICL remains under-explored. In this work, we propose an ensemble prompt framework to describe the selection criteria of multiple in-context examples, and preliminary experiments on machine translation (MT) across six translation directions confirm that this framework boosts ICL performance. But to our surprise, LLMs might not care what the descriptions actually say, and the performance gain is primarily caused by the ensemble format, since it could lead to improvement even with random descriptive nouns. We further apply this new ensemble framework on a range of commonsense, math, logical reasoning and hallucination tasks with three LLMs and achieve promising results, suggesting again that designing a proper prompt format would be much more effective and efficient than paying effort into specific descriptions.

Outline generation aims to uncover the internal content structure of a document by identifying potential chapter connections and generating corresponding summaries. A robust outline generation model strives for coherence between and within plots. However, existing methods perform well on short- and medium-length texts and struggle with generating readable outlines for very long texts (e.g., fictional literary works). The primary challenge lies in their inability to accurately segment plots within long texts. To address this issue, we propose a novel unsupervised guidance framework, LeStrTP, to guide large language model (LLM) outline generation. This framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries. Specifically, the LeStrTP framework constructs chapter-level graph from long texts and learns their embeddings. Subsequently, through Markov chain modeling chapter dependence, a unique search operator is designed to achieve plot segmentation. To facilitate research on this task, we introduce a new annotated benchmark dataset, NovOutlineSet. Experimental results demonstrate that structured plots not only enhance the coherence and integrity of generated outlines but also significantly improve their quality.

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Can Large Language Models Personalize Dialogues to Generational Styles?
Pier Felice Balestrucci | Ondrej Dusek | Luca Anselma | Alessandro Mazzei

We investigate how large language models (LLMs) can produce personalized dialogue responses, specifically focusing on whether they reflect linguistic styles pertaining to different generations: Baby Boomers, Generation X, Generation Y, and Generation Z. We create P-MultiWoZ, a personalized, generation-specific version of MultiWOZ 2.2, by prompting LLMs, and validate its alignment with the original dataset through automatic and human evaluations. To validate the appropriateness of generational linguistic traits, we introduce GeMoSC, a corpus of generation-annotated movie dialogues. Linguistic analysis and perplexity test suggest that P-MultiWoZ reflects patterns consistent with GeMoSC. Finally, a human evaluation reveals that annotators were able to mostly correctly identify the generation behind P-MultiWoZ dialogues, based only on a single query-reply pair.

Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values. This optimization typically relies on pre-collected prompts. The collection of these prompts often either requires careful human interventions or proves to be difficult to have a good coverage over all scenarios an LLM can improve over . To address this issue, we propose an alignment method based on a two-agent game, consisting of an adversarial agent and a defensive agent. The adversarial agent’s task is to generate prompts that expose the deficiencies of the defensive agent. At the same time, the defensive agent improves its performance on the prompts generated by the adversary based on feedback from the reward model. This iterative process is repeated to enhance the model’s performance. We theoretically demonstrate that, under mild assumptions, this iterative alignment process converges to a Nash equilibrium by both agents. Learning in this competitive environment results in policies with better generalization capabilities. We demonstrate the advantage of our framework using extensive experiments.

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Structural Patent Classification Using Label Hierarchy Optimization
Mengting Gui | Shufeng Hao | Chongyang Shi | Qi Zhang

Patent classification is a fundamental step in the patent examination process, directly impacting the efficiency and quality of substantive review. Existing methods mostly focus on general texts like titles and abstracts, thus ignoring the key technical content claims and the corresponding citation relationships. Meanwhile, these approaches treat labels as independent targets, failing to exploit the semantic and structural information within the label taxonomy. To address these problems, we propose a Claim Structure based Patent Classification model with Label Awareness (CSPC-LA). The method first utilizes the citation relationship of patent claim texts to construct the citation graph and the co-reference graph. Then structural graph learning is used on both graphs to mine the internal logic of patent claims. Finally, we optimize the tree hierarchy of IPC labels and employ tree propagation learning to enhance the patent representation. Extensive experiments on the latest patent classification dataset from USPTO demonstrate that the proposed method is more effective than the state-of-the-art baselines.

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Exploring Hyperbolic Hierarchical Structure for Multimodal Rumor Detection
Md Mahbubur Rahman | Shufeng Hao | Chongyang Shi | An Lao | Jinyan Liu

The rise of multimodal content on social platforms has led to the rapid spread of complex and persuasive false narratives, combining of text and images. Traditional rumor detection models attempt to identify such content by relying on textual cues or employing shallow multimodal fusion techniques. However, these methods often assume a simplistic one-to-one alignment between modalities, overlooking the richer hierarchical relationships across modalities, failing to capture the layered structure of meaning. In this paper, we present RumorCone, a novel method that employs hyperbolic geometry in order to preserve hierarchical, non-linear relationships, rather than representing them at a flat semantic level. First, RumorCone decomposes image and text content into three levels: base, mid, and high-level abstractions, and embeds them in hyperbolic space to model their tree-like semantic structure. Second, a dynamic hyperbolic multimodal attention mechanism aligns features across modalities and levels, and a flexible fusion strategy adjusts the contribution of each modality based on alignment quality. Our experiments indicate the importance of hierarchical semantic modeling for robust and interpretable multimodal rumor detection.

Neural topic modeling has substantially improved topic quality and document topic distribution compared to traditional probabilistic methods. These models often incorporate multiple loss functions. However, the disparate magnitudes of these losses can make hyperparameter tuning for these loss functions challenging, potentially creating obstacles for simultaneous optimization. While gradient-based Multi-objective Optimization (MOO) algorithms offer a potential solution, they are typically applied to shared parameters in multi-task learning, hindering their broader adoption, particularly in Neural Topic Models (NTMs). Furthermore, our experiments reveal that naïve MOO applications on NTMs can yield suboptimal results, even underperforming compared to implementations without the MOO mechanism. This paper proposes a novel approach to integrate MOO algorithms, independent of hard-parameter sharing architectures, and effectively optimizes multiple NTMs loss functions. Comprehensive evaluations on widely used benchmark datasets demonstrate that our approach significantly enhances baseline topic model performance and outperforms direct MOO applications on NTMs.

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How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code
Seonghyeon Lee | HeeJae Chon | Joonwon Jang | Dongha Lee | Hwanjo Yu

Language models (LMs) have exhibited impressive abilities in generating code from natural language requirements. In this work, we highlight the diversity of code generated by LMs as a critical criterion for evaluating their code generation capabilities. There is a lack of studies focused on assessing the diversity of generated code, which overlooks its importance in code LMs. Therefore, we propose a systematic approach to evaluate code diversity, introducing various metrics with inter-code similarity. Specifically, we introduce code clustering methods that leverages LMs’ capabilities in code understanding and reasoning, resulting in a set of metrics that represent the number of algorithms in model-generated solutions. We extensively investigate the property of model-generated solutions by contrasting them with human-written ones and quantifying the impact of various factors on code diversity: model size, temperature, instruction tuning, and problem complexity. Our analysis demonstrates that model-generated solutions exhibit low algorithmic diversity, which was neglected by the research community. Moreover, we explore methods to increase code diversity by combining solutions from different models and increasing sampling temperatures. Our findings highlight that code diversity can be enhanced with the help of heterogeneous models and setting temperature beyond 1.0 that has not been fully explored due to the functional correctness degradation. To facilitate our research direction, we publicly share our code and datasets through open-source repositories.

Adaptive learning focuses on recommending personalized materials (e.g., exercises, courses) to the unique needs of learners. Despite significant research, these methods still lag behind real teachers including two main limitations: (1) Prior methods model learner-item interactions based only on ID sequences, leading to insufficient use of both learner and item information, particularly the inability to leverage semantic content from item text; (2) The data-driven reinforcement learning frameworks struggle with stable performance in scenarios with sparse learning logs. To address these challenges, we introduce the Retrieval-enhanced Agent for Adaptive Learning (ReAL) powered by large language models (LLMs), to simulate teacher decision-making with extensive prior knowledge and teaching experience. Specifically, we approach the simulation from both internal and external perspectives. From the internal perspective, we utilize the superior natural language standing ability of LLMs to analyze item texts and learner profiles. This mechanism contributes to the generation of personalized and appropriate item candidates. From the external perspective, we simulate the teacher experience by retrieving similar learners, further ensuring the model’s performance on sparse interaction data. Furthermore, we design a reflector based on learners’ feedback to refine the recommendation process. Evaluation on three real-world datasets demonstrates the superiority of ReAL in both data utilization, recommendation accuracy and stability compared to various representative baselines.

As large language models (LLMs) advance across diverse tasks, the need for comprehensive evaluation beyond single metrics becomes increasingly important.To fully assess LLM intelligence, it is crucial to examine their interactive dynamics and strategic behaviors.We present LLMsPark, a game theory–based evaluation platform that measures LLMs’ decision-making strategies and social behaviors in classic game-theoretic settings, providing a multi-agent environment to explore strategic depth.Our system cross-evaluates 15 leading LLMs (both commercial and open-source) using leaderboard rankings and scoring mechanisms. Higher scores reflect stronger reasoning and strategic capabilities, revealing distinct behavioral patterns and performance differences across models.This work introduces a novel perspective for evaluating LLMs’ strategic intelligence, enriching existing benchmarks and broadening their assessment in interactive, game-theoretic scenarios.The benchmark and rankings are publicly available at https://llmsparks.github.io/.

Song generation focuses on producing controllable high-quality songs based on various prompts. However, existing methods struggle to generate vocals and accompaniments with prompt-based control and proper alignment. Additionally, they fall short in supporting various tasks. To address these challenges, we introduce VersBand, a multi-task song generation framework for synthesizing high-quality, aligned songs with prompt-based control. VersBand comprises these primary models: 1) VocalBand, a decoupled model, leverages the flow-matching method for generating singing styles, pitches, and mel-spectrograms, allowing fast, high-quality vocal generation with style control. 2) AccompBand, a flow-based transformer model, incorporates the Band-MOE, selecting suitable experts for enhanced quality, alignment, and control. This model allows for generating controllable, high-quality accompaniments aligned with vocals. 3) Two generation models, LyricBand for lyrics and MelodyBand for melodies, contribute to the comprehensive multi-task song generation system, allowing for extensive control based on multiple prompts. Experimental results demonstrate that VersBand performs better over baseline models across multiple song generation tasks using objective and subjective metrics.

Large language models (LLMs) typically undergo instruction tuning to enhance alignment. Recent studies emphasize that quality and diversity of instruction data are more crucial than quantity, highlighting the need to select diverse, high-quality subsets to reduce training costs. However, how to evolve these selected subsets alongside the development of new instruction data remains insufficiently explored. To achieve LLMs’ ongoing alignment, we introduce Instruction Bank (InsBank), a continuously updated repository that integrates the latest valuable instruction data. We further propose Progressive Instruction Bank Evolution (PIBE), a novel framework designed to evolve InsBank effectively and efficiently over time. PIBE employs a gradual data selection strategy to maintain long-term efficiency, leveraging a representation-based diversity score to capture relationships between data points and retain historical information for comprehensive diversity evaluation. This also allows for flexible combination of diversity and quality scores during data selection and ranking. Extensive experiments demonstrate that PIBE significantly outperforms baselines in InsBank evolution and is able to extract budget-specific subsets, demonstrating its effectiveness and adaptability.

Large language models (LLMs) achieve remarkable advancements by leveraging tools to interact with environments, a critical step toward generalized AI. However, the standard supervised fine-tuning (SFT) approach, which relies on large-scale datasets, often overlooks task-specific characteristics in tool use, leading to performance bottlenecks. To address this issue, we analyze three existing LLMs and uncover key insights: training data can inadvertently impede tool-use behavior, token importance is distributed unevenly, and errors in tool calls fall into a small set of categories. Building on these findings, we propose TL-Training, a task-feature-based framework that mitigates the effects of suboptimal training data, dynamically adjusts token weights to prioritize key tokens during SFT, and incorporates a robust reward mechanism tailored to error categories, optimized through proximal policy optimization. We validate TL-Training by training CodeLLaMA-2-7B and evaluating it on four open-source test sets. Our results demonstrate that the LLM trained by our method matches or surpasses both open- and closed-source LLMs in tool-use performance using only 1,217 training data points. Additionally, our method enhances robustness in noisy environments and improves general task performance, offering a scalable and efficient paradigm for tool-use training in LLMs. Code and data are available at https://github.com/Junjie-Ye/TL-Training.

Reasoning based on chains of thought (CoTs) enables large language models (LLMs) to solve problems by thinking step by step and becomes the mainstream solution for Question-Answering (QA) tasks. Knowledge graph (KG)-enhanced CoT technology helps correct factual errors or predict reasoning direction. Existing KG-enhanced methods find relevant information in KGs “within” each reasoning step of CoTs. However, in some cases, logical connections “between” reasoning steps may be missing or wrong, leading to broken reasoning chains and wrong reasoning direction. To solve the above problem, we argue that the errors between reasoning steps require collaborative verification and mining of multiple triplets and multiple paths in KG. So we propose the DCMKC (Dual Consistency Matching for KG and CoT) method, aiming to maintain semantic and structural consistency between KG and CoT. The main idea is to convert CoTs and KGs into two granularity-aligned graphs, transforming multi-hop reasoning and KG matching into iterative matching and modification of two graphs. In each iteration, DCMKC matches the KG reasoning chains with CoTs based on semantic similarity and judges the structural consistency between them. Then it modifies CoTs using the matched chains. After iterations, the CoTs and KG reasoning chains reach high semantic and structural consistency, which is theoretically and experimentally demonstrated by kernel and spectral methods. The two kinds of chains are then used to generate the final answers. Experimental results show that our method outperforms baselines on multiple datasets, especially on multi-answer questions, with up to 5.1% improvement over the baseline.

Adapting general multimodal large language models (MLLMs) to specific domains, such as scientific and industrial fields, is highly significant in promoting their practical applications. This paper systematically investigates domain adaptation of MLLMs via post-training, focusing on data synthesis, training pipeline, and task evaluation. (1) **Data Synthesis**: Using only open-source models, we develop a generate-then-filter pipeline that curates diverse visual instruction tasks based on domain-specific image-caption pairs. The resulting data surpass the data synthesized by manual rules or strong closed-source models in enhancing domain-specific performance. (2) **Training Pipeline**: Unlike general MLLMs that typically adopt a two-stage training paradigm, we find that a single-stage approach is more effective for domain adaptation. (3) **Task Evaluation**: We conduct extensive experiments in high-impact domains such as biomedicine, food, and remote sensing, by post-training a variety of MLLMs and then evaluating MLLM performance on various domain-specific tasks. Finally, we fully open-source our models, code, and data to encourage future research in this area.

Reinforcement Learning Fine-Tuning (RLFT) has achieved notable success in tasks with objectively verifiable answers (e.g., code generation, mathematical reasoning), yet struggles with open-ended subjective tasks like role-playing dialogue. Traditional reward modeling approaches, which rely on independent sample-wise scoring, face dual challenges: subjective evaluation criteria and unstable reward signals. Motivated by the insight that human evaluation inherently combines explicit criteria with implicit comparative judgments, we propose Comparative Policy Optimization (CPO). CPO redefines the reward evaluation paradigm by shifting from sample-wise scoring to comparative group-wise scoring. Building on the same principle, we introduce the CharacterArena evaluation framework, which comprises two stages: (1) Contextualized Multi-turn Role-playing Simulation, and (2) Trajectory-level Comparative Evaluation. By operationalizing subjective scoring via objective trajectory comparisons, CharacterArena minimizes contextual bias and enables more robust and fair performance evaluation. Empirical results on CharacterEval, CharacterBench, and CharacterArena confirm that CPO effectively mitigates reward ambiguity and leads to substantial improvements in dialogue quality.

Enhancing the numerical and logical reasoning capabilities of Large Language Models (LLMs) has become a prominent research focus. Existing approaches exhibit notable limitations: inference-phase techniques, such as Chain of Thought, depend on prompt engineering and pretrained knowledge; sentence-level Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) struggle to ensure step-wise mathematical correctness and often rely on model distillation or human annotations; Reinforcement Learning (RL) methods entail high GPU memory consumption and training instability. To overcome these challenges, we propose Self-training with Process Preference learning using Dynamic value margin (SPPD). SPPD formulates reasoning as a process-based Markov Decision Process (MDP), leveraging the Bellman optimality equation to derive a dynamic value margin for step-level preference optimization. It further incorporates tree-based self-sampling of model responses, eliminating the need for distillation. We theoretically establish that SPPD is equivalent to on-policy policy gradient methods under constrained reward functions. Experimental results on 7B-scale models show consistent superiority across both in-domain and out-of-domain mathematical benchmarks.

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Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework
Zhangyue Yin | YuHong Sun | Xuanjing Huang | Xipeng Qiu | Hui Zhao

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. Math Word Problems (MWPs) serve as a crucial benchmark for evaluating LLMs’ reasoning abilities. While most research primarily focuses on improving accuracy, it often neglects understanding and addressing the underlying patterns of errors. Current error classification methods rely on static and predefined categories, which limit their ability to capture the full spectrum of error patterns in mathematical reasoning. To enable systematic error analysis, we collect error samples from 15 different LLMs of varying sizes across four distinct MWP datasets using multiple sampling strategies. Based on this extensive collection, we introduce MWPES-300K, a comprehensive dataset containing 304,865 error samples that cover diverse error patterns and reasoning paths. To reduce human bias and enable fine-grained analysis of error patterns, we propose a novel framework for automated dynamic error classification in mathematical reasoning. Experimental results demonstrate that dataset characteristics significantly shape error patterns, which evolve from basic to complex manifestations as model capabilities increase. With deeper insights into error patterns, we propose Error-Aware Prompting (EAP) that incorporates common error patterns as explicit guidance, leading to significant improvements in mathematical reasoning performance.

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sudoLLM: On Multi-role Alignment of Language Models
Soumadeep Saha | Akshay Chaturvedi | Joy Mahapatra | Utpal Garain

User authorization-based access privileges are a key feature in many safety-critical systems, but have not been extensively studied in the large language model (LLM) realm. In this work, drawing inspiration from such access control systems, we introduce sudoLLM, a novel framework that results in multi-role aligned LLMs, i.e., LLMs that account for, and behave in accordance with, user access rights. sudoLLM injects subtle user-based biases into queries and trains an LLM to utilize this bias signal in order to produce sensitive information if and only if the user is authorized. We present empirical results demonstrating that this approach shows substantially improved alignment, generalization, resistance to prefix-based jailbreaking attacks, and “fails-closed”. The persistent tension between the language modeling objective and safety alignment, which is often exploited to jailbreak LLMs, is somewhat resolved with the aid of the injected bias signal. Our framework is meant as an additional security layer, and complements existing guardrail mechanisms for enhanced end-to-end safety with LLMs.

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DAC: Decomposed Automation Correction for Text-to-SQL
Dingzirui Wang | Longxu Dou | Xuanliang Zhang | Qingfu Zhu | Wanxiang Che

Text-to-SQL is an important task that helps access databases by generating SQL queries. Currently, correcting the generated SQL based on large language models (LLMs) automatically is an effective method to enhance the quality of the generated SQL. However, previous research shows that it is hard for LLMs to detect mistakes in SQL directly, leading to poor performance. Therefore, in this paper, we propose to employ the decomposed correction to enhance text-to-SQL performance. We first demonstrate that detecting and fixing mistakes based on the decomposed sub-tasks is easier than using SQL directly. Then, we introduce Decomposed Automation Correction (DAC), which first generates the entities and skeleton corresponding to the question, and then compares the differences between the initial SQL and the generated entities and skeleton as feedback for correction. Experimental results show that, compared with the previous automation correction method, DAC improves performance by 1.4% of Spider, Bird, and KaggleDBQA on average, demonstrating the effectiveness of DAC.

Intelligent vehicle cockpits present unique challenges for API Agents, requiring coordination across tightly-coupled subsystems that exceed typical task environments’ complexity. Traditional Function Calling (FC) approaches operate statelessly, requiring multiple exploratory calls to build environmental awareness before execution, leading to inefficiency and limited error recovery. We introduce VehicleWorld, the first comprehensive environment for the automotive domain, featuring 30 modules, 250 APIs, and 680 properties with fully executable implementations that provide real-time state information during agent execution. This environment enables precise evaluation of vehicle agent behaviors across diverse, challenging scenarios. Through systematic analysis, we discovered that direct state prediction outperforms function calling for environmental control. Building on this insight, we propose State-based Function Call (SFC), a novel approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions. Experimental results demonstrate that SFC significantly outperforms traditional FC approaches, achieving superior execution accuracy and reduced latency. We have made all implementation code publicly available on GitHub.

Multimodal Retrieval-Augmented Generation (MM-RAG) has emerged as a promising approach for enhancing the reliability and factuality of large vision-language models (LVLMs). While end-to-end loss backpropagation is infeasible due to non-differentiable operations during the forward process, current methods primarily focus on component-level optimizations, necessitate extensive component-specific training datasets and suffer from a gap between local and global optimization objectives. In this paper, we propose a new paradigm that backpropagates global rewards from the system output to each component and then transforms these rewards into specific local losses, enabling each component to perform gradient descent and thus ensuring end-to-end optimization. Specifically, we first insert two lightweight multimodal components, a query translator and an adaptive reranker, to address the heterogeneity of multimodal knowledge and the varying knowledge demands for different questions, and then tune only these inserted components using our proposed paradigm to integrate the entire system. Our method achieves SOTA performance on multiple knowledge-intensive multimodal benchmarks with high training efficiency, relying exclusively on supervised signals from an external reward model. Experimental results and our detailed analysis of the evolution of components during training collectively reveal the advantages and considerable potential of this paradigm as a promising direction for MM-RAG research.

Audio-aware large language models (ALLMs) can understand the textual and non-textual information in the audio input. In this paper, we explore using ALLMs as an automatic judge to assess the speaking styles of speeches. We use ALLM judges to evaluate the speeches generated by SLMs on two tasks: voice style instruction following and role-playing. The speaking style we consider includes emotion, volume, speaking pace, word emphasis, pitch control, and non-verbal elements. We use four spoken language models (SLMs) to complete the two tasks and use humans and ALLMs to judge the SLMs’ responses. We compare two ALLM judges, GPT-4o-audio and Gemini-2.5-pro, with human evaluation results and show that the agreement between Gemini and human judges is comparable to the agreement between human evaluators. These promising results show that ALLMs can be used as a judge to evaluate SLMs. Our results also reveal that current SLMs, even GPT-4o-audio, still have room for improvement in controlling the speaking style and generating natural dialogues.

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Evaluation of Text-to-Image Generation from a Creativity Perspective
Xinhao Wang | Xinyu Ma | ShengYong Ding | Derek F. Wong

In recent years, driven by advancements in the diffusion process, Text-to-Image (T2I) models have rapidly developed. However, evaluating T2I models remains a significant challenge. While previous research has thoroughly assessed the quality of generated images and image-text alignment, there has been little study on the creativity of these models. In this work, we defined the creativity of T2I models, inspired by previous definitions of machine creativity. We also proposed corresponding metrics and designed a method to test the reliability of the metric. Additionally, we developed a fully automated pipeline capable of transforming existing image-text datasets into benchmarks tailored for evaluating creativity, specifically through text vector retrieval and the text generation capabilities of large language models (LLMs). Finally, we conducted a series of tests and analyses on the evaluation methods for T2I model creativity and the factors influencing the creativity of the models, revealing that current T2I models demonstrate a lack of creativity. The code and benchmark will be released.

The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and reasoning systems in this domain. We present a comprehensive knowledge-enhanced system for PSCs that integrates three key components. First, we develop Perovskite-KG, a domain-specific knowledge graph constructed from 1,517 research papers, containing 23,789 entities and 22,272 relationships. Second, we create two complementary datasets: Perovskite-Chat, comprising 55,101 high-quality question-answer pairs generated through a novel multi-agent framework, and Perovskite-Reasoning, containing 2,217 carefully curated materials science problems. Third, we introduce two specialized large language models: Perovskite-Chat-LLM for domain-specific knowledge assistance and Perovskite-Reasoning-LLM for scientific reasoning tasks. Experimental results demonstrate that our system significantly outperforms existing models in both domain-specific knowledge retrieval and scientific reasoning tasks, providing researchers with effective tools for literature review, experimental design, and complex problem-solving in PSC research.

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ProPy: Building Interactive Prompt Pyramids upon CLIP for Partially Relevant Video Retrieval
Yi Pan | Yujia Zhang | Michael Kampffmeyer | Xiaoguang Zhao

Partially Relevant Video Retrieval (PRVR) is a practical yet challenging task that involves retrieving videos based on queries relevant to only specific segments. While existing works follow the paradigm of developing models to process unimodal features, powerful pretrained vision-language models like CLIP remain underexplored in this field. To bridge this gap, we propose ProPy, a model with systematic architectural adaption of CLIP specifically designed for PRVR. Drawing insights from the semantic relevance of multi-granularity events, ProPy introduces two key innovations: (1) A Prompt Pyramid, a hierarchical structure that organizes event prompts to capture semantics at multiple granularity levels, and (2) An Ancestor-Descendant Interaction Mechanism built on the pyramid that enables dynamic semantic interaction among events. With these designs, ProPy achieves SOTA performance on three public datasets, outperforming previous models by significant margins. We will release all code and checkpoints to facilitate further research.

Conversational explainable artificial intelligence (ConvXAI) systems based on large language models (LLMs) have garnered considerable attention for their ability to enhance user comprehension through dialogue-based explanations. Current ConvXAI systems often are based on intent recognition to accurately identify the user’s desired intention and map it to an explainability method. While such methods offer great precision and reliability in discerning users’ underlying intentions for English, a significant challenge in the scarcity of training data persists, which impedes multilingual generalization. Besides, the support for free-form custom inputs, which are user-defined data distinct from pre-configured dataset instances, remains largely limited. To bridge these gaps, we first introduce MultiCoXQL, a multilingual extension of the CoXQL dataset spanning five typologically diverse languages, including one low-resource language. Subsequently, we propose a new parsing approach aimed at enhancing multilingual parsing performance, and evaluate three LLMs on MultiCoXQL using various parsing strategies. Furthermore, we present Compass, a new multilingual dataset designed for custom input extraction in ConvXAI systems, encompassing 11 intents across the same five languages as MultiCoXQL. We conduct monolingual, cross-lingual, and multilingual evaluations on Compass, employing three LLMs of varying sizes alongside BERT-type models.

Enhancing large language models (LLMs) with external tools has become a promising approach for solving complex tasks. As the number of available tools grows, context-based prompting methods increasingly rely on retrieval mechanisms. A common solution is to represent each tool with a unique token and train LLMs to generate the corresponding token during inference. However, this approach suffers from linear growth in representation space, leading to scalability challenges. It also limits generalization to novel or rare tools and underutilizes collaborative signals among tools in downstream tasks. In this paper, we propose SGTC, a generative tool invocation framework that introduces structure-aware semantic tokenization to encode tools as discrete code sequences. This method ensures similar tools share subtokens, enabling compression of the representation space and facilitating token sharing for new tools. We further introduce a post-guided, multistage iterative training strategy on a shared backbone model, where collaborative signals from downstream tasks guide the dynamic refinement of tool representations. Extensive experiments on the ToolBench dataset, which includes over 47,000 APIs, demonstrate the effectiveness of SGTC across various tasks, showcasing its potential as a scalable and generalizable generative tool-using paradigm in large-scale tool usage scenarios. The code is available at https://github.com/OPilgrim/Toolscaler.

Auctions are a vital economic mechanism used to determine the market value of goods or services through competitive bidding within a specific framework. However, much of the current research primarily focuses on the bidding algorithms used within auction mechanisms. This often neglects the potential benefits of incorporating individual users’ unique preferences into the valuation process. Our theoretical and empirical analysis demonstrates that valuation errors can significantly impact the overall utility. To bridge this gap, we propose a personalized valuation framework, namely Large Language Models-powered Personalized Valuation (LaMP-Val), which integrates Large Language Models to incorporate personalized semantic preference into users valuation process. LaMP-Val integrating three components: data, learning, and evaluation. The data component tackles the challenge of building a novel dataset specifically for LLMs fine-tuning in personalized valuation modeling. The learning component introduces a diversity template to enhance LLMs’ capacity for modeling fine-grained personal valuation patterns. The evaluation component establishes a closed-loop system where LLM-generated valuations interact with bidding strategies and auction. It proposes two novel metrics to quantify valuation precision and bidding intention accuracy in personalized scenarios. Extensive experiments show that LaMP-Val more accurately captures personalized values and achieves greater profits than baseline approaches.

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Exploring Model Kinship for Merging Large Language Models
Yedi Hu | Yunzhi Yao | Ningyu Zhang | Huajun Chen | Shumin Deng

Model merging has become one of the key technologies for enhancing the capabilities and efficiency of Large Language Models (LLMs). The open-source community has driven model evolution by iteratively merging existing models. However, a principled understanding of the expected gains and underlying factors in model merging remains lacking. In this work, we examine model evolution through continual merging, analogous to biological evolution, and introduce the concept of model kinship, the degree of similarity or relatedness between LLMs. With comprehensive empirical analysis, we find that there is a certain relationship between model kinship and the performance gains after model merging, which can help guide our selection of candidate models. Inspired by this, we propose a new model merging strategy: Top-k Greedy Merging with Model Kinship, which can yield better performance on benchmark datasets. Specifically, we discover that using model kinship as a criterion can assist us in continuously performing model merging, alleviating the degradation (local optima) in model evolution, whereas model kinship can serve as a guide to escape these traps.

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MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering
Xuanliang Zhang | Dingzirui Wang | Keyan Xu | Qingfu Zhu | Wanxiang Che

Question answering on the hybrid context of tables and text (TATQA) is a critical task, with broad applications in data-intensive domains. However, existing TATQA datasets are limited to English, leading to several drawbacks: (i) They overlook the challenges of multilingual TAT-QA and cannot assess model performance in the multilingual setting. (ii) They do not reflect real-world multilingual scenarios where tables and texts frequently appear in non-English languages. To address the limitations, we propose the first multilingual TATQA dataset (MULTITAT). Specifically, we sample data from 3 mainstream TATQA datasets and translate it into 10 diverse languages. To align the model TATQA capabilities in English with other languages, we develop a baseline, Ours. Experimental results reveal that the performance on non-English data in MULTITAT drops by an average of 19.4% compared to English, proving the necessity of MULTITAT. We further analyze the reasons for this performance gap. Furthermore, Ours outperforms other baselines by an average of 3.3, demonstrating its effectiveness.

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LoRA-MGPO: Mitigating Double Descent in Low-Rank Adaptation via Momentum-Guided Perturbation Optimization
Yupeng Chang | Chenlu Guo | Yi Chang | Yuan Wu

Parameter-efficient fine-tuning (PEFT), particularly Low-Rank Adaptation (LoRA), adapts large language models (LLMs) by training only a small fraction of parameters. However, as the rank of the low-rank matrices used for adaptation increases, LoRA often exhibits an unstable “double descent” phenomenon, characterized by transient divergence in the training loss, which delays convergence and impairs generalization by causing instability due to the attraction to sharp local minima. To address this, we introduce **LoRA-MGPO**, a framework that incorporates Momentum-Guided Perturbation Optimization (MGPO). MGPO stabilizes training dynamics by mitigating the double descent phenomenon and guiding weight perturbations using momentum vectors from the optimizer’s state, thus avoiding dual gradient computations. Additionally, an adaptive normalization scheme scales the magnitude of perturbations based on an exponential moving average (EMA) of gradient norms, further enhancing stability. While EMA controls the magnitude of the perturbations, MGPO guides their direction, ensuring a more stable optimization trajectory. Experiments on a suite of natural language understanding and generation benchmarks show that LoRA-MGPO consistently achieves superior performance over LoRA and other PEFT methods. The analysis indicates that LoRA-MGPO leads to smoother loss curves, faster convergence, and improved generalization by stabilizing the training process and mitigating the attraction to sharp minima. The code is publicly available at [https://github.com/llm172/LoRA-MGPO](https://github.com/llm172/LoRA-MGPO).

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R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-task Learning
Jinda Liu | Yi Chang | Yuan Wu

Fine-tuning large language models (LLMs) is computationally expensive, and Low-Rank Adaptation (LoRA) provides a cost-effective solution by approximating weight updates through low-rank matrices. In real-world scenarios, LLMs are fine-tuned on data from multiple domains to perform tasks across various fields, embodying multi-task learning (MTL). LoRA often underperforms in such complex scenarios. To enhance LoRA’s capability in multi-task learning, we propose R-LoRA, which incorporates Multi-Head Randomization. Multi-Head Randomization diversifies the head matrices through Multi-Head Dropout and Multi-Head Random Initialization, enabling more efficient learning of task-specific features while maintaining shared knowledge representation. Our approach not only improves performance in MTL but also reduces GPU memory usage and training time. Experiments show that R-LoRA’s gains stem from increased diversity in the head matrices, demonstrating its effectiveness for multi-task learning. The code is open-sourced.

In web search scenarios, erroneous queries frequently degrade users’ experience through irrelevant results, underscoring the pivotal role of Chinese Spelling Check (CSC) systems. Although large language models (LLMs) exhibit remarkable capabilities across many tasks, they face critical challenges in the CSC scenario: (1) poor generalization to rare entities in open-domain searches, and (2) failure to adapt to temporal entity variations due to static parameters, resulting in serious over-correction issues. To tackle this, we present RACQC, a **C**hinese **Q**uery **C**orrection system with **R**etrieval-**A**ugmented Generation(RAG) and multi-task learning. Specifically, our approach (1) integrates dynamic knowledge retrieval through entity-centric RAG to address rare entities and innovatively proposes an entity-title collaborative corpus, and (2) employs contrastive correction tasks to mitigate LLM over-correction tendencies. Furthermore, we propose MDCQC, a **M**ulti-**D**omain **C**hinese **Q**uery **C**orrection benchmark to test the model’s entity correction capabilities. Extensive experiments on several datasets show that RACQC significantly outperforms existing baselines in CSC tasks. Specifically, RACQC achieves a maximum improvement of +9.92% on the search scenario benchmark and +3.2% on the general-domain dataset under the F₁ metric.

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Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models
Ercong Nie | Helmut Schmid | Hinrich Schuetze

Language confusion—where large language models (LLMs) generate unintended languages against the user’s need—remains a critical challenge, especially for English-centric models. We present the first mechanistic interpretability (MI) study of language confusion, combining behavioral benchmarking with neuron-level analysis. Using the Language Confusion Benchmark (LCB), we show that confusion points (CPs)—specific positions where language switches occur—are central to this phenomenon. Through layer-wise analysis with TunedLens and targeted neuron attribution, we reveal that transition failures in the final layers drive confusion. We further demonstrate that editing a small set of critical neurons, identified via comparative analysis with a multilingual-tuned counterpart, substantially mitigates confusion while largely preserving general competence and fluency. Our approach matches multilingual alignment in confusion reduction for many languages and yields cleaner, higher-quality outputs. These findings provide new insights into the internal dynamics of LLMs and highlight neuron-level interventions as a promising direction for robust, interpretable multilingual language modeling.

Large Language Models (LLMs) offer transformative potential across diverse fields, yet their safe and effective deployment is hindered by inherent knowledge conflicts—stemming from temporal evolution, divergent sources, and contradictory guidelines. This challenge is particularly acute in medicine, an interdisciplinary frontier for NLP. Rapid medical concept drift can lead LLMs to provide incorrect or outdated advice, impacting their utility and the broader societal benefits of NLP advances. This study introduces ConflictMedQA, a benchmark designed to systematically evaluate how LLMs manage varied knowledge conflicts in clinical guidelines. Our assessment of seven state-of-the-art models across 4,290 scenarios reveals significant difficulties in rejecting incorrect recommendations and frequent endorsement of conflicting advice, highlighting an important gap for NLP systems intended for real-world impact. We explore two fundamental mitigation approaches: retrieval-augmented generation and preference fine-tuning via direct preference optimization. While each offers improvements, their synergistic combination yields the best results. These findings emphasize the need for LLMs to discern subtle but critical guideline conflicts. This is a crucial step in advancing NLP’s capabilities and ensuring its dependable application in critical societal domains. The proposed dataset is available at https://huggingface.co/datasets/RDBH/DriftMed.

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Improving LLM Reasoning through Interpretable Role-Playing Steering
Anyi Wang | Dong Shu | Yifan Wang | Yunpu Ma | Mengnan Du

Role-playing has emerged as an effective technique for enhancing the reasoning capabilities of large language models (LLMs). However, existing methods primarily rely on prompt engineering, which often lacks stability and interpretability. In this paper, we introduce Sparse Autoencoder Role-Playing Steering (SRPS), a novel framework that identifies and manipulates internal model features associated with role-playing behavior. Our approach extracts latent representations from role-play prompts, selects the most relevant features based on activation patterns, and constructs a steering vector that can be injected into the model’s residual stream with controllable intensity. Our method enables fine-grained control over role-specific behavior and offers insights into how role information influences internal model activations. Extensive experiments across various reasoning benchmarks and model sizes demonstrate consistent performance gains. Notably, in the zero-shot chain-of-thought (CoT) setting, the accuracy of Llama3.1-8B on CSQA improves from 31.86% to 39.80%, while Gemma2-9B on SVAMP increases from 37.50% to 45.10%. These results highlight the potential of SRPS to enhance reasoning ability in LLMs, providing better interpretability and stability compared to traditional prompt-based role-playing.

Open-domain timeline summarization (TLS) faces challenges from information overload and data sparsity when processing large-scale textual streams. Existing methods struggle to capture coherent event narratives due to fragmented descriptions and often accumulate noise through iterative retrieval strategies that lack effective relevance evaluation. This paper proposes: Reflective Retrieval-Augmented Timeline Summarization with Causal-Semantic Intergration, which offers a novel perspective for open-domain TLS by time point completion and event element completion. R2A-TLS establishes an initial retrieval, reflection, and deep retrieval system that reduces noise through a double filtering mechanism that iteratively generates a timeline for each text which passes the filtering. Then, the system reflects on the initial timeline with the aim of identifying information gaps through causal chain analysis and FrameNet based element validation. These gaps are reformulated into targeted queries to trigger deep retrieval for refining timeline coherence and density. Empirical evaluation on Open-TLS dataset reveals that our approach outperforms the best prior published approaches.

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MedEBench: Diagnosing Reliability in Text-Guided Medical Image Editing
Minghao Liu | Zhitao He | Zhiyuan Fan | Qingyun Wang | Yi R. Fung

Text-guided image editing has seen significant progress in natural image domains, but its application in medical imaging remains limited and lacks standardized evaluation frameworks. Such editing could revolutionize clinical practices by enabling personalized surgical planning, enhancing medical education, and improving patient communication. To bridge this gap, we introduce MedEBench, a robust benchmark designed to diagnose reliability in text-guided medical image editing. MedEBench consists of 1,182 clinically curated image-prompt pairs covering 70 distinct editing tasks and 13 anatomical regions. It contributes in three key areas: (1) a clinically grounded evaluation framework that measures Editing Accuracy, Context Preservation, and Visual Quality, complemented by detailed descriptions of intended edits and corresponding Region-of-Interest (ROI) masks; (2) a comprehensive comparison of seven state-of-the-art models, revealing consistent patterns of failure; and (3) a diagnostic error analysis technique that leverages attention alignment, using Intersection-over-Union (IoU) between model attention maps and ROI masks to identify mislocalization issues, where models erroneously focus on incorrect anatomical regions. MedEBench sets the stage for developing more reliable and clinically effective text-guided medical image editing tools.

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FairCoT: Enhancing Fairness in Text-to-Image Generation via Chain of Thought Reasoning with Multimodal Large Language Models
Zahraa Al Sahili | Ioannis Patras | Matthew Purver

In the domain of text-to-image generative models, biases inherent in training datasets often propagate into generated content, posing significant ethical challenges, particularly in socially sensitive contexts. We introduce FairCoT, a novel framework that enhances fairness in text-to-image models through Chain-of-Thought (CoT) reasoning within multimodal generative large language models. FairCoT employs iterative CoT refinement to systematically mitigate biases, and dynamically adjusts textual prompts in real time, ensuring diverse and equitable representation in generated images. By integrating iterative reasoning processes, FairCoT addresses the limitations of zero-shot CoT in sensitive scenarios, balancing creativity with ethical responsibility. Experimental evaluations across popular text-to-image systems—including DALL-E and various Stable Diffusion variants—demonstrate that FairCoT significantly enhances fairness and diversity without sacrificing image quality or semantic fidelity. By combining robust reasoning, lightweight deployment, and extensibility to multiple models, FairCoT represents a promising step toward more socially responsible and transparent AI-driven content generation.

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Bag of Tricks for Sparse Mixture-of-Experts: A Benchmark Across Reasoning, Efficiency, and Safety
Mufan Qiu | Zheyu Shen | Pingzhi Li | Ang Li | Tianlong Chen

Mixture-of-Experts (MoE) has emerged as a promising approach for scaling large language models efficiently. However, how to design a desired MoE architecture given performance, efficiency, or safety goals remains absent. Existing benchmarks often focus on isolated aspects (e.g., reasoning, efficiency, safety), and there is a lack of consensus on optimal design choices, such as the number and size of experts, the type of routers, and the regularization during pre-training, or strategies like freezing, learning rate adjustments, and limiting expert collaboration during fine-tuning, with prior works often yielding conflicting conclusions. Motivated by this research gap, we introduce MoEBench, the first comprehensive assessment of MoE designs across the three dimensions of reasoning ability, efficiency, and safety. Our benchmark systematically evaluates optimal architectural choices during both pre-training and fine-tuning phases. We evaluate two popular MoE backbones across four dimensions of design choices on over eight metrics. Our empirical findings uncover hidden underlying correlations among MoE design choices. Specifically, we observe that (1) token-level routing and z-loss regularization improve reasoning performance; (2) shared experts enhance training stability but reduce specialization; and (3) collaboration-constrained routing and freezing strategies significantly influence load balance, specialization, and safety alignment. Furthermore, we propose three “sweet point” combinations of optimal strategies tailored to different scenarios. We hope this study provides actionable insights for building more robust, efficient, and secure MoE models. Code, checkpoints, and raw data will be released upon acceptance of the paper.

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Don’t Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models
Jinzhe Li | Gengxu Li | Yi Chang | Yuan Wu

Large language models (LLMs) have witnessed rapid advancements, demonstrating remarkable capabilities. However, a notable vulnerability persists: LLMs often uncritically accept flawed or contradictory premises, leading to inefficient reasoning and unreliable outputs. This emphasizes the significance of possessing the **Premise Critique Ability** for LLMs, defined as the capacity to proactively identify and articulate errors in input premises. Most existing studies assess LLMs’ reasoning ability in ideal settings, largely ignoring their vulnerabilities when faced with flawed premises. Thus, we introduce the **Premise Critique Bench (PCBench)**, designed by incorporating four error types across three difficulty levels, paired with multi-faceted evaluation metrics. We conducted systematic evaluations of 15 representative LLMs, Our findings reveal: (1) Most models rely heavily on explicit prompts to detect errors, with limited autonomous critique; (2) Premise critique ability depends on question difficulty and error type, with direct contradictions being easier to be detected than complex or procedural errors; (3) Reasoning ability does not consistently correlate with the premise critique ability; (4) Flawed premises trigger overthinking in reasoning models, markedly lengthening responses due to repeated attempts at resolving conflicts. These insights underscore the urgent need to enhance LLMs’ proactive evaluation of input validity, positioning premise critique as a foundational capability for developing reliable, human-centric systems.

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Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
Shengyuan Wang | Jie Feng | Tianhui Liu | Dan Pei | Yong Li

Large language models (LLMs) possess extensive world knowledge, including geospatial knowledge, which has been successfully applied to various geospatial tasks such as mobility prediction and social indicator prediction. However, LLMs often generate inaccurate geospatial knowledge, leading to geospatial hallucinations—incorrect or inconsistent representations of geospatial information—that compromise their reliability. While the phenomenon of general knowledge hallucination in LLMs has been widely studied, the systematic evaluation and mitigation of geospatial hallucinations remain largely unexplored. To address this gap, we propose a comprehensive evaluation framework for geospatial hallucinations, leveraging structured geospatial knowledge graphs for controlled assessment. Through extensive evaluation across 20 advanced LLMs, we uncover the hallucinations in their geospatial knowledge. Building on these insights, we introduce a dynamic factuality aligning method based on Kahneman-Tversky Optimization (KTO) to mitigate geospatial hallucinations in LLMs, leading to a performance improvement of over 29.6% on the proposed benchmark. Extensive experimental results demonstrate the effectiveness of our benchmark and learning algorithm in enhancing the trustworthiness of LLMs in geospatial knowledge and reasoning tasks.

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The Power of Framing: How News Headlines Guide Search Behavior
Amrit Poudel | Maria Milkowski | Tim Weninger

Search engines play a central role in how people gather information, but subtle cues like headline framing may influence not only what users believe but also how they search. While framing effects on judgment are well documented, their impact on subsequent search behavior is less understood. We conducted a controlled experiment where participants issued queries and selected from headlines filtered by specific linguistic frames. Headline framing significantly shaped follow-up queries: conflict and strategy frames disrupted alignment with prior selections, while episodic frames led to more concrete queries than thematic ones. We also observed modest short-term frame persistence that declined over time. These results suggest that even brief exposure to framing can meaningfully alter the direction of users’ information-seeking behavior.

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DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models
Tsz Ting Chung | Lemao Liu | Mo Yu | Dit-Yan Yeung

Logic reasoning in natural language has been recognized as an important measure of human intelligence for Large Language Models (LLMs). Popular benchmarks may entangle multiple reasoning skills and thus provide unfaithful evaluations on the logic reasoning skill. Meanwhile, existing logic reasoning benchmarks are limited in language diversity and their distributions are deviated from the distribution of an ideal logic reasoning benchmark, which may lead to biased evaluation results. This paper thereby proposes a new classical logic benchmark DivLogicEval, consisting of natural sentences composed of diverse statements in a counterintuitive way. To ensure a more reliable evaluation, we also introduce a new evaluation metric that mitigates the influence of bias and randomness inherent in LLMs. Through experiments, we demonstrate the extent to which logical reasoning is required to answer the questions in DivLogicEval and compare the performance of different popular LLMs in conducting logical reasoning.

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THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction
Xin Zhang | Qiyu Wei | Yingjie Zhu | Fanyi Wu | Sophia Ananiadou

Automated clinical risk prediction from electronic health records (EHRs) demands modeling both structured diagnostic codes and unstructured narrative notes. However, most prior approaches either handle these modalities separately or rely on simplistic fusion strategies that ignore the directional, hierarchical causal interactions by which narrative observations precipitate diagnoses and propagate risk across admissions. In this paper, we propose **THCM-CAL**, a Temporal-Hierarchical Causal Model with Conformal Calibration. Our framework constructs a multimodal causal graph where nodes represent clinical entities from two modalities: textual propositions extracted from notes and ICD codes mapped to textual descriptions. Through hierarchical causal discovery, **THCM-CAL** infers three clinically grounded interactions: intra-slice same-modality sequencing, intra-slice cross-modality triggers, and inter-slice risk propagation. To enhance prediction reliability, we extend conformal prediction to multi-label ICD coding, calibrating per-code confidence intervals under complex co-occurrences. Experimental results on MIMIC-III and MIMIC-IV demonstrate the superiority of **THCM-CAL**.

Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are model-specific and require training, while prior automatic prompt optimization (APO) approaches typically lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. Meanwhile, test-time scaling methods operate on fixed prompts and on noise or sample numbers, limiting their interpretability and adaptability. To solve these, we introduce a flexible and efficient test-time prompt optimization strategy that operates directly on the input text. We propose a plug-and-play multi-agent system called GenPilot, integrating error analysis, clustering-based adaptive exploration, fine-grained verification, and a memory module for iterative optimization. Our approach is model-agnostic, interpretable, and well-suited for handling long and complex prompts. Simultaneously, we summarize the common patterns of errors and the refinement strategy, offering more experience and encouraging further exploration. Experiments on DPG-bench and Geneval with improvements of up to 16.9% and 5.7% demonstrate the strong capability of our methods in enhancing the text and image consistency and structural coherence of generated images, revealing the effectiveness of our test-time prompt optimization strategy. The code is available at https://github.com/27yw/GenPilot.

Despite their impressive performance in coarse-grained video understanding, Video Large Language Models (Video-LLMs) still face challenges in fine-grained temporal grounding, including ineffective temporal modeling and inadequate timestamp representations. In this work, we introduce Grounded-VideoLLM, a novel Video-LLM designed to perceive and reason over specific video moments with fine-grained temporal precision. Our model features (1) a two-stream encoder that explicitly captures inter-frame relationships while preserving intra-frame visual details and (2) discrete temporal tokens enriched with structured time knowledge for timestamp representation. Besides, we propose a multi-stage training strategy tailored to such grounding-specific architecture. The model is initially trained on simple video-caption tasks and progressively introduced to complex video temporal grounding tasks, ensuring a smooth learning curve and temporal alignment. We further strengthen Grounded-VideoLLM’s temporal reasoning by constructing a VideoQA dataset with grounded information using an automated annotation pipeline. Extensive experiments demonstrate that Grounded-VideoLLM not only surpasses existing models in fine-grained grounding tasks but also exhibits strong potential as a general video understanding assistant.

Dongba pictographic is the only pictographic script still in use in the world. Its pictorial ideographic features carry rich cultural and contextual information. However, due to the lack of relevant datasets, research on semantic understanding of Dongba hieroglyphs has progressed slowly. To this end, we constructed DongbaMIE - the first dataset focusing on multimodal information extraction of Dongba pictographs. The dataset consists of images of Dongba hieroglyphic characters and their corresponding semantic annotations in Chinese. It contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs. The annotations cover four semantic dimensions: object, action, relation and attribute. Systematic evaluation of mainstream multimodal large language models shows that the models are difficult to perform information extraction of Dongba hieroglyphs efficiently under zero-shot and few-shot learning. Although supervised fine-tuning can improve the performance, accurate extraction of complex semantics is still a great challenge at present.

Federated domain-specific instruction tuning (FedDIT) for large language models (LLMs) aims to enhance performance in specialized domains using distributed private and limited data, yet identifying key performance drivers and optimal augmentation strategies remains challenging. We empirically establish that cross-client domain coverage, rather than data heterogeneity, is the pivotal factor. We then introduce FedDCA, an algorithm that explicitly maximizes this coverage through diversity-oriented client center selection and retrieval-based augmentation, constructing diverse, non-redundant cross-client instruction sets. Extensive experiments across multiple domains demonstrate FedDCA’s superiority over eleven baselines, achieving performance gains of up to 29.19% and domain coverage improvements of 4.82%-21.36%. FedDCA maintains its effectiveness in diverse and challenging scenarios, including data selection, held-out settings where task-specific public data is scarce and various data heterogeneity, with manageable privacy risks. This work clarifies critical FedDIT dynamics and presents FedDCA as an effective, privacy-preserving, and scalable solution for advancing domain-specific LLM tuning.

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Aligning Black-Box LLMs for Aspect Sentiment Quad Prediction
Shichen Li | Jiawei Zhang | Zhongqing Wang | Peifeng Li

Aspect-Based Sentiment Analysis (ABSA) focuses on extracting opinions about specific aspects, with Aspect Sentiment Quad Prediction (ASQP) being the most complex sub-task. Large language models (LLMs) like GPT4 exhibit strong generalization yet struggle with ASQP due to a lack of task-specific alignment. Supervised small language models (SLMs), while effective in capturing task-specific patterns, lack the extensive knowledge of LLMs. To address this, we propose a framework that combines SLMs and LLMs using supervised in-context learning to align LLM outputs with human preferences. One SLM is supervised to generate candidate answers and guide LLMs with task-specific instructions, while another SLM acts as a reward model iteratively evaluates and refines LLM outputs. Experiments show that our framework significantly improves ASQP performance, demonstrating robustness, scalability, and potential for advancing alignment techniques in sentiment analysis.

Multi-modal large language models (MLLMs) have recently achieved great success in processing and understanding information from diverse modalities (e.g., text, audio, and visual signals). Despite their growing popularity, there remains a lack of comprehensive evaluation measuring the audio-visual capabilities of these models, especially in diverse scenarios (e.g., distribution shifts and adversarial attacks). In this paper, we present a multifaceted evaluation of the audio-visual capability of MLLMs, focusing on four key dimensions: effectiveness, efficiency, generalizability, and robustness. Through extensive experiments, we find that MLLMs exhibit strong zero-shot and few-shot generalization abilities, enabling them to achieve great performance with limited data. However, their success relies heavily on the vision modality, which impairs performance when visual input is corrupted or missing. Additionally, while MLLMs are susceptible to adversarial samples, they demonstrate greater robustness compared to traditional models. The experimental results and our observations provide new insights into the audio-visual capabilities of MLLMs, highlighting areas for improvement and offering guidance for future research.

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Two Steps from Hell: Compositionality on Chemical LMs
Veronika Ganeeva | Kuzma Khrabrov | Artur Kadurin | Elena Tutubalina

This paper investigates compositionality in chemical language models (ChemLLMs). We introduce STEPS, a benchmark with compositional questions that reflect intricate chemical structures and reactions, to evaluate models’ understanding of chemical language. Our approach focuses on identifying and analyzing compositional patterns within chemical data, allowing us to evaluate how well existing LLMs can handle complex queries. Experiments with state-of-the-art ChemLLMs show significant performance drops in compositional tasks, highlighting the need for models that move beyond pattern recognition. By creating and sharing this benchmark, we aim to enhance the development of more capable chemical LLMs and provide a resource for future research on compositionality in chemical understanding.

In natural language processing (NLP) tasks, pure reinforcement learning fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tuning (SFT) methods, although efficient in training, have limited performance ceiling and less solid theoretical foundation compared to reinforcement learning. To address efficiency-capability trade-off, we propose the Guess-Think-Answer (GTA) framework that combines the efficiency of SFT with the capability gains of RL in a unified training paradigm. GTA works by having the model first produce a provisional guess (optimized via cross-entropy loss), then reflect on this guess before generating the final answer, with RL rewards shaping both the final output and the format of the entire GTA structure. This hybrid approach achieves both faster convergence than pure RL and higher performance ceiling than pure SFT. To mitigate gradient conflicts between the two training signals, we employ loss masking and gradient constraints. Empirical results on three text classification benchmarks demonstrate that GTA substantially accelerates convergence while outperforming both standalone SFT and RL baselines.

Recent advances in reasoning language models have witnessed a paradigm shift from short to long CoT pattern. Given the substantial computational cost of rollouts in long CoT models, maximizing the utility of fixed training datasets becomes crucial. Our analysis reveals that negative responses contain valuable components such as self-reflection and error-correction steps, yet primary existing methods either completely discard negative samples (RFT) or apply equal penalization across all tokens (RL), failing to leverage these potential learning signals. In light of this, we propose Behavior Constrained Policy Gradient with Negative Sample Augmentation (BCPG-NSA), a fine-grained offline RL framework that encompasses three stages: 1) sample segmentation, 2) consensus-based step correctness assessment combining LLM and PRM judgers, and 3) policy optimization with NSA designed to effectively mine positive steps within negative samples. Experimental results show that BCPG-NSA outperforms baselines on several challenging math/coding reasoning benchmarks using the same training dataset, achieving improved sample efficiency and demonstrating robustness and scalability when extended to multiple iterations.

This paper studies the problem of time series forecasting, which aims to generate future predictions given historical trajectories. Recent researchers have applied large language models (LLMs) into time series forecasting, which usually align the time series space with textual space and output future predictions with strong autoregressive reasoning abilities. Despite their remarkable progress, these approaches usually lack an understanding of holistic temporal patterns with potential error accumulation. Towards this end, this paper proposes a simple yet effective framework that marries ̲Larg ̲e Langu ̲age Diffusion Model with time series ̲forecasting (LEAF). The core of our framework is to generate future predictions with a diffusion model from a holistic view. In particular, we first introduce a tokenization module to convert time series into tokens and then adopt the language diffusion models to capture the temporal dependencies. In this way, we can transform masked time series into all the predictions with the remasking strategy. Extensive experiments on various benchmark datasets validate the effectiveness of the proposed LEAF in comparison to various baselines.

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SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning
Yuhao Zhang | Shaoming Duan | Jinhang Su | Chuanyi Liu | Peiyi Han

Despite the significant advancements of self-play fine-tuning (SPIN), which can transform a weak large language model (LLM) into a strong one through competitive interactions between models of varying capabilities, it still faces challenges in the Text-to-SQL task. SPIN does not generate new information, and the large number of correct SQL queries produced by the opponent model during self-play reduces the main model’s ability to generate accurate SQL queries. To address this challenge, we propose a new self-play fine-tuning method tailored for the Text-to-SQL task, called SPFT-SQL. Prior to self-play, we introduce a verification-based iterative fine-tuning approach, which synthesizes high-quality fine-tuning data iteratively based on the database schema and validation feedback to enhance model performance, while building a model base with varying capabilities. During the self-play fine-tuning phase, we propose an error-driven loss method that incentivizes incorrect outputs from the opponent model, enabling the main model to distinguish between correct SQL and erroneous SQL generated by the opponent model, thereby improving its ability to generate correct SQL. Extensive experiments and in-depth analyses on six open-source LLMs and five widely used benchmarks demonstrate that our approach outperforms existing state-of-the-art (SOTA) methods.

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Multilingual Verbalisation of Knowledge Graphs
Yifei Song | William Soto Martinez | Anna Nikiforovskaya | Evan Parker Kelly Chapple | Claire Gardent

Most work on Knowledge Graph (KG) verbalisation is monolingual leaving open the question of how to scale KG-to-Text generation to languages with varying amounts of resources. In this work, we explore KG-to-Text generation on nine languages including five high-resource (HR) languages (English, Chinese, French, Spanish, Russian) and four low-resource (LR) languages (Breton, Irish, Maltese, Welsh). We first construct silver multilingual training data for all nine languages and new gold out-of-domain test data for the five HR languages. Using this data and already available in-domain test sets for 7 of our 9 languages, we then compare three strategies: (1) NLG+MT—a state-of-the-art KG-to-English model followed by Machine Translation (MT) into the target language; (2) FTMT—multilingual MT models fine-tuned end-to-end on the silver data; and (3) FewShot—few-shot LLM prompting comparing 4 LLMs. We explore different prompting strategies and show that our best prompting strategy performs the best on all 9 languages, discussing the relative performance of the three approaches on Low vs High Resource languages and on in- vs out-of-domain data.The models, the test set, and the silver training data are available at https://github.com/MeloS7/Multilingual-KG-Verbalisation.

A core barrier preventing recommender systems from reaching their full potential lies in the inherent limitations of user-item interaction data: (1) Sparse user-item interactions, making it difficult to learn reliable user preferences; (2) Traditional contrastive learning methods often treat negative samples as equally hard or easy, ignoring the informative semantic difficulty during training. (3) Modern LLM-based recommender systems, on the other hand, discard all negative feedback, leading to unbalanced preference modeling. To address these issues, we propose LAGCL4Rec, a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation. Our approach operates through three stages: (i) Data-Level: augmenting sparse interactions with balanced positive and negative samples using LLM-enriched profiles; (ii) Rank-Level: assessing semantic difficulty of negative samples through LLM-based grouping for fine-grained contrastive learning; and (iii) Rerank-Level: reasoning over augmented historical interactions for personalized recommendations. Theoretical analysis proves that LAGCL4Rec achieves effective information utilization with minimal computational overhead. Experiments across multiple benchmarks confirm our method consistently outperforms state-of-the-art baselines.

Large language models (LLMs) excel in many tasks, but their safety guarantees vary by language, e.g., responses in English tend to be safer than those in low-resource languages. This inconsistency creates a vulnerability, since an attacker can circumvent safety measures by using a less-supported language as an intermediary, even without fluency in that language. Traditional solutions rely on multilingual safety alignment, which demands vast, per-language datasets and introduces significant trade-offs between usefulness and safety (the so-called “alignment tax”). To overcome these limitations, we introduce English as Defense Proxy (E-Proxy), a unified approach that leverages English, usually the advantage language of LLMs, as a universal safety anchor. During multilingual training, E-Proxy uses English jailbreak prompts to extract the model’s existing safety knowledge, then applies simple language-mapping prompts (e.g., “Please answer in target language”) to transfer that knowledge across languages. Our analysis shows that formulating prompts in a high-resource language preserves the model’s utility, while enforcing responses in the target language significantly enhances safety. We evaluate E-Proxy on extensive benchmarks of both attack resistance and task performance. On the MultiJail benchmark, E-Proxy blocks over 99 % of jailbreak attempts while retaining 95 % of average task performance, all with a simply constructed multilingual alignment data.

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Dagger Behind Smile: Fool LLMs with a Happy Ending Story
Xurui Song | Zhixin Xie | Shuo Huai | Jiayi Kong | Jun Luo

The wide adoption of Large Language Models (LLMs) has attracted significant attention from jailbreak attacks, where adversarial prompts crafted through optimization or manual design exploit LLMs to generate malicious contents. However, optimization-based attacks have limited efficiency and transferability, while existing manual designs are either easily detectable or demand intricate interactions with LLMs. In this paper, we first point out a novel perspective for jailbreak attacks: LLMs are more responsive to positive prompts. Based on this, we deploy Happy Ending Attack (HEA) to wrap up a malicious request in a scenario template involving a positive prompt formed mainly via a happy \ ending, it thus fools LLMs into jailbreaking either immediately or at a follow-up malicious request. This has made HEA both efficient and effective, as it requires only up to two turns to fully jailbreak LLMs. Extensive experiments show that our HEA can successfully jailbreak on state-of-the-art LLMs, including GPT-4o, Llama3-70b, Gemini-pro, and achieves 88.79% attack success rate on average. We also provide quantitative explanations for the success of HEA.

Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs is often compromised by object hallucinations. We identify that a key cause of these hallucinations is the model’s over-susceptibility to image frequency features in detecting objects. In this paper, we introduce Multi-Frequency Perturbations (MFP), a simple, cost-effective, and pluggable adversarial training method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference, thereby mitigating hallucinations. Experimental results demonstrate that our method significantly mitigates object hallucinations across various model architectures. Furthermore, as a training-time method, MFP can be combined with inference-time methods to achieve state-of-the-art performance on the CHAIR benchmark.

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Natural Context Drift Undermines the Natural Language Understanding of Large Language Models
Yulong Wu | Viktor Schlegel | Riza Batista-Navarro

How does the natural evolution of context paragraphs affect Question Answering (QA) in generative Large Language Models (LLMs)? To address this, we propose a framework for curating naturally evolved, human-edited variants of reading passages from contemporary QA benchmarks and for analysing LLM performance across a range of semantic similarity scores, which quantify how closely each variant aligns with Wikipedia content on the same article topic that the LLM saw during pretraining. Using this framework, we evaluate 6 QA datasets and 8 LLMs with publicly available training data. Our experiments reveal that LLM performance declines as reading passages naturally diverge from the versions encountered during pretraining–even when the question and all necessary information remains present at inference time. For instance, average accuracy on BoolQ drops by over 30% from the highest to lowest similarity bins. This finding suggests that natural text evolution may pose a significant challenge to the language understanding capabilities of fully open-source LLMs.

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Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRA
Patryk Marszałek | Klaudia Bałazy | Jacek Tabor | Tomasz Kuśmierczyk

Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large language models by decomposing weight updates into low-rank matrices, significantly reducing storage and computational overhead. While effective, standard LoRA lacks mechanisms for uncertainty quantification, leading to overconfident and poorly calibrated models. Bayesian variants of LoRA address this limitation, but at the cost of a significantly increased number of trainable parameters, partially offsetting the original efficiency gains. Additionally, these models are harder to train and may suffer from unstable convergence.In this work, we propose a novel parameter-efficient Bayesian LoRA via subspace inference, demonstrating that effective uncertainty quantification can be achieved in very low-dimensional parameter spaces. The proposed method achieves strong performance with improved calibration and generalization while maintaining computational efficiency. Our empirical findings show that, with the appropriate projection of the weight space: (1) uncertainty can be effectively modeled in a low-dimensional space, and (2) weight covariances exhibit low ranks.

Large Language Models (LLMs) often exhibit hallucinations, generating factually incorrect or semantically irrelevant content in response to prompts. Chain-of-Thought (CoT) prompting can mitigate hallucinations by encouraging step-by-step reasoning, but its impact on hallucination detection remains underexplored. To bridge this gap, we conduct a systematic empirical evaluation. We begin with a pilot experiment, revealing that CoT reasoning significantly affects the LLM’s internal states and token probability distributions. Building on this, we evaluate the impact of various CoT prompting methods on mainstream hallucination detection methods across both instruction-tuned and reasoning-oriented LLMs. Specifically, we examine three key dimensions: changes in hallucination score distributions, variations in detection accuracy, and shifts in detection confidence. Our findings show that while CoT prompting helps reduce hallucination frequency, it also tends to obscure critical signals used for detection, impairing the effectiveness of various detection methods. Our study highlights an overlooked trade-off in the use of reasoning. Code is publicly available at: https://github.com/ECNU-Text-Computing/cot-hallu-detect .

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Large Language Model Evaluation via Matrix Nuclear-Norm
Yahan Li | Tingyu Xia | Yuan Wu | Yi Chang

As large language models (LLMs) continue to evolve, efficient evaluation metrics are vital for assessing their ability to compress information and reduce redundancy. While traditional metrics like Matrix Entropy offer valuable insights, they are computationally intensive for large-scale models due to their O(n³) time complexity with Singular Value Decomposition (SVD). To mitigate this issue, we introduce the Matrix Nuclear-Norm, which not only serves as a metric to quantify the data compression proficiency of LLM but also provides a convex approximation of matrix rank to capture both predictive discriminability and diversity. By employing the L_1,2-norm to further approximate the nuclear norm, we can effectively assess the model’s information compression capabilities. This approach reduces the time complexity to O(n²) and eliminates the need for SVD computation. Consequently, the Matrix Nuclear-Norm achieves speeds 8 to 24 times faster than Matrix Entropy for the CEREBRAS-GPT model as sizes increase from 111M to 6.7B. This performance gap becomes more pronounced with larger models, as validated in tests with other models like Pythia. Additionally, evaluations on benchmarks and model responses confirm that our proposed Matrix Nuclear-Norm is a reliable, scalable, and efficient tool for assessing LLMs’ performance, striking a balance between accuracy and computational efficiency.

Foundation models (FMs) are increasingly applied to bridge language and action in embodied agents, yet the operational characteristics of different integration strategies remain under-explored—especially for complex instruction following and versatile action generation in changing environments. We investigate three paradigms for robotic systems: end-to-end vision-language-action models (VLAs) that implicitly unify perception and planning, and modular pipelines using either vision-language models (VLMs) or multimodal large language models (MLLMs). Two case studies frame the comparison: instruction grounding, which probs fine-grained language understanding and cross-modal disambiguation; and object manipulation, which targets skill transfer via VLA finetuning. Our experiments reveal trade-offs in system scale, generalization and data efficiency. These findings indicate design lessons for language-driven physical agents and point to challenges and opportunities for FM-powered robotics in real-world conditions.

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Flexible Thinking for Multimodal Emotional Support Conversation via Reinforcement Learning
Fanfan Wang | Xiangqing Shen | Jianfei Yu | Rui Xia

Emotional Support Conversation (ESC) systems aim to alleviate user distress. However, current Chain-of-Thought based ESC methods often employ rigid, text-only reasoning, limiting adaptability in dynamic, multimodal interactions and introducing reasoning noise that degrades support quality. To address this, we introduce “Flexible Thinking” for multimodal ESC, enabling models to adaptively select contextually relevant thinking aspects: Visual Scene, Emotion, Situation, and Response Strategy. We first construct training data by manually curating flexible thinking demonstrations on the MESC dataset, then using a Multimodal Large Language Model to synthesize these processes for the full training set. Then, we propose FIRES, a framework integrating Supervised Fine-Tuning (SFT) for initial learning with Reinforcement Learning for refinement. This two-stage approach helps FIRES transcend SFT’s generalization limits and, crucially, directly links thinking processes to response quality via tailored rewards, moving beyond imitating potentially imperfect synthetic data. Experiments on MESC and EMOTyDA datasets demonstrate FIRES’s effectiveness and generalizability in fostering higher-quality emotional support responses through adaptive reasoning.

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ORAL: Prompting Your Large-Scale LoRAs via Conditional Recurrent Diffusion
Rana Shahroz | Dongwen Tang | Pingzhi Li | Kai Wang | Tianlong Chen

Parameter generation has emerged as a novel paradigm for neural network development, offering an alternative to traditional neural network training by synthesizing high-quality model weights directly. In the context of Low-Rank Adaptation (LoRA) for evolving (i.e, constantly updated) large language models (LLMs), this approach promises efficient adaptation without costly retraining. However, existing methods face critical limitations in simultaneously achieving scalability and controllability. In this paper, we introduce ORAL, a novel conditional recurrent diffusion framework that addresses these challenges. ORAL incorporates a novel conditioning mechanism that integrates model architecture and textual task specifications, enabling the generation of task-specific LoRA parameters that can seamlessly transfer across evolving foundation models. Our approach successfully scales to billions-of-parameter LLMs and maintains controllability. Through extensive experiments across seven language tasks, four vision tasks, and three multimodal tasks using five pre-trained LLMs, we demonstrate that ORAL generates high-quality LoRA parameters that achieve comparable or superior performance to vanilla trained counterparts.

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NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models
Chenlu Guo | Yi Chang | Yuan Wu

Parameter-efficient fine-tuning (PEFT) is essential for adapting large language models (LLMs), with low rank adaptation (LoRA) being the most popular approach. However, LoRA suffers from slow convergence, and some recent LoRA variants, such as PiSSA, primarily rely on Singular Value Decomposition (SVD) for initialization, leading to expensive computation. To mitigate these problems, we resort to Nyström method, which follows a three-matrix manipulation. Therefore, we first introduce StructuredLoRA (SLoRA), investigating to introduce a small intermediate matrix between the low-rank matrices (A) and (B). Secondly, we propose NyströmLoRA (NLoRA), which leverages Nyström-based initialization for SLoRA to improve its effectiveness and efficiency. Finally, we propose IntermediateTune (IntTune) to explore fine-tuning exclusively the intermediate matrix of NLoRA to furthermore boost LLMs’ efficiency. We evaluate our methods on 5 natural language generation (NLG) tasks and 8 natural language understanding (NLU) tasks. On GSM8K, SLoRA and NLoRA achieve accuracies of 56.48% and 57.70%, surpassing LoRA by 33.52% and 36.41% with only 3.67M additional trainable parameters. IntTune boosts average NLG performance over LoRA by 7.45% while using only 1.25% of its parameters. These results demonstrate the efficiency and effectiveness of our approach in enhancing model performance with minimal parameter overhead.

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Bhaasha, Bhāṣā, Zaban: A Survey for Low-Resourced Languages in South Asia – Current Stage and Challenges
Sampoorna Poria | Xiaolei Huang

Rapid developments of large language models have revolutionized many NLP tasks for English data. Unfortunately, the models and their evaluations for low-resource languages are being overlooked, especially for languages in South Asia. Although there are more than 650 languages in South Asia, many of them either have very limited computational resources or are missing from existing language models.Thus, a concrete question to be answered is: _Can we assess the current stage and challenges to inform our NLP community and facilitate model developments for South Asian languages?_ In this survey, we have comprehensively examined current efforts and challenges of NLP models for South Asian languages by retrieving studies since 2020, with a focus on transformer-based models, such as BERT, T5, & GPT. We present advances and gaps across 3 essential aspects: data, models, & tasks, such as available data sources, fine-tuning strategies, & domain applications. Our findings highlight substantial issues, including missing data in critical domains (e.g., health), code-mixing, and lack of standardized evaluation benchmarks. Our survey aims to raise awareness within the NLP community for more targeted data curation, unify benchmarks tailored to cultural and linguistic nuances of South Asia, and encourage an equitable representation of South Asian languages. The complete list of resources is available at: [https://github.com/trust-nlp/LM4SouthAsia-Survey](https://github.com/trust-nlp/LM4SouthAsia-Survey).

Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). Among RLHF methods, Group Relative Policy Optimization (GRPO) has gained attention for its simplicity and strong performance, notably eliminating the need for a learned value function. However, GRPO implicitly assumes a balanced domain distribution and uniform semantic alignment across groups—assumptions that rarely hold in real-world datasets. When applied to multi-domain, imbalanced data, GRPO disproportionately optimizes for dominant domains, neglecting underrepresented ones and resulting in poor generalization and fairness. We propose Domain-Informed Self-Consistency Policy Optimization (DISCO), a principled extension to GRPO that addresses inter-group imbalance with two key innovations. Domain-aware reward scaling counteracts frequency bias by reweighting optimization based on domain prevalence. Difficulty-aware reward scaling leverages prompt-level self-consistency to identify and prioritize uncertain prompts that offer greater learning value. Together, these strategies promote more equitable and effective policy learning across domains. Extensive experiments across multiple LLMs and skewed training distributions show that DISCO improves generalization, outperforms existing GRPO variants by 5% on Qwen3 models, and sets new state-of-the-art results on multi-domain alignment benchmarks.

This paper establishes a formal information-theoretic framework for image captioning, conceptualizing captions as compressed linguistic representations that selectively encode semantic units in images. Our framework posits that good image captions should balance three key aspects: informationally sufficient, minimally redundant, and readily comprehensible by humans. By formulating these aspects as quantitative measures with adjustable weights, our framework provides a flexible foundation for analyzing and optimizing image captioning systems across diverse task requirements. To demonstrate its applicability, we introduce the Pyramid of Captions (PoCa) method, which generates enriched captions by integrating local and global visual information. We present both theoretical proof that PoCa improves caption quality under certain assumptions, and empirical validation of its effectiveness across various image captioning models and datasets.

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What’s Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs
Jinhao Pan | Chahat Raj | Ziyu Yao | Ziwei Zhu

Large Language Models (LLMs) often exhibit social biases inherited from their training data. While existing benchmarks evaluate bias by term-based mode through direct term associations between demographic terms and bias terms, LLMs have become increasingly adept at avoiding biased responses, leading to seemingly low levels of bias. However, biases persist in subtler, contextually hidden forms that traditional benchmarks fail to capture. We introduce the Description-based Bias Benchmark (DBB), a novel dataset designed to assess bias at the semantic level that bias concepts are hidden within naturalistic, subtly framed contexts in real-world scenarios rather than superficial terms. We analyze six state-of-the-art LLMs, revealing that while models reduce bias in response at the term level, they continue to reinforce biases in nuanced settings. Data, code, and results are available at https://github.com/JP-25/Description-based-Bias-Benchmark.

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Identifying Rare Languages in Common Crawl Data is a Needles-in-a-Haystack Problem
Rasul Dent | Pedro Ortiz Suarez | Thibault Clérice | Benoît Sagot

Automatic language identification is frequentlyframed as a multi-class classification problem.However, when creating digital corpora forless commonly written languages, it may bemore appropriate to consider it a data min-ing problem. For these varieties, one knowsahead of time that the vast majority of doc-uments are of little interest. By minimizingresources spent on classifying such documents,we can create corpora covering previously over-looked languages faster than existing pipelines.To demonstrate the effectiveness of the tar-geted mining perspective, we introduce a newpipeline that can filter a single snapshot in twohours. We also provide web corpora for severalFrench-based Creoles.

Critique ability, a meta-cognitive capability of humans, presents significant challenges for LLMs to improve. While utilizing human annotation can enhance critique ability effectively, most recent works primarily rely on supervised fine-tuning (SFT) using critiques generated by a single LLM like GPT-4, which is more scalable and cost-effective.However, such model-generated critiques often suffer from inherent flaws due to the complexity of critique. Consequently, fine-tuning LLMs on these flawed critiques not only limits performance but also propagates errors into the learned model.To address this issue, we propose MultiCritique, a unified framework that leverages multi-agent feedback to improve critique ability in both the supervised fine-tuning (SFT) and reinforcement learning (RL) stages.In the SFT stage, MultiCritique aggregates high-quality multi-agent critiques through a fine-grained meta-critique mechanism. In the RL stage, preference critiques are constructed and refined by validating their contributions to revisions, thereby enhancing robustness of RL in improving critique ability.Based on MultiCritique, we construct SFT and RL datasets. Extensive experimental results on two benchmarks highlight the key benefits of our dataset, including superior quality, enhanced data efficiency, strong generalization on unseen tasks, and improvements in the general capability of LLMs.Notably, our fine-tuned 7B model significantly surpasses advanced 7B-13B models, approaching advanced 70B LLMs and GPT-4.Resources have been publicly available.

Reward models are essential for aligning large language models (LLMs) with human preferences. However, most open-source multilingual reward models are primarily trained on preference datasets in high-resource languages, resulting in unreliable reward signals for low-resource Indic languages. Collecting large-scale, high-quality preference data for these languages is prohibitively expensive, making preference-based training approaches impractical. To address this challenge, we propose RELIC, a novel in-context learning framework for reward modeling in low-resource Indic languages. RELIC trains a retriever with a pairwise ranking objective to select in-context examples from auxiliary high-resource languages that most effectively highlight the distinction between preferred and less-preferred responses. Extensive experiments on three preference datasets—PKU-SafeRLHF, WebGPT, and HH-RLHF—using state-of-the-art open-source reward models demonstrate that RELIC significantly improves reward model accuracy for low-resource Indic languages, consistently outperforming existing example selection methods. For example, on Bodo—a low-resource Indic language—using a LLaMA-3.2-3B reward model, RELIC achieves a 12.81% and 10.13% improvement in accuracy over zero-shot prompting and state-of-the-art example selection method, respectively

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Invoke Interfaces Only When Needed: Adaptive Invocation for Large Language Models in Question Answering
Jihao Zhao | Chunlai Zhou | Daixuan Li | Shuaishuai Zu | Biao Qin

The collaborative paradigm of large and small language models (LMs) effectively balances performance and cost, yet its pivotal challenge lies in precisely pinpointing the moment of invocation when hallucinations arise in small LMs. Previous optimization efforts primarily focused on post-processing techniques, which were separate from the reasoning process of LMs, resulting in high computational costs and limited effectiveness. In this paper, we propose a practical invocation evaluation metric called AttenHScore, which calculates the accumulation and propagation of hallucinations during the generation process of small LMs, continuously amplifying potential reasoning errors. By dynamically adjusting the detection threshold, we achieve more accurate real-time invocation of large LMs. Additionally, considering the limited reasoning capacity of small LMs, we leverage uncertainty-aware knowledge reorganization to assist them better capture critical information from different text chunks. Extensive experiments reveal that our AttenHScore outperforms most baselines in enhancing real-time hallucination detection capabilities across multiple QA datasets, especially when addressing complex queries. Moreover, our strategies eliminate the need for additional model training and display flexibility in adapting to various transformer-based LMs. Our code is available at https://github.com/Robot2050/AttenHScore.

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SQLSpace: A Representation Space for Text-to-SQL to Discover and Mitigate Robustness Gaps
Neha Srikanth | Victor Bursztyn | Puneet Mathur | Ani Nenkova

We introduce SQLSpace, a human-interpretable, generalizable, compact representation for text-to-SQL examples derived with minimal human intervention. We demonstrate the utility of these representations in evaluation with three use cases: (i) closely comparing and contrasting the composition of popular NL2SQL benchmarks to identify unique dimensions of examples they evaluate, (ii) understanding model performance at a granular level beyond overall accuracy scores, and (iii) improving model performance through targeted query rewriting based on learned correctness estimation. We show that SQLSpace enables analysis that would be difficult with raw examples alone: it reveals compositional differences between benchmarks, exposes performance patterns obscured by accuracy alone, and supports modeling of query success.

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One More Modality: Does Abstract Meaning Representation Benefit Visual Question Answering?
Abhidip Bhattacharyya | Emma Markle | Shira Wein

Visual Question Answering (VQA) requires a vision-language model to reason over both visual and textual inputs to answer questions about images. In this work, we investigate whether incorporating explicit semantic information, in the form of Abstract Meaning Representation (AMR) graphs, can enhance model performance—particularly in low-resource settings where training data is limited. We augment two vision-language models, LXMERT and BLIP-2, with sentence- and document-level AMRs and evaluate their performance under both full and reduced training data conditions. Our findings show that in well-resourced settings, models (in particular the smaller LXMERT) are negatively impacted by incorporating AMR without specialized training. However, in low-resource settings, AMR proves beneficial: LXMERT achieves up to a 13.1% relative gain using sentence-level AMRs. These results suggest that while addition of AMR can lower the performance in some settings, in a low-resource setting AMR can serve as a useful semantic prior, especially for lower-capacity models trained on limited data.

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DP-GTR: Differentially Private Prompt Protection via Group Text Rewriting
Mingchen Li | Heng Fan | Song Fu | Junhua Ding | Yunhe Feng

Prompt privacy is crucial, especially when using online large language models (LLMs), due to the sensitive information often contained within prompts. While LLMs can enhance prompt privacy through text rewriting, existing methods primarily focus on document-level rewriting, neglecting the rich, multi-granular representations of text. This limitation restricts LLM utilization to specific tasks, overlooking their generalization and in-context learning capabilities, thus hindering practical application. To address this gap, we introduce DP-GTR, a novel three-stage framework that leverages local differential privacy (DP) and the composition theorem via group text rewriting. DP-GTR is the first framework to integrate both document-level and word-level information while exploiting in-context learning to simultaneously improve privacy and utility, effectively bridging local and global DP mechanisms at the individual data point level. Experiments on CommonSense QA and DocVQA demonstrate that DP-GTR outperforms existing approaches, achieving a superior privacy-utility trade-off. Furthermore, our framework is compatible with existing rewriting techniques, serving as a plug-in to enhance privacy protection. Our code is publicly available at anonymous.4open.science for reproducibility.

Legal mathematical reasoning is essential for applying large language models (LLMs) in high-stakes legal contexts, where outputs must be both mathematically accurate and procedurally compliant. However, existing legal LLMs lack structured numerical reasoning, and open-domain models, though capable of calculations, often overlook mandatory legal steps. To address this, we present LexNum, the first Chinese legal mathematical reasoning benchmark, covering three representative scenarios where each instance reflects legally grounded procedural flows. We further propose LexPam, a two-stage reinforcement learning framework for efficient legal reasoning training. Leveraging curriculum learning, we use a stronger teacher model to partition data into basic and challenging subsets. A lightweight 1.5B student model is then fine-tuned with Group Relative Policy Optimization, which avoids costly value networks and enables stable training from sparse, end-of-sequence rewards. The first stage improves accuracy and format; the second introduces a novel reward to guide procedural alignment via task-specific legal elements. Experiments show that existing models perform poorly on LexNum, while LexPam enhances both mathematical accuracy and legal coherence, and generalizes effectively across tasks and domains.

Recent progress in large language models (LLMs) has enabled substantial advances in solving mathematical problems. However, existing benchmarks often fail to reflect real-world complexity, which demand open-ended, interdisciplinary reasoning and integration of computational tools. To address this gap, we introduce **ModelingBench**, a novel benchmark featuring real-world-inspired, open-ended problems from math modeling competitions across diverse domains, ranging from urban traffic optimization to ecosystem resource planning. These tasks require translating natural language into formal mathematical formulations, applying appropriate tools, and producing structured, defensible reports. ModelingBench supports multiple valid solutions, capturing the ambiguity and creativity of practical modeling. To solve these challenges, we present **ModelingAgent**, a multi-agent framework that coordinates tool use, supports structured workflows, and enables iterative self-refinement to generate well-grounded, creative solutions. Empirical results show that ModelingAgent substantially outperforms strong baselines and often produces solutions indistinguishable from those of human experts. Together, our work provides a comprehensive framework for evaluating and advancing real-world problem-solving in open-ended, interdisciplinary modeling challenges. All the codes are released for future research.

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Beyond Coarse Labels: Fine-Grained Problem Augmentation and Multi-Dimensional Feedback for Emotional Support Conversation
Yuanchen Shi | Jiawang Hao | Fang Kong

Emotional support conversation systems aim to help users alleviate distress through empathetic dialogue. However, existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges. To address this, we propose a generalizable fine-grained problem enhancement method that systematically augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora. As a demonstration, we construct EmoCare, a large-scale ESC dataset with 2.6K dialogues and 42.8K utterances, expanding problem type coverage from 13 to 45 fine-grained categories. Building on this data augmentation process, we introduce FPEMF, a flexible framework for empathetic dialogue generation, which comprises two modules: fine-grained problem enhancement and multi-dimensional feedback, which can be seamlessly integrated with various backbone models. The multi-dimensional feedback module evaluates responses from four perspectives: emotional understanding, strategy effectiveness, contextual consistency, and topic relevance, guiding models to generate more supportive replies. Experiments show that FPEMF consistently improves both automatic and human evaluation metrics across different models.

Financial decision-making presents unique challenges for language models, requiring them to handle temporally evolving, risk-sensitive, and event-driven contexts. While large language models (LLMs) demonstrate strong general reasoning abilities, they often overlook key behavioral patterns underlying human financial behavior—such as expert reliance under information asymmetry, loss-averse risk adjustment, and temporal adaptation. We propose FinHEAR, a multi-agent framework for Human Expertise and Adaptive Risk-aware reasoning. FinHEAR coordinates multiple LLM-based agents to capture historical trends, interpret current events, and incorporate expert knowledge within a unified, event-aware pipeline. Grounded in behavioral economics, FinHEAR features mechanisms for expert-guided retrieval to reduce information asymmetry, dynamic position sizing to reflect loss aversion, and feedback-driven refinement to enhance temporal consistency. Experiments on a curated real-world financial dataset show that FinHEAR consistently outperforms strong baselines in both trend forecasting and decision-making.

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EvolKV: Evolutionary KV Cache Compression for LLM Inference
Bohan Yu | Yekun Chai

Existing key-value (KV) cache compression methods typically rely on heuristics, such as uniform cache allocation across layers or static eviction policies, however, they ignore the critical interplays among layer-specific feature patterns and task performance, which can lead to degraded generalization. In this paper, we propose EvolKV, an adaptive framework for layer-wise, task-driven KV cache compression that jointly optimizes the memory efficiency and task performance. By reformulating cache allocation as a multi-objective optimization problem, EvolKV leverages evolutionary search to dynamically configure layer budgets while directly maximizing downstream performance. Extensive experiments on 11 tasks demonstrate that our approach outperforms all baseline methods across a wide range of KV cache budgets on long-context tasks and surpasses heuristic baselines by up to 7 percentage points on GSM8K. Notably, EvolKV achieves superior performance over the full KV cache setting on code completion while utilizing only 1.5% of the original budget, suggesting the untapped potential in learned compression strategies for KV cache budget allocation.

Large Language Models (LLMs) have transformed natural language processing, yet their internal mechanisms remain largely opaque. Recently, mechanistic interpretability has attracted significant attention from the research community as a means to understand the inner workings of LLMs. Among various mechanistic interpretability approaches, Sparse Autoencoders (SAEs) have emerged as a promising method due to their ability to disentangle the complex, superimposed features within LLMs into more interpretable components. This paper presents a comprehensive survey of SAEs for interpreting and understanding the internal workings of LLMs. Our major contributions include: (1) exploring the technical framework of SAEs, covering basic architecture, design improvements, and effective training strategies; (2) examining different approaches to explaining SAE features, categorized into input-based and output-based explanation methods; (3) discussing evaluation methods for assessing SAE performance, covering both structural and functional metrics; and (4) investigating real-world applications of SAEs in understanding and manipulating LLM behaviors.

Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in processing both visual and textual information. However, the critical challenge of alignment between visual and textual representations is not fully understood. This survey presents a comprehensive examination of alignment and misalignment in LVLMs through an explainability lens. We first examine the fundamentals of alignment, exploring its representational and behavioral aspects, training methodologies, and theoretical foundations. We then analyze misalignment phenomena across three semantic levels: object, attribute, and relational misalignment. Our investigation reveals that misalignment emerges from challenges at multiple levels: the data level, the model level, and the inference level. We provide a comprehensive review of existing mitigation strategies, categorizing them into parameter-frozen and parameter-tuning approaches. Finally, we outline promising future research directions, emphasizing the need for standardized evaluation protocols and in-depth explainability studies.

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Attention Consistency for LLMs Explanation
Tian Lan | Jinyuan Xu | Xue He | Jenq-Neng Hwang | Lei Li

Understanding the decision-making processes of large language models (LLMs) is essential for their trustworthy development and deployment, however, current interpretability methods often face challenges such as low resolution and high computational cost. To address these limitations, we propose the Multi-Layer Attention Consistency Score (MACS), a novel, lightweight, and easily deployable heuristic for estimating the importance of input tokens in decoder-based models. MACS measures contributions of input tokens based on the consistency of maximal attention. Empirical evaluations demonstrate that MACS achieves a favorable trade-off between interpretability quality and computational efficiency, showing faithfulness comparable to complex techniques with a 22% decrease in VRAM usage and 30% reduction in latency.

With the development of Large Language Models (LLMs), numerous efforts have revealed their vulnerabilities to jailbreak attacks. Although these studies have driven the progress in LLMs’ safety alignment, it remains unclear whether LLMs have internalized authentic knowledge to deal with real-world crimes, or are merely forced to simulate toxic language patterns. This ambiguity raises concerns that jailbreak success is often attributable to a hallucination loop between jailbroken LLM and judger LLM. By decoupling the use of jailbreak techniques, we construct knowledge-intensive Q&A to investigate the misuse threats of LLMs in terms of dangerous knowledge possession, harmful task planning utility, and harmfulness judgment robustness. Experiments reveal a mismatch between jailbreak success rates and harmful knowledge possession in LLMs, and existing LLM-as-a-judge frameworks tend to anchor harmfulness judgments on toxic language patterns. Our study reveals a gap between existing LLM safety assessments and real-world threat potential.

Multilingual Large Language Models (MLLMs) demonstrate strong generalization across languages, yet they remain prone to hallucinations, especially in low-resource languages, due to training data imbalances. These hallucinations, which include inaccurate or fabricated outputs, are particularly problematic in domain-specific generation tasks (Chataigner et al., 2024). To address this challenge, we propose CCL-XCoT (Curriculum-based Contrastive Learning-based Cross-lingual Chain-of-Thought), a two-stage fine-tuning framework for mitigating hallucination in MLLMs. Our approach first enhances cross-lingual semantic alignment through curriculum-based contrastive learning combined with next-token prediction during continued pre-training. Building on this foundation, we then introduce a cross-lingual Chain-of-Thought (XCoT) prompting strategy during instruction fine-tuning, which guides the model to reason in a high-resource language before generating answers in the target low-resource language. Experimental results show that CCL-XCoT reduces hallucination rates by up to 62% and substantially improves factual knowledge transfer across language pairs, without relying on external retrieval or multi-model ensembles.

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Evaluating Step-by-step Reasoning Traces: A Survey
Jinu Lee | Julia Hockenmaier

Step-by-step reasoning is widely used to enhance the reasoning ability of large language models (LLMs) in complex problems. Evaluating the quality of reasoning traces is crucial for understanding and improving LLM reasoning. However, existing evaluation practices are highly inconsistent, resulting in fragmented progress across evaluator design and benchmark development. To address this gap, this survey provides a comprehensive overview of step-by-step reasoning evaluation, proposing a taxonomy of evaluation criteria with four top-level categories (factuality, validity, coherence, and utility). Based on the taxonomy, we review different datasets, evaluator implementations, and recent findings, leading to promising directions for future research.

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Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning
Kepu Zhang | Haoyue Yang | Xu Tang | Weijie Yu | Jun Xu

In legal practice, judges apply the trichotomous dogmatics of criminal law, sequentially assessingthe elements of the offense, unlawfulness, and culpability to determine whether an individual’s conduct constitutes a crime. Although current legal large language models (LLMs) show promising accuracy in judgment prediction, they lack trichotomous reasoning capabilities due to the absence of an appropriate benchmark dataset, preventing them from predicting innocent outcomes. As a result, every input is automatically assigned a charge, limiting their practical utility in legal contexts. To bridge this gap, we introduce LJPIV, the first benchmark dataset for Legal Judgment Prediction with Innocent Verdicts. Adhering to the trichotomous dogmatics, we extend three widely-used legal datasets through LLM-based augmentation and manual verification. Our experiments with state-of-the-art legal LLMs and novel strategies that integrate trichotomous reasoning into zero-shot prompting and fine-tuning reveal: (1) current legal LLMs have significant room for improvement, with even the best models achieving an F1 score of less than 0.3 on LJPIV; and (2) our strategies notably enhance both in-domain and cross-domain judgment prediction accuracy, especially for cases resulting in an innocent verdict.

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Not Every Token Needs Forgetting: Selective Unlearning Balancing Forgetting and Utility in Large Language Models
Yixin Wan | Anil Ramakrishna | Kai-Wei Chang | Volkan Cevher | Rahul Gupta

Large Language Model (LLM) unlearning has recently gained significant attention, driven by the need to remove unwanted information—such as private, sensitive, or copyrighted content—from trained models. However, conventional unlearning approaches indiscriminately update model parameters to forget all tokens in a target document, including common tokens (e.g., pronouns, prepositions, general nouns) that carry general knowledge. In this paper, we highlight that “not every token needs forgetting”. We propose **Selective Unlearning (SU)**, which identifies a critical subset of tokens within the forgetting set that is relevant to the unwanted information, and unlearns only those tokens. Experiments on two benchmarks and six baseline unlearning algorithms demonstrate that SU not only achieves effective unlearning on the targeted forget data, but also significantly preserves the model’s utility in the retaining set.

Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. To bridge this gap, we introduce DisastIR, the first comprehensive IR evaluation benchmark specifically tailored for disaster management. DisastIR comprises 9,600 diverse user queries and more than 1.3 million labeled query-passage pairs, covering 48 distinct retrieval tasks derived from six search intents and eight general disaster categories that include 301 specific event types. Our evaluations of 30 state-of-the-art retrieval models demonstrate significant performance variances across tasks, with no single model excelling universally. Furthermore, comparative analyses reveal significant performance gaps between general-domain and disaster management-specific tasks, highlighting the necessity of disaster management-specific benchmarks for guiding IR model selection to support effective decision-making in disaster management scenarios. All source codes and DisastIR are available at https://github.com/KaiYin97/Disaster_IR.

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Data or Language Supervision: What Makes CLIP Better than DINO?
Yiming Liu | Yuhui Zhang | Dhruba Ghosh | Ludwig Schmidt | Serena Yeung-Levy

CLIP outperforms self-supervised models like DINO as vision encoders for vision-language models (VLMs), but it remains unclear whether this advantage stems from CLIP’s language supervision or its much larger training data. To disentangle these factors, we pre-train CLIP and DINO under controlled settings—using the same architecture, dataset, and training configuration—achieving similar ImageNet accuracy. Embedding analysis shows that CLIP captures high-level semantics (e.g., object categories, text), while DINO is more responsive to low-level features like colors and styles. When integrated into VLMs and evaluated on 20 VQA benchmarks, CLIP excels at text-intensive tasks, while DINO slightly outperforms on vision-centric ones. Variants of language supervision (e.g., sigmoid loss, pre-trained language encoders) yield limited gains. Our findings provide scientific insights into vision encoder design and its impact on VLM performance.

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Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
Chenye Zou | Xingyue Wen | Tianyi Hu | Qian Janice Wang | Daniel Hershcovich

Recent advances in large language models (LLMs) have opened the door to culture-aware language tasks. We introduce the novel problem of adapting wine reviews across Chinese and English, which goes beyond literal translation by incorporating regional taste preferences and culture-specific flavor descriptors. In a case study on cross-cultural wine review adaptation, we compile the first parallel corpus of professional reviews, containing 8k Chinese and 16k Anglophone reviews. We benchmark both neural-machine-translation baselines and state-of-the-art LLMs with automatic metrics and human evaluation. For the latter, we propose three culture-oriented criteria—Cultural Proximity, Cultural Neutrality, and Cultural Genuineness—to assess how naturally a translated review resonates with target-culture readers. Our analysis shows that current models struggle to capture cultural nuances, especially in translating wine descriptions across different cultures. This highlights the challenges and limitations of translation models in handling cultural content.

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DeFT-X: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer
Sona Elza Simon | Preethi Jyothi

Effective cross-lingual transfer remains a critical challenge in scaling the benefits of large language models from high-resource to low-resource languages. Towards this goal, prior studies have explored many approaches to combine task knowledge from task-specific data in a (high-resource) source language and language knowledge from unlabeled text in a (low-resource) target language. One notable approach proposed composable sparse fine-tuning (SFT) for cross-lingual transfer that learns task-specific and language-specific sparse masks to select a subset of the pretrained model’s parameters that are further fine-tuned. These sparse fine-tuned vectors (SFTs) are subsequently composed with the pretrained model to facilitate zero-shot cross-lingual transfer to a task in a target language, using only task-specific data from a source language. These sparse masks for SFTs were identified using a simple magnitude-based pruning. In our work, we introduce DeFT-X, a novel composable SFT approach that denoises the weight matrices of a pretrained model before magnitude pruning using singular value decomposition, thus yielding more robust SFTs. We evaluate DeFT-X on a diverse set of extremely low-resource languages for sentiment classification (NusaX) and natural language inference (AmericasNLI) and demonstrate that it performs at par or outperforms SFT and other prominent cross-lingual transfer baselines.

Large language models (LLMs) demonstrate remarkable text generation and syntax parsing capabilities in high-resource languages. However, their performance notably declines in low-resource languages due to memory forgetting stemming from semantic interference across languages. To address this issue, we propose a novel deep hierarchical syntax understanding approach to improve the cross-lingual semantic memory capability of LLMs. First, we design a multi-task joint fine-tuning strategy to implicitly align linguistic knowledge between source and target languages in LLMs, which is leveraged to initially parse the target text. Second, we automatically construct the multilingual dependency label banks based on the statistical structure information from the Universal Dependencies (UD) data. Third, we obtain each label’s memory strength via in-depth analysis of the initial parsing tree and its dependency label bank. Finally, memory strength is further exploited to guide LLMs to learn the linguistic commonalities from multilingual dependency label banks, thus activating the memory ability of weak labels. Experimental results on four benchmark datasets show that our method can dramatically improve the parsing accuracy of all baseline models, leading to new state-of-the-art results. Further analysis reveals that our approach can effectively enhance the weak syntactic label memory cognition of LLMs by combining the advantages of both implicit multi-task fine-tuning and explicit label bank guiding. Our code and dependency label banks are released at https://github.com/Flamelunar/memory_dep.

High-quality data resources play a crucial role in learning large language models (LLMs), particularly for low-resource languages like Cantonese. Despite having more than 85 million native speakers, Cantonese is still considered a low-resource language in the field of natural language processing (NLP) due to factors such as the dominance of Mandarin, lack of cohesion within the Cantonese-speaking community, diversity in character encoding and input methods, and the tendency of overseas Cantonese speakers to prefer using English. In addition, rich colloquial vocabulary of Cantonese, English loanwords, and code-switching characteristics add to the complexity of corpus collection and processing. To address these challenges, we collect Cantonese texts from a variety of sources, including open source corpora, Hong Kong-specific forums, Wikipedia, and Common Crawl data. We conduct rigorous data processing through language filtering, quality filtering, content filtering, and de-duplication steps, successfully constructing a high-quality Cantonese corpus of over 2 billion tokens for training large language models. We further refined the model through supervised fine-tuning (SFT) on curated Cantonese tasks, enhancing its ability to handle specific applications. Upon completion of the training, the model achieves state-of-the-art (SOTA) performance on four Cantonese benchmarks. After training on our dataset, the model also exhibits improved performance on other mainstream language tasks.

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A Structured Framework for Evaluating and Enhancing Interpretive Capabilities of Multimodal LLMs in Culturally Situated Tasks
Haorui Yu | Ramon Ruiz-Dolz | Qiufeng Yi

This study aims to test and evaluate the capabilities and characteristics of current mainstream Visual Language Models (VLMs) in generating critiques for traditional Chinese painting. To achieve this, we first developed a quantitative framework for Chinese painting critique. This framework was constructed by extracting multi-dimensional evaluative features covering evaluative stance, feature focus, and commentary quality from human expert critiques using a zero-shot classification model. Based on these features, several representative critic personas were defined and quantified. This framework was then employed to evaluate selected VLMs such as Llama, Qwen, or Gemini. The experimental design involved persona-guided prompting to assess the VLM’s ability to generate critiques from diverse perspectives. Our findings reveal the current performance levels, strengths, and areas for improvement of VLMs in the domain of art critique, offering insights into their potential and limitations in complex semantic understanding and content generation tasks.

The Multimodal Large Language Models (MLLMs) are continually pre-trained on a mixture of image-text caption data and interleaved document data, while the high-quality data filtering towards image-text interleaved document data is under-explored. We propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to Filter both high-quality image-text caption and interleaved data (UniFilter). To address the challenge of collecting diverse labeled multimodal data, we introduce a semi-synthetic approach that leverages readily available raw images and generates corresponding text across four quality levels. This method enables efficient creation of sample-score pairs for both caption and interleaved document data to train UniFilter. We apply UniFilter to curate high-quality caption data from DataComp caption dataset and interleaved data from the OBELICS image-text interleaved dataset. MLLMs pre-trained on the filtered data demonstrate significantly enhanced capabilities compared to those trained on baseline-filtered data, achieving stronger zero-shot reasoning and in-context learning capabilities. After visual supervised fine-tuning, these UniFilter-induced MLLMs achieve stronger performance on various benchmarks, highlighting the downstream benefits of high-quality multimodal pre-training.

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Self-Improvement in Multimodal Large Language Models: A Survey
Shijian Deng | Kai Wang | Tianyu Yang | Harsh Singh | Yapeng Tian

Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview of the current literature and discuss methods from three perspectives: 1) data collection, 2) data organization, and 3) model optimization, to facilitate the further development of self-improvement in MLLMs. We also include commonly used evaluations and downstream applications. Finally, we conclude by outlining open challenges and future research directions.

This paper proposes Complete Textual Concept Bottleneck Model (CT-CBM), a novel TCBM generator building concept labels in a fully unsupervised manner using a small language model, eliminating both the need for predefined human labeled concepts and LLM annotations. CT-CBM iteratively targets and adds important and identifiable concepts in the bottleneck layer to create a complete concept basis. CT-CBM achieves striking results against competitors in terms of concept basis completeness and concept detection accuracy, offering a promising solution to reliably enhance interpretability of NLP classifiers.

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EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian
Daryna Dementieva | Nikolay Babakov | Alexander Fraser

While Ukrainian NLP has seen progress in many texts processing tasks, emotion classification remains an underexplored area with no publicly available benchmark to date. In this work, we introduce **EmoBench-UA**, the first annotated dataset for emotion detection in Ukrainian texts. Our annotation schema is adapted from the previous English-centric works on emotion detection (Mohammad et al., 2018; Mohammad, 2022) guidelines. The dataset was created through crowdsourcing using the Toloka.ai platform ensuring high-quality of the annotation process. Then, we evaluate a range of approaches on the collected dataset, starting from linguistic-based baselines, synthetic data translated from English, to large language models (LLMs). Our findings highlight the challenges of emotion classification in non-mainstream languages like Ukrainian and emphasize the need for further development of Ukrainian-specific models and training resources.

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Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking
Yunyi Zhang | Ruozhen Yang | Siqi Jiao | SeongKu Kang | Jiawei Han

Scientific paper retrieval is essential for supporting literature discovery and research. While dense retrieval methods demonstrate effectiveness in general-purpose tasks, they often fail to capture fine-grained scientific concepts that are essential for accurate understanding of scientific queries. Recent studies also use large language models (LLMs) for query understanding; however, these methods often lack grounding in corpus-specific knowledge and may generate unreliable or unfaithful content. To overcome these limitations, we propose SemRank, an effective and efficient paper retrieval framework that combines LLM-guided query understanding with a concept-based semantic index. Each paper is indexed using multi-granular scientific concepts, including general research topics and detailed key phrases. At query time, an LLM identifies core concepts derived from the corpus to explicitly capture the query’s information need. These identified concepts enable precise semantic matching, significantly enhancing retrieval accuracy. Experiments show that SemRank consistently improves the performance of various base retrievers, surpasses strong existing LLM-based baselines, and remains highly efficient.

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DLIR: Spherical Adaptation for Cross-Lingual Knowledge Transfer of Sociological Concepts Alignment
Zeqiang Wang | Jon Johnson | Suparna De

Cross-lingual alignment of nuanced sociological concepts is crucial for comparative cross-cultural research, harmonising longitudinal studies, and leveraging knowledge from social science taxonomies (e.g., ELSST). However, aligning these concepts is challenging due to cultural context-dependency, linguistic variation, and data scarcity, particularly for low-resource languages. Existing methods often fail to capture domain-specific subtleties or require extensive parallel data. Grounded in a Vector Decomposition Hypothesis—positing separable domain and language components within embeddings, supported by observed language-pair specific geometric structures—we propose DLIR (Dual-Branch LoRA for Invariant Representation). DLIR employs parallel Low-Rank Adaptation (LoRA) branches: one captures core sociological semantics (trained primarily on English data structured by the ELSST hierarchy), while the other learns language invariance by counteracting specific language perturbations. These perturbations are modeled by Gaussian Mixture Models (GMMs) fitted on minimal parallel concept data using spherical geometry. DLIR significantly outperforms strong baselines on cross-lingual sociological concept retrieval across 10 languages. Demonstrating powerful zero-shot knowledge transfer, English-trained DLIR substantially surpasses target-language (French/German) LoRA fine-tuning even in monolingual tasks. DLIR learns disentangled, language-robust representations, advancing resource-efficient multilingual understanding and enabling reliable cross-lingual comparison of sociological constructs.

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Test-Time Steering for Lossless Text Compression via Weighted Product of Experts
Qihang Zhang | Muchen Li | Ziao Wang | Renjie Liao | Lele Wang

Lossless compression techniques are crucial in an era of rapidly growing data. Traditional universal compressors like gzip offer low computational overhead, high speed, and broad applicability across data distributions. However, they often lead to worse compression rates than modern neural compressors, which leverage large-scale training data to model data distributions more effectively.Despite their advantages, neural compressors struggle to generalize to unseen data. To address this limitation, we propose a novel framework that performs Test-Time Steering via a Weighted Product of Experts (wPoE).At inference, our method adaptively combines a universal compression model with a pretrained neural language model, ensuring the compression rate is at least as good as the best individual model.Extensive experiments demonstrate that our approach improves the performance of text compression without requiring fine-tuning. Furthermore, it seamlessly integrates with any autoregressive language model, providing a practical solution for enhancing text compression across diverse data distributions.

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Zero-Shot Contextual Embeddings via Offline Synthetic Corpus Generation
Philip Lippmann | Jie Yang

Context-aware embedding methods boost retrieval accuracy by conditioning on corpus statistics (e.g., term co-occurrence and topical patterns) extracted from neighboring documents. However, this context-aware approach requires access to the target corpus or requires domain-specific finetuning, posing practical barriers in privacy-sensitive or resource-constrained settings. We present ZEST, a zero-shot contextual adaptation framework that replaces real corpus access with a one-time offline synthesis of a compact proxy. Given only a handful of exemplar documents representative of the general target domain, we use a multi-step hierarchical procedure to generate a synthetic context corpus of several hundred documents that aims to emulate key domain-specific distributions. At inference, the frozen context-aware encoder uses this proxy corpus – without any finetuning or target corpus access – to produce domain-adapted embeddings. Across the MTEB benchmark, ZEST’s zero-shot synthetic context adaptation using only five example documents performs within 0.5% of models leveraging full target corpus access – demonstrating remarkable efficacy without any retraining. ZEST thus provides a practical method for deploying high-performance, adaptable embeddings in constrained environments.

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The Hallucination Tax of Reinforcement Finetuning
Linxin Song | Taiwei Shi | Jieyu Zhao

Reinforcement finetuning (RFT) has become a standard approach for enhancing the reasoning capabilities of large language models (LLMs). However, its impact on model trustworthiness remains underexplored. In this work, we identify and systematically study a critical side effect of RFT, which we term the hallucination tax: a degradation in refusal behavior causing models to produce hallucinated answers to unanswerable questions confidently. To investigate this, we introduce SUM (Synthetic Unanswerable Math), a high-quality dataset of unanswerable math problems designed to probe models’ ability to recognize an unanswerable question by reasoning from the insufficient or ambiguous information. Our results show that standard RFT training could reduce model refusal rates by more than 80%, which significantly increases model’s tendency to hallucinate. We further demonstrate that incorporating just 10% SUM during RFT substantially restores appropriate refusal behavior, with minimal accuracy trade-offs on solvable tasks. Crucially, this approach enables LLMs to leverage inference-time compute to reason about their own uncertainty and knowledge boundaries, improving generalization not only to out-of-domain math problems but also to factual question answering tasks.

Large Language Models (LLMs) are capable of recalling multilingual factual knowledge present in their pretraining data. However, most studies evaluate only the final model, leaving the development of factual recall and crosslingual consistency throughout pretraining largely unexplored. In this work, we trace how factual recall and crosslingual consistency evolve during pretraining, focusing on OLMo-7B as a case study. We find that both accuracy and consistency improve over time for most languages. We show that this improvement is primarily driven by the fact frequency in the pretraining corpus: more frequent facts are more likely to be recalled correctly, regardless of language. Yet, some low-frequency facts in non-English languages can still be correctly recalled. Our analysis reveals that these instances largely benefit from crosslingual transfer of their English counterparts – an effect that emerges predominantly in the early stages of pretraining. We pinpoint two distinct pathways through which multilingual factual knowledge acquisition occurs: (1) frequency-driven learning, which is dominant and language-agnostic, and (2) crosslingual transfer, which is limited in scale and typically constrained to relation types involving named entities. We release our code and data to facilitate further research at https://github.com/cisnlp/multilingual-fact-tracing.

Intent detection, a core component of natural language understanding, has considerably evolved as a crucial mechanism in safeguarding large language models (LLMs). While prior work has applied intent detection to enhance LLMs’ moderation guardrails, showing a significant success against content-level jailbreaks, the robustness of these intent-aware guardrails under malicious manipulations remains under-explored. In this work, we investigate the vulnerability of intent-aware guardrails and demonstrate that LLMs exhibit implicit intent detection capabilities. We propose a two-stage intent-based prompt-refinement framework, IntentPrompt, that first transforms harmful inquiries into structured outlines and further reframes them into declarative-style narratives by iteratively optimizing prompts via feedback loops to enhance jailbreak success for red-teaming purposes. Extensive experiments across four public benchmarks and various black-box LLMs indicate that our framework consistently outperforms several cutting-edge jailbreak methods and evades even advanced Intent Analysis (IA) and Chain-of-Thought (CoT)-based defenses. Specifically, our “FSTR+SPIN” variant achieves attack success rates ranging from 88.25% to 96.54% against CoT-based defenses on the o1 model, and from 86.75% to 97.12% on the GPT-4o model under IA-based defenses. These findings highlight a critical weakness in LLMs’ safety mechanisms and suggest that intent manipulation poses a growing challenge to content moderation guardrails.

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Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples
Andrianos Michail | Simon Clematide | Rico Sennrich

The evaluation of cross-lingual semantic search models is often limited to existing datasets from tasks such as information retrieval and semantic textual similarity. We introduce Cross-Lingual Semantic Discrimination (CLSD), a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. CLSD measures an embedding model’s ability to rank the true parallel sentence above semantically misleading but lexically similar alternatives. As a case study, we construct CLSD datasets for German–French in the news domain. Our experiments show that models fine-tuned for retrieval tasks benefit from pivoting through English, whereas bitext mining models perform best in direct cross-lingual settings. A fine-grained similarity analysis further reveals that embedding models differ in their sensitivity to linguistic perturbations.

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EmoGist: Efficient In-Context Learning for Visual Emotion Understanding
Ronald Seoh | Dan Goldwasser

In this paper, we introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. The key intuition of our approach is that context-dependent definition of emotion labels could allow more accurate predictions of emotions, as the ways in which emotions manifest within images are highly context dependent and nuanced. EmoGist pre-generates multiple descriptions of emotion labels, by analyzing the clusters of example images belonging to each label. At test time, we retrieve a version of description based on the cosine similarity of test image to cluster centroids, and feed it together with the test image to a fast LVLM for classification. Through our experiments, we show that EmoGist allows up to 12 points improvement in micro F1 scores with the multi-label Memotion dataset, and up to 8 points in macro F1 in the multi-class FI dataset.

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Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
Haokun Chen | Sebastian Szyller | Weilin Xu | Nageen Himayat

Large language models (LLMs) are trained using massive datasets.However, these datasets often contain undesirable content, e.g., harmful texts, personal information, and copyrighted material.To address this, machine unlearning aims to remove information from trained models.Recent work has shown that soft token attacks () can successfully extract unlearned information from LLMs.In this work, we show that s can be an inadequate tool for auditing unlearning.Using common unlearning benchmarks, i.e., Who Is Harry Potter? and TOFU, we demonstrate that, in a strong auditor setting, such attacks can elicit any information from the LLM, regardless of (1) the deployed unlearning algorithm, and (2) whether the queried content was originally present in the training corpus.Also, we show that with just a few soft tokens (1-10) can elicit random strings over 400-characters long.Thus showing that s must be used carefully to effectively audit unlearning.Example code can be found at https://github.com/IntelLabs/LLMart/tree/main/examples/unlearning

Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating strong capabilities in tasks such as text generation, summarization, and reasoning. Recently, their potential for automating precise text editing tasks across specialized domains, such as programming code, LaTeX, and structured database languages, has gained attention. However, current state-of-the-art LLMs still struggle with executing precise, instruction-driven edits, particularly when structural accuracy and strict adherence to domain conventions are required.To address these challenges, we introduce InstrEditBench, an automated benchmark dataset comprising over 30,000 structured editing tasks spanning diverse domains, including Wikipedia articles, LaTeX documents, source code, and database languages. Using this benchmark, we develop FineEdit, a specialized editing model explicitly trained for accurate, context-aware text modifications. Experimental evaluations demonstrate that FineEdit outperforms state-of-the-art models, achieving improvements of approximately 10% over Gemini models on single-turn edits, up to 30% over Llama-3.2-3B, and exceeding Mistral-7B-OpenOrca performance by over 40% on direct editing tasks. FineEdit also effectively generalizes to realistic multi-turn editing scenarios, highlighting its practical applicability. To facilitate further research and reproducibility, we release FineEdit at https://github.com/StuRinDQB/FineEdit and https://huggingface.co/datasets/YimingZeng/FineEdit_bench.

Large language models (LLMs) have demonstrated impressive zero-shot capabilities in conversational recommender systems (CRS). However, effectively utilizing historical conversations remains a significant challenge. Current approaches either retrieve few-shot examples or extract global rules to enhance the prompt, which fail to capture the implicit and preference-oriented knowledge. To address this challenge, we propose LLM-based Conversational Recommendation Agents with Collaborative Verbalized Experience, abbreviated as CRAVE. CRAVE begins by sampling trajectories of LLM-based CRS agents on historical queries and establishing verbalized experience banks by reflecting the agents’ actions on user feedback. Additionally, we introduce a collaborative retriever network fine-tuned with item content-parameterized multinomial likelihood on query-item pairs to retrieve preference-oriented verbal experiences for new queries. Furthermore, we developed a debater-critic agent (DCA) system where each agent maintains an independent collaborative experience bank and works together to enhance the CRS recommendations. We demonstrate that the open-ended debate and critique nature of DCA benefits significantly from the collaborative experience augmentation with CRAVE. The code is available at https://github.com/yaochenzhu/CRAVE.

The auto-regressive decoding of Large Language Models (LLMs) results in significant overheads in their hardware performance. While recent research has explored various speculative decoding techniques for multi-token generation, these methods introduce high memory costs from the additional weights and KV cache of separate draft models, limiting efficiency in edge and long-context scenarios. To overcome these limitations in edge-scale LLMs, we propose a novel parallel prompt decoding that requires only runtime memory overhead by employing a unified single model for both speculation and verification. Inspired by the human natural language generation process, PPD approximates outputs generated at future timesteps in parallel by using multiple prompt tokens. Furthermore, we present a hardware-aware two-stage tree pruning algorithm that adaptively optimizes this decoding scheme to fully leverage the computational capacities on different GPUs. Through extensive experiments across LLMs ranging from MobileLlama to Vicuna-13B on a wide range of benchmarks, our approach demonstrates up to 2.49 times speedup. Moreover, our parallel prompt decoding can serve as an orthogonal optimization for synergistic integration with existing speculative decoding, showing up to 1.22 times further speed improvement. To support future development, we have included our code implementation with this submission.

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Measuring Sycophancy of Language Models in Multi-turn Dialogues
Jiseung Hong | Grace Byun | Seungone Kim | Kai Shu

Large Language Models (LLMs) are expected to provide helpful and harmless responses, yet they often exhibit sycophancy—conforming to user beliefs regardless of factual accuracy or ethical soundness. Prior research on sycophancy has primarily focused on single-turn factual correctness, overlooking the dynamics of real-world interactions. In this work, we introduce SYCON Bench (SYcophantic CONformity benchmark), a novel evaluation suite that assesses sycophantic behavior in multi-turn, free-form conversational settings. Our benchmark measures how quickly a model conforms to the user (Turn of Flip) and how frequently it shifts its stance under sustained user pressure (Number of Flip). Applying SYCON Bench to 17 LLMs across three real-world scenarios, we find that sycophancy remains a prevalent failure mode. Our analysis shows that alignment tuning amplifies sycophantic behavior, whereas model scaling and reasoning optimization strengthen the model’s ability to resist undesirable user views. Reasoning models generally outperform instruction-tuned models but often fail when they over-index on logical exposition instead of directly addressing the user’s underlying beliefs. Finally, we evaluate four additional prompting strategies and demonstrate that adopting a third-person perspective reduces sycophancy by up to 63.8% in debate scenario.

Conceptualization, a fundamental element of human cognition, plays a pivotal role in human generalizable reasoning.Generally speaking, it refers to the process of sequentially abstracting specific instances into higher-level concepts and then forming abstract knowledge that can be applied in unfamiliar or novel situations. This enhances models’ inferential capabilities and supports the effective transfer of knowledge across various domains.Despite its significance, the broad nature of this term has led to inconsistencies in understanding conceptualization across various works, as there exists different types of instances that can be abstracted in a wide variety of ways.There is also a lack of a systematic overview that comprehensively examines existing works on the definition, execution, and application of conceptualization to enhance reasoning tasks.In this paper, we address these gaps by first proposing a categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized, in order to clarify the term and define the scope of our work.Then, we present the first comprehensive survey of over 150 papers, surveying various definitions, resources, methods, and downstream applications related to conceptualization into a unified taxonomy, with a focus on the entity and event levels.Furthermore, we shed light on potential future directions in this field and hope to garner more attention from the community.

Recent MLLMs have demonstrated strong visual understanding and reasoning after large-scale multimodal pre-training. However, instruction-tuning is typically text-driven with limited visual supervision, leading to significant visual forgetting and degradation of pre-trained visual knowledge. Existing fine-tuning and continual learning methods compress visual representations and emphasize task alignment over visual retention, failing to address this challenge. We present a novel perspective using effective rank to quantify the loss of visual representation richness, framing visual forgetting as excessive compression under the information bottleneck principle. To address this, we propose modality-decoupled gradient descent (MDGD), which regulates gradient updates to preserve the effective rank of visual features and explicitly disentangles visual learning from task-specific alignment. We further introduce a memory-efficient fine-tuning variant using gradient masking for parameter-efficient adaptation. Extensive experiments show that MDGD effectively mitigates visual forgetting across downstream tasks and models, maintaining pre-trained visual knowledge while supporting strong task adaptation.

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PathoHR: Hierarchical Reasoning for Vision-Language Models in Pathology
Yating Huang | Ziyan Huang | Lintao Xiang | Qijun Yang | Hujun Yin

Accurate analysis of pathological images is essential for automated tumor diagnosis but remains challenging due to high structural similarity and subtle morphological variations in tissue images. Current vision-language (VL) models often struggle to capture the complex reasoning required for interpreting structured pathological reports. To address these limitations, we propose PathoHR-Bench, a novel benchmark designed to evaluate VL models’ abilities in hierarchical semantic understanding and compositional reasoning within the pathology domain. Results of this benchmark reveal that existing VL models fail to effectively model intricate cross-modal relationships, hence limiting their applicability in clinical setting. To overcome this, we further introduce a pathology-specific VL training scheme that generates enhanced and perturbed samples for multimodal contrastive learning. Experimental evaluations demonstrate that our approach achieves state-of-the-art performance on PathoHR-Bench and six additional pathology datasets, highlighting its effectiveness in fine-grained pathology representation.

People are increasingly seeking healthcare information from large language models (LLMs) via interactive chatbots, yet the nature and inherent risks of these conversations remain largely unexplored. In this paper, we filter large-scale conversational AI datasets to achieve HealthChat-11K, a curated dataset of 11K real-world conversations composed of 25K user messages. We use HealthChat-11K and a clinician-driven taxonomy for how users interact with LLMs when seeking healthcare information in order to systematically study user interactions across 21 distinct health specialties. Our analysis reveals insights into the nature of how and why users seek health information, such as common interactions, instances of incomplete context, affective behaviors, and interactions (e.g., leading questions) that can induce sycophancy, underscoring the need for improvements in the healthcare support capabilities of LLMs deployed as conversational AI. We release code and artifacts to retrieve our analyses and combine them into a curated dataset for further research.

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Dynamic Evaluation for Oversensitivity in LLMs
Sophia Xiao Pu | Sitao Cheng | Xin Eric Wang | William Yang Wang

Oversensitivity occurs when language models defensively reject prompts that are actually benign. This behavior not only disrupts user interactions but also obscures the boundary between harmful and harmless content. Existing benchmarks rely on static datasets that degrade over time as models evolve, leading to data contamination and diminished evaluative power. To address this, we develop a framework that dynamically generates model-specific challenging datasets, capturing emerging defensive patterns and aligning with each model’s unique behavior. Building on this approach, we construct OverBench, a benchmark that aggregates these datasets across diverse LLM families, encompassing 450,000 samples from 25 models. OverBench provides a dynamic and evolving perspective on oversensitivity, allowing for continuous monitoring of defensive triggers as models advance, highlighting vulnerabilities that static datasets overlook.

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Self-Correcting Code Generation Using Small Language Models
Jeonghun Cho | Deokhyung Kang | Hyounghun Kim | Gary Lee

Self-correction has demonstrated potential in code generation by allowing language models to revise and improve their outputs through successive refinement. Recent studies have explored prompting-based strategies that incorporate verification or feedback loops using proprietary models, as well as training-based methods that leverage their strong reasoning capabilities. However, whether smaller models possess the capacity to effectively guide their outputs through self-reflection remains unexplored. Our findings reveal that smaller models struggle to exhibit reflective revision behavior across both self-correction paradigms. In response, we introduce CoCoS, an approach designed to enhance the ability of small language models for multi-turn code correction. Specifically, we propose an online reinforcement learning objective that trains the model to confidently maintain correct outputs while progressively correcting incorrect outputs as turns proceed. Our approach features an accumulated reward function that aggregates rewards across the entire trajectory and a fine-grained reward better suited to multi-turn correction scenarios. This facilitates the model in enhancing initial response quality while achieving substantial improvements through self-correction. With 1B-scale models, CoCoS achieves improvements of 35.8% on the MBPP and 27.7% on HumanEval compared to the baselines.

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A Unified Framework for N-ary Property Information Extraction in Materials Science
Van-Thuy Phi | Yuji Matsumoto

This paper presents a unified framework for extracting n-ary property information from materials science literature, addressing the critical challenge of capturing complex relationships that often span multiple sentences. We introduce three complementary approaches: RE-Composition, which transforms binary relations into n-ary structures; Direct EAE, which models polymer properties as events with multiple arguments; and LLM-Guided Assembly, which leverages high-confidence entity and relation outputs to guide structured extraction. Our framework is built upon two novel resources: MatSciNERE, a comprehensive corpus for materials science entities and relations, and PolyEE, a specialized corpus for polymer property events. Through strategic synthetic data generation for both NER and EAE tasks, we achieve significant performance improvements (up to 5.34 F1 points). Experiments demonstrate that our combined approaches outperform any single method, with the LLM-guided approach achieving the highest F1 score (71.53%). The framework enables more comprehensive knowledge extraction from scientific literature, supporting materials discovery and database curation applications. We plan to release our resources and trained models to the research community.

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A Benchmark for Translations Across Styles and Language Variants
Xin Tan | Bowei Zou | AiTi Aw

As machine translation (MT) rapidly advances in bridging global communication gaps, there is growing interest in variety-targeted translation for fine-grained language variants and specific translation styles. This translation variant aims to generate target outputs that are not only contextually accurate but also culturally sensitive. However, the lack of comprehensive evaluation benchmarks has hindered progress in this field. To bridge this gap, this work focuses on the translation across styles and language variants, aiming to establish a robust foundation for the automatic evaluation of fine-grained cultural and stylistic nuances, thereby fostering innovation in culturally sensitive translations. Specifically, we evaluate translations across four key dimensions: semantic preservation, cultural and regional specificity, expression style, and fluency at both the word and sentence levels. Through detailed human evaluations, we validate the high reliability of the proposed evaluation framework. On this basis, we thoroughly assess translations of state-of-the-art large language models (LLMs) for this task, highlighting their strengths and identifying areas for future improvement.

Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary systems with opaque architectures. In this work, we propose ManuSearch, a transparent and modular multi-agent framework designed to democratize deep search for LLMs. ManuSearch decomposes the search and reasoning process into three collaborative agents: (1) a solution planning agent that iteratively formulates sub-queries, (2) an Internet search agent that retrieves relevant documents via real-time web search, and (3) a structured webpage reading agent that extracts key evidence from raw web content. To rigorously evaluate deep reasoning abilities, we introduce ORION, a challenging benchmark focused on open-web reasoning over long-tail entities, covering both English and Chinese. Experimental results show that ManuSearch substantially outperforms prior open-source baselines and even surpasses leading closed-source systems. Our work paves the way for reproducible, extensible research in open deep search systems. We release the data and code in [https://github.com/RUCAIBox/ManuSearch](https://github.com/RUCAIBox/ManuSearch).

Chat-oriented dialogue systems that deliver tangible benefits, such as sharing news or frailty prevention for seniors, require proactive acquisition of specific user information via chats on user-favored topics. This study proposes the Proactive Information Acquisition (PIA) task to support the development of these systems. In this task, a system needs to acquire a user’s answers to predefined questions without making the user feel abrupt while engaging in a chat on a predefined topic. We created and analyzed a dataset of 650 PIA chats, identifying key challenges and effective strategies for recent LLMs. Our system, designed from these insights, surpassed the performance of LLMs prompted solely with task instructions. Finally, we demonstrate that automatic evaluation of this task is reasonably accurate, suggesting its potential as a framework to efficiently develop techniques for systems dealing with complex dialogue goals, extending beyond the scope of PIA alone. Our dataset is available at: https://github.com/CyberAgentAILab/PIA

We propose a novel automatic evaluation metric for open-ended text generation, which is a substantial improvement of the recently developed method, Fourier analysis of cross-entropy (FACE), hence, FACE-2. FACE-2 is a psycholinguistically inspired metric that extracts the dynamic patterns (spectrum) of text surprisal. Examined with open-ended text generation tasks, FACE-2 significantly outperforms a broad set of baseline metrics in revealing the model scaling effect, which scales up to models of 70B parameters, while many other existing metrics fail to capture this effect. We have also confirmed the advantage of FACE-2 in producing stronger agreement with human preferences from a large human-annotated dataset. We advocate for including metrics that mine the dynamics of likelihood in evaluating open-ended text generation, which covers broader aspects of human language than only using static likelihood-based or semantic-based metrics. Code repository: https://github.com/CLCS-SUSTech/FACEScore.

Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis. However, AD is relatively understudied in a natural language processing (NLP) context, limiting its effectiveness in detecting harmful content, phishing attempts, and spam reviews. We introduce NLP-ADBench, the most comprehensive NLP anomaly detection (NLP-AD) benchmark to date, which includes eight curated datasets and 19 state-of-the-art algorithms. These span 3 end-to-end methods and 16 two-step approaches that adapt classical, non-AD methods to language embeddings from BERT and OpenAI. Our empirical results show that no single model dominates across all datasets, indicating a need for automated model selection. Moreover, two-step methods with transformer-based embeddings consistently outperform specialized end-to-end approaches, with OpenAI embeddings outperforming those of BERT. We release NLP-ADBench at https://github.com/USC-FORTIS/NLP-ADBench, providing a unified framework for NLP-AD and supporting future investigations.

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Toward Inclusive Language Models: Sparsity-Driven Calibration for Systematic and Interpretable Mitigation of Social Biases in LLMs
Prommy Sultana Hossain | Chahat Raj | Ziwei Zhu | Jessica Lin | Emanuela Marasco

Large Language Models (LLMs) such as GPT and LLaMA excel in natural language tasks, e.g., text generation and machine translation. However, inherent biases from training on vast Internet datasets potentially amplify harmful stereotypes—widely held, oversimplified, and often inaccurate generalizations about groups of people. Our contribution introduces a novel, systematic, and architecture-aware method to identify and mitigate stereotypical bias in decoder-only transformer models. This interpretable approach operates without gradient access or retraining from scratch. We first evaluate bias and then apply a bias localization mechanism that correlates internal activations with a newly defined Context Influence (CI) Score. Our method pinpoints specific attention heads that consistently align with biased shifts in model predictions. To mitigate this, we introduce a soft pruning strategy that scales attention head parameters based on their correlation strength, followed by lightweight fine-tuning to maintain fluent text generation. Experiments across five models demonstrate our approach reduces bias by up to 37% on BBQ, 32% on StereoSet, and 33% on CrowS-Pairs while simultaneously improving reasoning performance on MMLU by up to 10%.

Scientific claim verification against tables typically requires predicting whether a claim is supported or refuted given a table. However, we argue that predicting the final label alone is insufficient: it reveals little about the model’s reasoning and offers limited interpretability. To address this, we reframe table–text alignment as an explanation task, requiring models to identify the table cells essential for claim verification. We build a new dataset by extending the SciTab benchmark with human-annotated cell-level rationales. Annotators verify the claim label and highlight the minimal set of cells needed to support their decision. After the annotation process, we utilize the collected information and propose a taxonomy for handling ambiguous cases. Our experiments show that (i) incorporating table alignment information improves claim verification performance, and (ii) most LLMs, while often predicting correct labels, fail to recover human-aligned rationales, suggesting that their predictions do not stem from faithful reasoning.

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DCRM: A Heuristic to Measure Response Pair Quality in Preference Optimization
Chengyu Huang | Tanya Goyal

Recent research has attempted to associate preference optimization (PO) performance with the underlying preference datasets. In this work, our observation is that the differences between the preferred response y⁺ and dispreferred response y^- influence what LLMs can learn, which may not match the desirable differences to learn. Therefore, we use distance and reward margin to quantify these differences, and combine them to get Distance Calibrated Reward Margin (DCRM), a metric that measures the quality of a response pair for PO. Intuitively, DCRM encourages minimal noisy differences and maximal desired differences. With this, we study three types of commonly used preference datasets, classified along two axes: the source of the responses and the preference labeling function. We establish a general correlation between higher DCRM of the training set and better learning outcome. Inspired by this, we propose a best-of-N² pairing method that selects response pairs with the highest DCRM. Empirically, in various settings, our method produces training datasets that can further improve models’ performance on AlpacaEval, MT-Bench, and Arena-Hard over the existing training sets.

Large Language Models (LLMs) have shown strong capabilities in zero-shot reasoning and generalization to new tasks. However, the zero-shot performance of general LLMs on complex tasks, such as multi-hop reasoning, remains suboptimal, while reasoning LLMs suffer from hallucinations and unfaithfulness. In this paper, to handle these limitations, we introduce a novel structure analysis method that helps LLMs better understand the question structure and guide the problem-solving process. We demonstrate that existing reasoning strategies, such as Chain-of-Thought and ReAct, significantly benefit from the LLM’s inherent understanding of semantic structure. We further ground our method in the theory of probabilistic graphical models to support its effectiveness. To enhance the reasoning process, we augment the structure analysis with refinement and retrieval capabilities, forming a multi-agent reasoning system called Structure-oriented Autonomous Reasoning Agents (SARA). Extensive experiments show that SARA significantly improves zero-shot performance on knowledge-intensive and mathematical tasks. Remarkably, our approach makes a general LLM competitive with dedicated reasoning models in several benchmarks and demonstrates strong robustness against corrupted reasoning paths.

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LLM-based Open Domain Planning by Leveraging Entity-Attribute-Level Domain Models
Dongning Rao | Songlin He | Zhihua Jiang | Ruishi Liang

Currently, large language models (LLMs) based Open domain Natural language planning (LONG) has considerable room for improvement. E.g., non-reusable plans with incomplete intermediate states and missing steps hinder real-world applications. To remedy these flaws, this paper establishes a dataset with a baseline for LONG. The GOLD dataset provides the largest dataset for textual procedures, along with corresponding reusable formal planning domain definitions, to date. The baseline, DIGGER, leverages entity-attribute-level action models, which reveal relevant implicit physical properties (aka attributes) of salient entities in actions. DIGGER first extracts action models and builds typed entity lists from textual procedures. Then, it builds goal states for new tasks and instantiates grounded actions using domain prediction. At last, plans are generalized and translated into textual procedures by LLM. Reference-based metrics, LLM-as-a-Judge, and human evaluation are employed to comprehensively evaluate LONG. Experiments on GOLD validate that DIGGER is stronger and more generalizable than recently proposed approaches and LLMs. I.e., DIGGER is the best in seen domains and applicable to unseen domains without adaptation. Specifically, the BLEU-1 score increased from 0.385 to 0.408 on seen domains and rose to 0.310 on unseen domains.

Event causality identification (ECI) is a challenging task that involves predicting causal relationships between events in text. Existing prompt-learning-based methods typically concatenate in-context examples only at the input layer, this shallow integration limits the model’s ability to capture the abstract semantic cues necessary for identifying complex causal relationships. To address this limitation, we propose a novel model called Deep In-Context Prompt (DICP), which injects in-context examples into the deeper layer of a pre-trained language model (PLM). This strategy enables the model to leverage the hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions. Moreover, DICP introduces a multi-layer prompt injection mechanism, distributing diverse in-context examples across multiple transformer layers. This design allows the model to recognize a broader range of causal patterns and improves its generalization across different contexts. We evaluate the DICP model through extensive experiments on two widely used datasets, demonstrating its significant improvement in ECI performance compared to existing approaches. Furthermore, we explore the impact of varying the number of deep layers on performance, providing valuable insights into the optimal layer configuration for ECI tasks.

We present an Audio-Visual Language Model (AVLM) for expressive speech generation by integrating full-face visual cues into a pre-trained expressive speech model. We explore multiple visual encoders and multimodal fusion strategies during pre-training to identify the most effective integration approach. Subsequent fine-tuning on emotion recognition and expressive dialogue tasks yields substantial gains over speech-only baselines (e.g., +5 F1 in emotion recognition). AVLM highlights the value of expressive visual information in guiding speech generation and offers a foundation for end-to-end multimodal conversational systems.

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GRV-KBQA: A Three-Stage Framework for Knowledge Base Question Answering with Decoupled Logical Structure, Semantic Grounding and Structure-Aware Validation
Yuhang Tian | Pan Yang | Dandan Song | Zhijing Wu | Hao Wang

Knowledge Base Question Answering (KBQA) is a fundamental task that enables natural language interaction with structured knowledge bases (KBs).Given a natural language question, KBQA aims to retrieve the answers from the KB. However, existing approaches, including retrieval-based, semantic parsing-based methods and large-language model-based methods often suffer from generating non-executable queries and inefficiencies in query execution. To address these challenges, we propose GRV-KBQA, a three-stage framework that decouples logical structure generation from semantic grounding and incorporates structure-aware validation to enhance accuracy. Unlike previous methods, GRV-KBQA explicitly enforces KB constraints to improve alignment between generated logical forms and KB structures. Experimental results on WebQSP and CWQ show that GRV-KBQA significantly improves performance over existing approaches. The ablation study conducted confirms the effectiveness of the decoupled logical form generation and validation mechanism of our framework.

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Improving Prompt Generalization for Cross-prompt Essay Trait Scoring from the Scoring-invariance Perspective
Jiong Wang | Shengquan Yu

Cross-prompt trait scoring task aims to learn generalizable scoring capabilities from source- prompt data, enabling automatic scoring across multiple dimensions on unseen essays. Existing research on cross-prompt trait essay scoring primarily focuses on improving model generalization by obtaining prompt-invariant representations. In this paper, we approach the research problem from a different perspective on invariance learning and propose a scoring-invariant learning objective. This objective encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features. To further enhance the model’s ability to score across multiple dimensions, we introduce a trait feature extraction network based on routing gates into the scoring architecture and propose a trait consistency scoring objective to encourage the model to balance the diversity of trait-specific features with scoring consistency across traits when learning trait-specific essay features. Extensive experiments demonstrate the effectiveness of our approach, showing advantages in multi-trait scoring performance and achieving significant improvements with low-resource prompts.

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When Format Changes Meaning: Investigating Semantic Inconsistency of Large Language Models
Cheongwoong Kang | Jongeun Baek | Yeonjea Kim | Jaesik Choi

Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks. However, they remain vulnerable to semantic inconsistency, where minor formatting variations result in divergent predictions for semantically equivalent inputs. Our comprehensive evaluation reveals that this brittleness persists even in state-of-the-art models such as GPT-4o, posing a serious challenge to their reliability. Through a mechanistic analysis, we find that semantic-equivalent input changes induce instability in internal representations, ultimately leading to divergent predictions. This reflects a deeper structural issue, where form and meaning are intertwined in the embedding space. We further demonstrate that existing mitigation strategies, including direct fine-tuning on format variations, do not fully address semantic inconsistency, underscoring the difficulty of the problem. Our findings highlight the need for deeper mechanistic understanding to develop targeted methods that improve robustness.

Existing LLM red-teaming approaches prioritize high attack success rate, often resulting in high-perplexity prompts. This focus overlooks low-perplexity attacks that are more difficult to filter, more likely to arise during benign usage, and more impactful as negative downstream training examples. In response, we introduce ASTPrompter, a single-step optimization method that uses contrastive preference learning to train an attacker to maintain low perplexity while achieving a high attack success rate (ASR). ASTPrompter achieves an attack success rate 5.1 times higher on Llama-8.1B while using inputs that are 2.1 times more likely to occur according to the frozen LLM. Furthermore, our attack transfers to Mistral-7B, Qwen-7B, and TinyLlama in both black- and white-box settings. Lastly, by tuning a single hyperparameter in our method, we discover successful attack prefixes along an efficient frontier between ASR and perplexity, highlighting perplexity as a previously under-considered factor in red-teaming.

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How Do Large Language Models Perform on PDE Discovery: A Coarse-to-fine Perspective
Xiao Luo | Changhu Wang | Yizhou Sun | Wei Wang

This paper studies the problem of how to use large language models (LLMs) to identify the underlying partial differential equations (PDEs) out of very limited observations of a physical system. Previous methods usually utilize physical-informed neural networks (PINNs) to learn the PDE solver and coefficient of PDEs simultaneously, which could suffer from performance degradation under extreme data scarcity. Towards this end, this paper attempts to utilize LLMs to solve this problem without further fine-tuning by proposing a novel framework named LLM for PDE Discovery (LLM4PD). The core of our LLM4PD is to utilize a coarse-to-fine paradigm to automatically discover underlying PDEs. In the coarse phase, LLM4PD selects the crucial terms from a library with hierarchical prompts and incorporates a review agent to enhance the accuracy. In the fine phase, LLM4PD interacts with a PDE solver to optimize the coefficient of the selected terms with the optimization trajectory. We also provide an adaptive hybrid optimization strategy switching between fine-tuning and exploration to balance stability and efficiency. Extensive experiments on several systems validate the effectiveness of our proposed LLM4PD in different settings.

Supervised fine-tuning (SFT) is crucial for aligning Large Language Models (LLMs) with human instructions. The primary goal during SFT is to select a small yet representative subset of training data from the larger pool, such that fine-tuning with this subset achieves results comparable to or even exceeding those obtained using the entire dataset. However, most existing data selection techniques are designed for small-scale data pools, which fail to meet the demands of real-world SFT scenarios. In this paper, we replicated several self-scoring methods—those that do not rely on external model assistance—on two million-scale datasets, and found that nearly all methods struggled to significantly outperform random selection when dealing with such large-scale data pools. Moreover, our comparisons suggest that, during SFT, diversity in data selection is more critical than simply focusing on high-quality data. We also analyzed the limitations of several current approaches, explaining why they perform poorly on large-scale datasets and why they are unsuitable for such contexts. Finally, we found that filtering data by token length offers a stable and efficient method for improving results. This approach, particularly when training on long-text data, proves highly beneficial for relatively weaker base models, such as Llama3. The code is available at https://github.com/xiatingyu/SFT-DataSelection-at-scale.

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PromptKeeper: Safeguarding System Prompts for LLMs
Zhifeng Jiang | Zhihua Jin | Guoliang He

System prompts are widely used to guide the outputs of large language models (LLMs). These prompts often contain business logic and sensitive information, making their protection essential. However, adversarial and even regular user queries can exploit LLM vulnerabilities to expose these hidden prompts. To address this issue, we propose PromptKeeper, a defense mechanism designed to safeguard system prompts by tackling two core challenges: reliably detecting leakage and mitigating side-channel vulnerabilities when leakage occurs. By framing detection as a hypothesis-testing problem, PromptKeeper effectively identifies both explicit and subtle leakage. Upon leakage detected, it regenerates responses using a dummy prompt, ensuring that outputs remain indistinguishable from typical interactions when no leakage is present. PromptKeeper ensures robust protection against prompt extraction attacks via either adversarial or regular queries, while preserving conversational capability and runtime efficiency during benign user interactions.

The absence of explicit communication channels between automated vehicles (AVs) and other road users requires the use of external Human-Machine Interfaces (eHMIs) to convey messages effectively in uncertain scenarios. Currently, most eHMI studies employ predefined text messages and manually designed actions to perform these messages, which limits the real-world deployment of eHMIs, where adaptability in dynamic scenarios is essential. Given the generalizability and versatility of large language models (LLMs), they could potentially serve as automated action designers for the message-action design task. To validate this idea, we make three contributions: (1) We propose a pipeline that integrates LLMs and 3D renderers, using LLMs as action designers to generate executable actions for controlling eHMIs and rendering action clips. (2) We collect a user-rated Action-Design Scoring dataset comprising a total of 320 action sequences for eight intended messages and four representative eHMI modalities. The dataset validates that LLMs can translate intended messages into actions close to a human level, particularly for reasoning-enabled LLMs. (3) We introduce two automated raters, Action Reference Score (ARS) and Vision-Language Models (VLMs), to benchmark 18 LLMs, finding that the VLM aligns with human preferences yet varies across eHMI modalities. The source code, prompts, Blender scenarios, and rendered clips are available at https://github.com/ApisXia/AutoActionDesign.

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A Dynamic Fusion Model for Consistent Crisis Response
Xiaoying Song | Anirban Saha Anik | Eduardo Blanco | Vanessa Frias-Martinez | Lingzi Hong

In response to the urgent need for effective communication with crisis-affected populations, automated responses driven by language models have been proposed to assist in crisis communications. A critical yet often overlooked factor is the consistency of response style, which could affect the trust of affected individuals in responders. Despite its importance, few studies have explored methods for maintaining stylistic consistency across generated responses. To address this gap, we propose a novel metric for evaluating style consistency and introduce a fusion-based generation approach grounded in this metric. Our method employs a two-stage process: it first assesses the style of candidate responses and then optimizes and integrates them at the instance level through a fusion process. This enables the generation of high-quality responses while significantly reducing stylistic variation between instances. Experimental results across multiple datasets demonstrate that our approach consistently outperforms baselines in both response quality and stylistic uniformity.

Recent advances in large language models (LLMs) have significantly improved automated code generation, enabling tools such as GitHub Copilot and CodeWhisperer to assist developers in a wide range of programming tasks. However, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenging and underexplored area, especially as modern app interfaces become increasingly intricate. In this work, we propose UIOrchestra, a collaborative multi-agent system designed for the AppUI2Code task, which aims to reconstruct static single-page applications from design mockups. UIOrchestra integrates three specialized agents, layout description, code generation, and difference analysis agent that work collaboratively to address the limitations of single-model approaches. To facilitate robust evaluation, we introduce APPUI, the first benchmark dataset for AppUI2Code, constructed through a human-in-the-loop process to ensure data quality and coverage. Experimental results demonstrate that UIOrchestra outperforms existing methods in reconstructing complex app pages and highlight the necessity of multi-agent collaboration for this task. We hope our work will inspire further research on leveraging LLMs for front-end automation. The code and data will be released upon paper acceptance.

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CrossQG: Improving Difficulty-Controllable Question Generation through Consistency Enhancement
Kunze Li | Yu Zhang

Automatically generating questions with controlled difficulty has great application value, especially in the field of education. Although large language models are capable of generating questions of various difficulty levels, the generated questions often fail to align with the given target difficulty. To mitigate this issue, we propose CrossQG, a novel question generation method that requires no tuning of generator parameters, yet significantly improves difficulty consistency. Specifically, CrossQG consists of two steps: (1) contrast enhancement, which leverages questions from different difficulty levels to enhance the base models’ understanding of the target difficulty, and (2) cross filtering, which compares generated questions across different difficulty levels and filters out those that do not meet the target difficulty. We evaluate CrossQG on three high-quality question answering datasets. Experimental results demonstrate that across multiple models, CrossQG significantly outperforms several mainstream methods, achieving superior consistency with target difficulty and improving question quality. Notably, without generator training, CrossQG surpasses supervised fine-tuning in various instances.

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Progressive Facial Granularity Aggregation with Bilateral Attribute-based Enhancement for Face-to-Speech Synthesis
Yejin Jeon | Youngjae Kim | Jihyun Lee | Hyounghun Kim | Gary Lee

For individuals who have experienced traumatic events such as strokes, speech may no longer be a viable means of communication. While text-to-speech (TTS) can be used as a communication aid since it generates synthetic speech, it fails to preserve the user’s own voice. As such, face-to-voice (FTV) synthesis, which derives corresponding voices from facial images, provides a promising alternative. However, existing methods rely on pre-trained visual encoders, and finetune them to align with speech embeddings, which strips fine-grained information from facial inputs such as gender or ethnicity, despite their known correlation with vocal traits. Moreover, these pipelines are multi-stage, which requires separate training of multiple components, thus leading to training inefficiency. To address these limitations, we utilize fine-grained facial attribute modeling by decomposing facial images into non-overlapping segments and progressively integrating them into a multi-granular representation. This representation is further refined through multi-task learning of speaker attributes such as gender and ethnicity at both the visual and acoustic domains. Moreover, to improve alignment robustness, we adopt a multi-view training strategy by pairing various visual perspectives of a speaker in terms of different angles and lighting conditions, with identical speech recordings. Extensive subjective and objective evaluations confirm that our approach substantially enhances face-voice congruence and synthesis stability.

Health misinformation spreading online poses a significant threat to public health. Researchers have explored methods for automatically generating counterspeech to health misinformation as a mitigation strategy. Existing approaches often produce uniform responses, ignoring that the health literacy level of the audience could affect the accessibility and effectiveness of counterspeech. We propose a Controlled-Literacy framework using retrieval-augmented generation (RAG) with reinforcement learning (RL) to generate tailored counterspeech adapted to different health literacy levels. In particular, we retrieve knowledge aligned with specific health literacy levels, enabling accessible and factual information to support generation. We design a reward function incorporating subjective user preferences and objective readability-based rewards to optimize counterspeech to the target health literacy level. Experiment results show that Controlled-Literacy outperforms baselines by generating more accessible and user-preferred counterspeech. This research contributes to more equitable and impactful public health communication by improving the accessibility and comprehension of counterspeech to health misinformation.

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FNSCC: Fuzzy Neighborhood-Aware Self-Supervised Contrastive Clustering for Short Text
Zijian Zheng | Yonghe Lu | Jian Yin

Short texts pose significant challenges for clustering due to semantic sparsity, limited context, and fuzzy category boundaries. Although recent contrastive learning methods improve instance-level representation, they often overlook local semantic structure within the clustering head. Moreover, treating semantically similar neighbors as negatives impair cluster-level discrimination. To address these issues, we propose Fuzzy Neighborhood-Aware Self-Supervised Contrastive Clustering (FNSCC) framework. FNSCC incorporates neighborhood information at both the instance-level and cluster-level. At the instance-level, it excludes neighbors from the negative sample set to enhance inter-cluster separability. At the cluster-level, it introduces fuzzy neighborhood-aware weighting to refine soft assignment probabilities, encouraging alignment with semantically coherent clusters. Experiments on multiple benchmark short text datasets demonstrate that FNSCC consistently outperforms state-of-the-art models in accuracy and normalized mutual information. Our code is available at https://github.com/zjzone/FNSCC.

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AuraDial: A Large-Scale Human-Centric Dialogue Dataset for Chinese AI Psychological Counseling
Xiantao Zhang

This paper introduces AuraDial, a large-scale, human-centric dialogue dataset for Chinese AI psychological counseling, comprising over 300,000 single-turn dialogues and 90,000 multi-turn dialogue sessions. A key distinction of AuraDial is its instruction set, primarily derived from real-world user queries, better reflecting genuine expression patterns compared to synthetic or template-based alternatives. Furthermore, we propose an innovative rephrasing-based data generation methodology designed to foster more human-like and empathetic responses, addressing a common shortcoming in AI-generated dialogue. Experimental results demonstrate that models fine-tuned on AuraDial significantly outperform those trained on other public datasets in generating empathetic and relevant replies. AuraDial offers a novel, valuable resource to the Chinese NLP community for advancing AI in psychological counseling. The dataset is publicly available at [https://huggingface.co/datasets/Mxode/AuraDial](https://huggingface.co/datasets/Mxode/AuraDial).

As LLM-based agents become increasingly prevalent, triggers implanted in user queries or environment feedback can activate hidden backdoors, raising critical concerns about safety vulnerabilities in agents.However, traditional backdoor attacks are often detectable by safety audits that analyze the reasoning process of agents, hindering further progress in agent safety research.To this end, we propose a novel backdoor implantation strategy called Dynamically Encrypted Multi-Backdoor Implantation Attack. Specifically, we introduce dynamic encryption, which maps the backdoor into benign content, effectively circumventing safety audits.To enhance stealthiness, we further decompose the backdoor into multiple sub-backdoor fragments. Based on these advancements, backdoors are allowed to bypass safety audits significantly.Additionally, we present AgentBackdoorEval, a dataset designed for the comprehensive evaluation of agent backdoor attacks.Experimental results across multiple datasets demonstrate that our method achieves an attack success rate approaching 100% while maintaining a detection rate of 0%, illustrating its effectiveness in evading safety audits.Our findings highlight the limitations of existing safety mechanisms in detecting advanced attacks, underscoring the urgent need for more robust defenses against backdoor threats.Code and data are available at https://github.com/whfeLingYu/DemonAgent.

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MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation
Xinping Lei | Tong Zhou | Yubo Chen | Kang Liu | Jun Zhao

Large Language Models (LLMs) hold significant promise for accelerating academic ideation but face critical challenges in grounding ideas and mitigating confirmation bias during refinement. To address these limitations, we propose MotivGraph-SoIQ, a novel framework that enhances LLM ideation by integrating a Motivational Knowledge Graph (MotivGraph), which provides essential grounding from research literature, with a Q-Driven Socratic Ideator. The Ideator, a dual-agent system utilizing Socratic questioning, facilitates a rigorous refinement process that mitigates confirmation bias and significantly improves idea quality across dimensions of novelty, experimental feasibility, and motivation. Our experimental results demonstrate MotivGraph-SoIQ’s effectiveness. Comparative studies show significant quantitative improvements over SOTA methods across LLM-based scoring, ELO ranking, and human evaluation. Ablation studies further validate the crucial contributions of both the MotivGraph for enhancing idea novelty and practicality, and the Socratic dialogue with the teacher agent for substantial quality improvement. This work underscores the potential of combining structured knowledge with interactive, critique-based refinement for robust LLM ideation.

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ExpertGenQA: Open-ended QA generation in Specialized Domains
Haz Sameen Shahgir | Chansong Lim | Jia Chen | Evangelos E. Papalexakis | Yue Dong

Generating high-quality question–answer (QA) pairs for specialized technical domains is essential for advancing knowledge comprehension, yet remains challenging. Existing methods often yield generic or shallow questions that fail to reflect the depth and structure of expert-written examples. We propose ExpertGenQA, a generation protocol that combines few-shot prompting with dual categorization by topic and question style to produce more diverse and cognitively meaningful QA pairs. ExpertGenQA achieves twice the efficiency of standard few-shot methods while maintaining 94.4% topic coverage. Unlike LLM-based judges, which often favor surface fluency, Bloom’s Taxonomy analysis shows that ExpertGenQA better captures expert-level cognitive complexity. When used to train retrieval systems, our questions improve top-1 accuracy by 13.02%, demonstrating their practical value for domain-specific applications.

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VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation
Yuansheng Ni | Ping Nie | Kai Zou | Xiang Yue | Wenhu Chen

Large language models (LLMs) often struggle with visualization tasks like plotting diagrams, charts, where success depends on both code correctness and visual semantics. Existing instruction-tuning datasets lack execution-grounded supervision and offer limited support for iterative code correction, resulting in fragile and unreliable plot generation. We present **VisCode-200K**, a large-scale instruction tuning dataset for Python-based visualization and self-correction. It contains over 200K examples from two sources: (1) validated plotting code from open-source repositories, paired with natural language instructions and rendered plots; and (2) 45K multi-turn correction dialogues from Code-Feedback, enabling models to revise faulty code using runtime feedback. We fine-tune Qwen2.5-Coder-Instruct on VisCode-200K to create **VisCoder**, and evaluate it on PandasPlotBench. VisCoder significantly outperforms strong open-source baselines and approaches the performance of proprietary models like GPT-4o-mini. We further adopt a self-debug evaluation protocol to assess iterative repair, demonstrating the benefits of feedback-driven learning for executable, visually accurate code generation.

Large language models (LLMs) have advanced virtual educators and learners, bridging NLP with AI4Education. Existing work often lacks scalability and fails to leverage diverse, large-scale course content, with limited frameworks for assessing pedagogic quality. To this end, we propose WikiHowAgent, a multi-agent workflow leveraging LLMs to simulate interactive teaching-learning conversations. It integrates teacher and learner agents, an interaction manager, and an evaluator to facilitate procedural learning and assess pedagogic quality. We introduce a dataset of 114,296 teacher-learner conversations grounded in 14,287 tutorials across 17 domains and 727 topics. Our evaluation protocol combines computational and rubric-based metrics with human judgment alignment. Results demonstrate the workflow’s effectiveness in diverse setups, offering insights into LLM capabilities across domains. Our datasets and implementations are fully open-sourced.

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Visual Program Distillation with Template-Based Augmentation
Michal Shlapentokh-Rothman | Yu-Xiong Wang | Derek Hoiem

Adapting visual programming or prompting large language models (LLMs) to generate executable code for visual tasks like visual question answering (VQA) for specialized tasks or domains remains challenging due to high annotation and inference costs. We propose a low-cost visual program distillation method that can be used for models with at most 1 billion parameters and requires no human-generated program annotations. We achieve this through synthetic data augmentation based on decoupling programs into higher-level skills, called templates, and their corresponding arguments. Experimental results show that, with a relatively small amount of question/answer data, small language models can generate high-quality specialized visual programs with the added benefit of much faster inference.

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NeighXLM: Enhancing Cross-Lingual Transfer in Low-Resource Languages via Neighbor-Augmented Contrastive Pretraining
Sicheng Wang | Wenyi Wu | Zibo Zhang

Recent progress in multilingual pretraining has yielded strong performance on high-resource languages, albeit with limited generalization to genuinely low-resource settings. While prior approaches have attempted to enhance cross-lingual transfer through representation alignment or contrastive learning, they remain constrained by the extremely limited availability of parallel data to provide positive supervision in target languages. In this work, we introduce NeighXLM, a neighbor-augmented contrastive pretraining framework that enriches target-language supervision by mining semantic neighbors from unlabeled corpora. Without relying on human annotations or translation systems, NeighXLM exploits intra-language semantic relationships captured during pretraining to construct high-quality positive pairs. The approach is model-agnostic and can be seamlessly integrated into existing multilingual pipelines. Experiments on Swahili demonstrate the effectiveness of NeighXLM in improving cross-lingual retrieval and zero-shot transfer performance.

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ICLER: Intent CLassification with Enhanced Reasoning
Dezheng Gao | Dong Xiaozheng | SHuangtao Yang | Bo Fu

In recent years, intent classification technology based on In-Context Learning (ICL) has made significant progress. However, when applied to enterprise vertical domains, existing methods are inadequate in identifying micro-grained intentions. This study identifies two primary causes of errors in data analysis: (1) Retrieving incorrect instances, this is often due to the limitations of embedding models in capturing subtle sentence-level information in business scenarios (such as entity-related or phenomenon-specific details) (2) Insufficient reasoning ability of Large Language Models (LLMs), which tend to rely on surface-level semantics while overlooking deeper semantic associations and business logic, leading to misclassification. To address these issues, we propose ICLER, an intent classification method with enhanced reasoning. This method first optimizes the embedding model by introducing a reasoning mechanism to enhance its ability to fine-grained sentence-level information. Then, this mechanism is incorporated into the ICL framework, maintaining computational efficiency while significantly enhancing intent recognition accuracy. Experimental results demonstrate that ICLER significantly outperforms the original ICL method in intent identification within vertical domains. Moreover, it yields accuracy improvements of 0.04% to 1.14% on general datasets and its fine-tuned embedding model achieves an average performance gain of 5.56% on selected classification tasks in the MTEB benchmark.

Visual presentations are vital for effective communication. Early attempts to automate their creation using deep learning often faced issues such as poorly organized layouts, inaccurate text summarization, and a lack of image understanding, leading to mismatched visuals and text. These limitations restrict their application in formal contexts like business and scientific research. To address these challenges, we propose PreGenie, an agentic and modular framework powered by multimodal large language models (MLLMs) for generating high-quality visual presentations.PreGenie is built on the Slidev presentation framework, where slides are rendered from Markdown code. It operates in two stages: (1) Analysis and Initial Generation, which summarizes multimodal input and generates initial code, and (2) Review and Re-generation, which iteratively reviews intermediate code and rendered slides to produce final, high-quality presentations. Each stage leverages multiple MLLMs that collaborate and share information. Comprehensive experiments demonstrate that PreGenie excels in multimodal understanding, outperforming existing models in both aesthetics and content consistency, while aligning more closely with human design preferences.

Large language models (LLMs) possess strong multilingual capabilities, and combining Reinforcement Learning from Human Feedback (RLHF) with translation tasks has shown great potential. However, we observe that this paradigm performs unexpectedly poorly when applied to colloquial subtitle translation tasks. In this work, we investigate this issue and find that the offline reward model (RM) gradually diverges from the online LLM due to distributional shift, ultimately leading to undesirable training outcomes. To address this, we propose RIVAL, an adversarial training framework that formulates the process as a min–max game between the RM and the LLM. RIVAL iteratively updates the both models, with the RM trained to distinguish strong from weak translations (qualitative preference reward), and the LLM trained to enhance its translation for closing this gap. To stabilize training and improve generalizability, we also incorporate quantitative preference reward (e.g., BLEU) into the RM, enabling reference-free quality modeling aligned with human evaluation. Through extensive experiments, we demonstrate that the proposed training framework significantly improves upon translation baselines.

Understanding temporal concepts and answering time-sensitive questions is crucial yet a challenging task for question-answering systems powered by large language models (LLMs). Existing approaches either update the parametric knowledge of LLMs with new facts, which is resource-intensive and often impractical, or integrate LLMs with external knowledge retrieval (i.e., retrieval-augmented generation). However, off-the-shelf retrievers often struggle to identify relevant documents that require intensive temporal reasoning. To systematically study time-sensitive question answering, we introduce the TempRAGEval benchmark, which repurposes existing datasets by incorporating complex temporal perturbations and gold evidence labels. As anticipated, all existing retrieval methods struggle with these temporal reasoning-intensive questions. We further propose Modular Retrieval (MRAG), a trainless framework that includes three modules: (1) Question Processing that decomposes question into a main content and a temporal constraint; (2) Retrieval and Summarization that retrieves, splits, and summarize evidence passages based on the main content; (3) Semantic-Temporal Hybrid Ranking that scores semantic and temporal relevance separately for each fine-grained evidence. On TempRAGEval, MRAG significantly outperforms baseline retrievers in retrieval performance, leading to further improvements in final answer accuracy.

Chain-of-thought (CoT) reasoning boosts large language models’ (LLMs) performance on complex tasks but faces two key limitations: a lack of reliability when solely relying on LLM-generated reasoning chains and interference from natural language reasoning steps with the models’ inference process, also known as the inference logic of LLMs. To address these issues, we propose CoT-RAG, a novel reasoning framework with three key designs: (i) Knowledge Graph-driven CoT Generation, featuring knowledge graphs to modulate reasoning chain generation of LLMs, thereby enhancing reasoning credibility; (ii) Learnable Knowledge Case-aware RAG, which incorporates retrieval-augmented generation (RAG) into knowledge graphs to retrieve relevant sub-cases and sub-descriptions, providing LLMs with learnable information; (iii) Pseudo-Program Prompting Execution, which promotes greater logical rigor by guiding LLMs to execute reasoning tasks as pseudo-programs. Evaluations on nine public datasets spanning three reasoning tasks reveal significant accuracy gains—ranging from 4.0% to 44.3%–over state-of-the-art methods. Furthermore, tests on four domain-specific datasets demonstrate exceptional accuracy and efficient execution, underscoring its practical applicability and scalability. Our code and data are available at https://github.com/hustlfy123/CoT-RAG.

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TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data
Changjiang Jiang | Fengchang Yu | Haihua Chen | Wei Lu | Jin Zeng

Complex reasoning over tabular data is crucial in real-world data analysis, yet large language models (LLMs) often underperform due to complex queries, noisy data, and limited numerical capabilities. To address these issues, we propose TabDSR, a three-agent framework consisting of: (1) a query decomposer that breaks down complex questions, (2) a table sanitizer that cleans and filters noisy tables, and (3) a program-of-thoughts (PoT)-based reasoner that generates executable code to derive the final answer from the sanitized table. To ensure unbiased evaluation and mitigate data leakage, we introduce a new dataset, CalTab151, specifically designed for complex numerical reasoning over tables. Experimental results demonstrate that TabDSR consistently outperforms existing methods, achieving state-of-the-art (SOTA) performance with 8.79%, 6.08%, and 19.87% accuracy improvement on TAT-QA, TableBench, and CalTab151, respectively. Moreover, our framework integrates seamlessly with mainstream LLMs, providing a robust solution for complex tabular numerical reasoning. These findings highlight the effectiveness of our framework in enhancing LLM performance for complex tabular numerical reasoning.

Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks, where models need to reason over extensive input contexts to aggregate target information. While Chain-of-Thought (CoT) prompting has shown promise for multi-step reasoning, its effectiveness for long-context scenarios remains underexplored. Through systematic investigation across diverse tasks, we demonstrate that CoT’s benefits generalize across most long-context scenarios and amplify with increasing context length. Motivated by this, we propose a process-supervised framework that teaches models to generate high-quality reasoning paths for enhanced long-context performance. Our framework incorporates a self-sampling mechanism to bootstrap reasoning paths and a novel quality assessment protocol specifically designed for long-context scenarios. This protocol evaluates both answer correctness and process reliability, with the latter decomposed into source faithfulness and intrinsic consistency components for efficient and accurate assessment. Experimental results on various long-context benchmarks demonstrate the effectiveness of our approach, achieving significant improvements over outcome supervision baselines on both in-domain tasks (+13.6/+3.8 points for LLaMA/Qwen on MuSiQue) and cross-domain generalization (+9.3/+8.1 points on average across diverse QA tasks). Our code, data and trained models will be released upon acceptance.

Multimodal documents, which are among the most prevalent data formats, combine a large amount of textual and visual content. Extracting structured triples knowledge from these documents is a highly valuable task, aimed at helping users efficiently acquire key entities and their relationships. However, existing methods face limitations in simultaneously processing long textual content and multiple associated images for triple extraction. Therefore, we propose a Multimodal Document-level Triple Extraction (MDocTE) framework. Specifically, we introduce a dynamic document graph construction method that extends the model’s scope to the entire document and the external world, while adaptively optimizing the graph structure. Next, we inject the global information and external knowledge learned by the graph neural network into the large language model, generating structured triples after deep interaction. Finally, we design a multimodal relation-aware mechanism and loss function to guide the model in reflecting on the shared information between text and visuals. We release a new triple extraction dataset for multimodal documents and conduct extensive experiments. The results demonstrate that the proposed framework outperforms the state-of-the-art baselines, thus filling the gap in multimodal document extraction. Our data is available at https://github.com/XiangLiphd/Triple-extraction-dataset-for-multimodal-documents.

Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs), including recognizing key information from visual inputs and conducting reasoning over it. While fine-tuning MLLMs for reasoning is critical, collecting and annotating charts and questions is expensive, hard to scale, and often results in low-quality annotations. To address this, we propose Code-as-Intermediary Translation (CIT), a cost-effective, efficient and scalable data synthesis method for distilling visual reasoning abilities from LLMs to MLLMs. The code serves as an intermediary that translates visual chart representations into textual representations, enabling language models to understand cross-modal information and generate reasoning chains accordingly. In this way, we can employ text-based synthesizing techniques to expand chart-plotting code and generate high-quality Q&A pairs for training models. This produces ReachQA, a dataset containing 3k reasoning-intensive charts and 20k Q&A pairs to enhance both recognition and reasoning abilities of MLLMs. Experiments show that models fine-tuned with ReachQA not only perform well on chart-related tasks but also show performance gains on general reasoning benchmarks.

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FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture
Minghao Hu | Junzhe Wang | Weisen Zhao | Qiang Zeng | Lannan Luo

Applying deep learning to malware detection has drawn great attention due to its notable performance. With the increasing prevalence of cyberattacks targeting IoT devices, there is a parallel rise in the development of malware across various Instruction Set Architectures (ISAs). It is thus important to extend malware detection capacity to multiple ISAs. However, training a deep learning-based malware detection model usually requires a large number of labeled malware samples. The process of collecting and labeling sufficient malware samples to build datasets for each ISA is labor-intensive and time-consuming. To reduce the burden of data collection, we propose to leverage the ideas of Neural Machine Translation (NMT) and Normalizing Flows (NFs) for malware detection. Specifically, when dealing with malware in a certain ISA, we translate it to an ISA with sufficient malware samples (like X86-64). This allows us to apply a model trained on one ISA to analyze malware from another ISA. Our approach reduces the data collection effort by enabling malware detection across multiple ISAs using a model trained on a single ISA.

Video Large Language Models (Video LLMs) have achieved remarkable results in video understanding tasks. However, they often suffer from heavy computational overhead due to the large number of visual tokens generated from multiple video frames. Existing visual token compression methods often rely on attention scores from language models as guidance. However, these scores exhibit inherent biases: global bias reflects a tendency to focus on the two ends of the visual token sequence, while local bias leads to an over-concentration on the same spatial positions across different frames. To address the issue of attention bias, we propose Attention-Debiased Token Pruning for Video Large Language Models(AdaTP), a novel token pruning pipeline for Video LLMs. AdaTP integrates two dedicated debiasing modules into the pipeline, targeting global attention bias and local attention bias, respectively. Without the need for additional training, our method significantly reduces the computational overhead of Video LLMs while retaining the performance of vanilla models. Extensive evaluation shows that AdaTP achieves state-of-the-art performance in various commonly used video understanding benchmarks. In particular, on LLaVA-OneVision-7B, AdaTP maintains performance without degradation while using only up to 27.3% FLOPs compared to the vanilla model. Our code will be released soon.

Recent advances in large language models (LLMs) have sparked growing interest in agentic workflows—structured sequences of LLM invocations designed to solve complex tasks. However, existing approaches often rely on static templates or manually designed workflows, which limit adaptability to diverse tasks and hinder scalability. We propose AdaptFlow, a natural language-based meta-learning framework inspired by model-agnostic meta-learning (MAML). AdaptFlow uses a bi-level optimization process: the inner loop performs task-specific adaptation via LLM-generated feedback, while the outer loop consolidates these refinements into a shared, generalizable initialization. Evaluated across question answering, code generation, and mathematical reasoning benchmarks, AdaptFlow consistently outperforms both manually crafted and automatically searched baselines, achieving state-of-the-art results with strong generalization across tasks and models.

As language models become integral to critical workflows, assessing their behavior remains a fundamental challenge – human evaluation is costly and noisy, while automated metrics provide only coarse, difficult-to-interpret signals. We introduce natural language unit tests, a paradigm that decomposes response quality into explicit, testable criteria, along with a unified scoring model, LMUnit, which combines multi-objective training across preferences, direct ratings, and natural language rationales. Through controlled human studies, we show this paradigm significantly improves inter-annotator agreement and enables more effective LLM development workflows. LMUnit achieves state-of-the-art performance on evaluation benchmarks including FLASK, BigGenBench, and RewardBench 2, while maintaining competitive results on the original RewardBench. These results validate both our proposed paradigm and scoring model, suggesting a promising path forward for language model evaluation and development. Our code has been released at github.com/ContextualAI/LMUnit with an MIT license.

Knowledge distillation for large language models often uses Chain-of-Thought (CoT) and answer pairs, but existing methods struggle with appropriate supervision signals. Uniform constraints (e.g., cross-entropy) on CoT can enforce literal, verbose reasoning and suppress expressive diversity, while solely semantic constraints on answers can reduce accuracy in classification tasks. This paper proposes ThinkAnswer Loss, an information-theoretic differential supervision framework that decouples CoT and answer supervision. ThinkAnswer Loss applies semantic similarity constraints to the CoT portion while maintaining strict literal matching for the answer. We theoretically demonstrate its connection to mutual information maximization and derive a tight upper bound on generalization error. Experimental validation on text quality assessment and mathematical reasoning tasks shows that our method maintains answer accuracy while effectively reducing CoT length and preserving semantic content, thereby accelerating inference.

Stealthy data poisoning during fine-tuning can backdoor large language models (LLMs), threatening downstream safety. Existing detectors either use classifier-style probability signals—ill-suited to generation—or rely on rewriting, which can degrade quality and even introduce new triggers. We address the practical need to efficiently remove poisoned examples before or during fine-tuning. We observe a robust signal in the response space: after applying TF-IDF to model responses, poisoned examples form compact clusters (driven by consistent malicious outputs), while clean examples remain dispersed. We leverage this with RFTC—Reference-Filtration + TF-IDF Clustering. RFTC first compares each example’s response with that of a reference model and flags those with large deviations as suspicious; it then performs TF-IDF clustering on the suspicious set and identifies true poisoned examples using intra-class distance. On two machine translation datasets and one QA dataset, RFTC outperforms prior detectors in both detection accuracy and the downstream performance of the fine-tuned models. Ablations with different reference models further validate the effectiveness and robustness of Reference-Filtration.

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Rust-doctor: Enhanced Feature for Rust Ownership and Lifetime Repair with Balanced Training Data Generation
Wenzhang Yang | Xiaoning Ren | Cuifeng Gao | Yinxing Xue

As a relatively new programming language, Rust has gained significant popularity in recent years due to its safety features during compilation. However, Rust developers often face challenges stemming from its strict compilation checks due to the steep learning curve of safety rules. To make matters worse, the lack of training data and the unique semantics of Rust lead to poor performance in learning-based automated program repair techniques. To address these challenges, we propose a novel error injection approach to generate a balanced training dataset and leverage the Mid-level Intermediate Representation (MIR) as enhanced features for Rust’s unique compilation error repair. Using these innovations, we fine-tuned a new code model, LLaRRA: Large Language and Rust Repair Assistant. Experimental results demonstrate that LLaRRA significantly outperforms state-of-the-art models in terms of Pass@K and Acc@K.

In recent months, substantial progress has been made in complex reasoning of Large Language Models (LLMs), particularly through the application of test-time scaling. Notable examples include, though are not limited to, OpenAI’s o1/o3/o4 series and DeepSeek-R1. When responding to a query, these models generate an extended reasoning trajectory, during which the model explores, reflects, backtracks, and self-verifies before arriving at a conclusion. However, fine-tuning models with such reasoning trajectories may not always be optimal. Our findings indicate that not all components within these reasoning trajectories contribute positively to the reasoning process; in fact, some components may affect the overall performance negatively. In this study, we divide a reasoning trajectory into individual subtrajectories and develop a “5+2” framework to: (1) systematically identify suboptimal subtrajectories within the reasoning trajectory based on five human-established criteria; (2) assess the independence of the suboptimal subtrajectories identified in (1) from the subsequent content, ensuring that their elimination does not compromise overall flow and coherence of the reasoning process. Additionally, a sampling algorithm, built upon the “5+2” framework, is employed to select data whose reasoning process is free from suboptimal subtrajectories to the highest degree. Experimental results demonstrate that our method can reduce the number of suboptimal subtrajectories by 25.9% during the inference. Furthermore, our method achieves an average accuracy of 58.92% on highly challenging AIME24, AIME25, AMC24 and MATH500 benchmarks with only two thirds of training data, surpassing the average accuracy of 58.06% achieved with the entire data, and outperforming open-source datasets, including s1K-1.1, Light-R1-SFT-stage-1, OpenR1-Math-94k, and OpenThoughts-114k, when fine-tuning Qwen2.5-Math-7B. Finally, we have validated the efficacy of our method under resource-constrained scenarios, where it exhibits performance improvements across different maximum inference token limits: 2k, 4k, 8k, and 16k tokens.

The capability of in-context learning (ICL) enables large language models (LLMs) to perform novel tasks without parameter updates by conditioning on a few input-output examples. However, collecting high-quality examples for new or challenging tasks can be costly and labor-intensive. In this work, we propose a cost-efficient two-stage pipeline that reduces reliance on LLMs for data labeling. Our approach first leverages readily available cross-task examples to prompt an LLM and pseudo-label a small set of target task instances. We then introduce a graph-based label propagation method that spreads label information to the remaining target examples without additional LLM queries. The resulting fully pseudo-labeled dataset is used to construct in-task demonstrations for ICL. This pipeline combines the flexibility of cross-task supervision with the scalability of LLM-free propagation. Experiments across five tasks demonstrate that our method achieves strong performance while lowering labeling costs.

Evaluations of large language models (LLMs) suffer from instability, where small changes of random factors such as few-shot examples can lead to drastic fluctuations of scores and even model rankings. Moreover, different LLMs can have different preferences for a certain setting of random factors. As a result, using a fixed setting of random factors, which is often adopted as the paradigm of current evaluations, can lead to potential unfair comparisons between LLMs. To mitigate the volatility of evaluations, we first theoretically analyze the sources of variance induced by changes in random factors. Targeting these specific sources, we then propose the instance-level randomization (ILR) method to reduce variance and enhance fairness in model comparisons. Instead of using a fixed setting across the whole benchmark in a single experiment, we randomize all factors that affect evaluation scores for every single instance, run multiple experiments and report the averaged score. Theoretical analyses and empirical results demonstrate that ILR can reduce the variance and unfair comparisons caused by random factors, as well as achieve similar robustness level with less than half computational cost compared with previous methods. Codes and data are available at https://github.com/EricLee8/Instance-level-Randomization.

The advancement of Large Language Models (LLMs) has made ensuring their trustworthiness increasingly critical, especially in terms of fairness across diverse human groups. While modern LLMs are aligned with user preferences through Reinforcement Learning from Human Feedback (RLHF), the reward models used for alignment are trained on preference data that may both reflect societal biases and suffer from demographic skewness, as labeler populations are often uneven due to systemic accessibility or participation gaps. In this work, we reveal that reward models can exhibit significant discrepancies across different demographic groups, posing a fundamental challenge to fair and robust alignment. Using real-world datasets, we conduct the most comprehensive study to date, auditing various state-of-the-art reward models across nine sensitive attributes, including age, gender, ethnicity, etc. Our evaluation spans both (1) the agreement level between reward models and specific user groups, and (2) the reward model’s preference toward responses associated with different groups. Based on these findings, we propose the first method to mitigate group disparities in reward modeling. Code is available at https://github.com/Violet24K/FaRM.

Contrast-enhanced 3D Medical imaging (e.g., CT, MRI) leverages phase sequences to uncover temporal dynamics vital for diagnosing tumors, lesions, and vascular issues. However, current retrieval models primarily focus on spatial features, neglecting phase-specific progression detailed in clinical reports. We present the **Phase-aware Memory Network (PAMN)**, a novel framework enhancing 3D medical image retrieval by fusing imaging phases with diagnostic text. PAMN creates rich radiological representations that enhance diagnostic accuracy by combining image details with clinical report context, rigorously tested on a novel phase-series dataset of 12,230 hospital CT scans. PAMN achieves an effective balance of performance and scalability in 3D radiology retrieval, outperforming state-of-the-art baselines through the robust fusion of spatial, temporal, and textual information.

Large Reasoning Models (LRMs) have exhibited extraordinary prowess in tasks like mathematics and coding, leveraging their advanced reasoning capabilities. Nevertheless, as these capabilities progress, significant concerns regarding their vulnerabilities and safety have arisen, which can pose challenges to their deployment and application in real-world settings. This paper presents the first comprehensive survey of LRMs, meticulously exploring and summarizing the newly emerged safety risks, attacks, and defense strategies specific to these powerful reasoning-enhanced models. By organizing these elements into a detailed taxonomy, this work aims to offer a clear and structured understanding of the current safety landscape of LRMs, facilitating future research and development to enhance the security and reliability of these powerful models.

Large language models (LLMs) have achieved groundbreaking progress in Natural Language Processing (NLP). Despite the numerous advantages of LLMs, they also pose significant safety risks. Self-evaluation mechanisms have gained increasing attention as a key safeguard to ensure safe and controllable content generation. However, LLMs often exhibit overconfidence, which seriously compromises the accuracy of safety self-evaluation. To address this challenge, we propose SafeConf, a method to enhance the safety self-evaluation capability of LLMs through confidence calibration. The method performs semantic mutations on the original safety evaluation questions and adopts a self-consistency strategy to quantify confidence based on answer accuracy on the mutated questions. Finally, these confidence scores are used to construct a dataset for fine-tuning. We conducte experiments on both Chinese and English datasets. The results show that SafeConf improves self-evaluation accuracy by an average of 5.86% and 7.79% over the state-of-the-art baseline methods on Qwen2.5-7B-Instruct and Llama3-8B-Instruct models, respectively, without affecting the general capabilities of the models.

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DocAssistant: Integrating Key-region Reading and Step-wise Reasoning for Robust Document Visual Question Answering
Jinxu Zhang | Qiyuan Fan | Yu Zhang

Understanding the multimodal documents is essential for accurately extracting relevant evidence and using it for reasoning. Existing document understanding models struggle to focus on key information and tend to generate answers straightforwardly, ignoring evidence from source documents and lacking interpretability. In this work, we improve the visual encoder to focus on key information relevant to the question and address the shortcomings of existing document visual question-answering datasets to provide the model with the ability to answer questions step-wise, dubbed DocAssistant. Specifically, for the visual side, we propose an effective vision-language adaptation that fuses text into visual encoders without compromising the performance of the original model. For the language side, we use Multimodal Large Language Models (MLLMs) as data generators and checkers to produce high-quality step-wise question-and-answer pairs for document images. We then use the generated high-quality data to train our enhanced model, specifically designed to solve complex questions that require reasoning or multi-hop question answering. The experimental results demonstrate the effectiveness of the model.

The problem of data contamination is now almost inevitable during the development of large language models (LLMs), with the training data commonly integrating those evaluation benchmarks even unintentionally. This problem subsequently makes it hard to benchmark LLMs fairly. Instead of constructing contamination-free datasets (quite hard), we propose a novel framework, LNE-Blocking, to restore model performance prior to contamination on potentially leaked datasets. Our framework consists of two components: contamination detection and disruption operation. For the prompt, the framework first uses the contamination detection method, LNE, to assess the extent of contamination in the model. Based on this, it adjusts the intensity of the disruption operation, Blocking, to elicit non-memorized responses from the model. Our framework is the first to efficiently restore the model’s greedy decoding performance. This comes with a strong performance on multiple datasets with potential leakage risks, and it consistently achieves stable recovery results across different models and varying levels of data contamination. We release the code at https://github.com/RuijieH/LNE-Blocking to facilitate research.

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Enhancing Hate Speech Classifiers through a Gradient-assisted Counterfactual Text Generation Strategy
Michael Van Supranes | Shaowen Peng | Shoko Wakamiya | Eiji Aramaki

Counterfactual data augmentation (CDA) is a promising strategy for improving hate speech classification, but automating counterfactual text generation remains a challenge. Strong attribute control can distort meaning, while prioritizing semantic preservation may weaken attribute alignment. We propose **Gradient-assisted Energy-based Sampling (GENES)** for counterfactual text generation, which restricts accepted samples to text meeting a minimum BERTScore threshold and applies gradient-assisted proposal generation to improve attribute alignment. Compared to other methods that solely rely on either prompting, gradient-based steering, or energy-based sampling, GENES is more likely to jointly satisfy attribute alignment and semantic preservation under the same base model. When applied to data augmentation, GENES achieved the best macro F1-score in two of three test sets, and it improved robustness in detecting targeted abusive language. In some cases, GENES exceeded the performance of prompt-based methods using a GPT-4o-mini, despite relying on a smaller model (Flan-T5-Large). Based on our cross-dataset evaluation, the average performance of models aided by GENES is the best among those methods that rely on a smaller model (Flan-T5-L). These results position GENES as a possible lightweight and open-source alternative.

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Learning SQL Like a Human: Structure-Aware Curriculum Learning for Text-to-SQL Generation
Xiaohu Zhu | Qian Li | Lizhen Cui | Yuntao Du

The Text-to-SQL capabilities of large language allow users to interact with databases using natural language. While current models struggle with handling complex queries, especially involving multi-table joins and reasoning. To address this gap, we propose to construct a model, namely SAC-SQL, with synthetic training samples followed by a structure-aware curriculum learning framework for enhancing SQL generation. Our approach begins with a supervised fine-tuning (SFT) stage, where we train open-source models on a synthetically constructed, cross-domain SQL dataset with diverse structural patterns. Moreover, we introduce a unified structure difficulty scoring function to partition the training samples into non-overlapping curriculum phases, guiding the model progressively learning from simpler to more complex SQL structures. Extensive experiments are conducted and the results show that SAC-SQL achieves better results than the baselines, and significantly narrows the performance gap between open-source and close-source models on Spider and Bird benchmarks.

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Chain-of-Interactions: Multi-step Iterative ICL Framework for Abstractive Task-Oriented Dialogue Summarization of Conversational AI Interactions
Jason S Lucas | Ali Al Lawati | Mahjabin Nahar | John Chen | Mahnoosh Mehrabani

Large Language Models (LLMs) have introduced paradigm-shifting approaches in natural language processing. Yet, their transformative in-context learning (ICL) capabilities remain underutilized, especially in customer service dialogue summarization—a domain plagued by generative hallucinations, detail omission, and inconsistencies. We present Chain-of-Interactions (CoI), a novel single-instance, multi-step framework that orchestrates information extraction, self-correction, and evaluation through sequential interactive generation chains. By strategically leveraging LLMs’ ICL capabilities through precisely engineered prompts, CoI dramatically enhances abstractive task-oriented dialogue summarization (ATODS) quality and usefulness. Our comprehensive evaluation on real-world and benchmark human-agent interaction datasets demonstrates CoI’s effectiveness through rigorous testing across 11 models and 7 prompting approaches, with 9 standard automatic evaluation metrics, 3 LLM-based evaluations, and human studies involving 480 evaluators across 9 quality dimensions. Results reveal CoI’s decisive superiority, outperforming all single-step approaches and achieving 6× better entity preservation, 49% higher quality scores, and 322% improvement in accuracy compared to state-of-the-art multi-step Chain-of-Density (CoD). This research addresses critical gaps in task-oriented dialogue summarization for customer service applications and establishes new standards for harnessing LLMs’ reasoning capabilities in practical, industry-relevant contexts.

Embedding-as-a-Service (EaaS) has emerged as a successful business pattern but faces significant challenges related to various forms of copyright infringement, particularly the API misuse and model extraction attacks. Various studies have proposed backdoor-based watermarking schemes to protect the copyright of EaaS services. In this paper, we reveal that previous watermarking schemes possess semantic-independent characteristics and propose the Semantic Perturbation Attack (SPA). Our theoretical and experimental analysis demonstrates that this semantic-independent nature makes current watermarking schemes vulnerable to adaptive attacks that exploit semantic perturbation tests to bypass watermark verification. Extensive experimental results across multiple datasets demonstrate that the True Positive Rate (TPR) for identifying watermarked samples under SPA can reach up to more than 95%, rendering watermarks ineffective while maintaining the high utility of the embeddings. In addition, we discuss current potential defense strategies to mitigate SPA. Our code is available at https://github.com/Zk4-ps/EaaS-Embedding-Watermark.

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Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models
Youan Cong | Pritom Saha Akash | Cheng Wang | Kevin Chen-Chuan Chang

We introduce the Extract-Refine-Retrieve-Read (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RAG) systems through query optimization tailored to meet the specific knowledge requirements of Large Language Models (LLMs). Unlike conventional query optimization techniques used in RAG, the ERRR framework begins by extracting parametric knowledge from LLMs, followed by using a specialized query optimizer for refining these queries. This process ensures the retrieval of only the most pertinent information essential for generating accurate responses. Moreover, to enhance flexibility and reduce computational costs, we propose a trainable scheme for our pipeline that utilizes a smaller, tunable model as the query optimizer, which is refined through knowledge distillation from a larger teacher model. Our evaluations on various question-answering (QA) datasets and with different retrieval systems show that ERRR consistently outperforms existing baselines, proving to be a versatile and cost-effective module for improving the utility and accuracy of RAG systems.

Although large language models (LLMs) have made significant progress in understanding Structured Knowledge (SK) like KG and Table, existing evaluations for SK understanding are non-rigorous (i.e., lacking evaluations of specific capabilities) and focus on a single type of SK. Therefore, we aim to propose a more comprehensive and rigorous structured knowledge understanding benchmark to diagnose the shortcomings of LLMs. In this paper, we introduce SKA-Bench, a Structured Knowledge Augmented QA Benchmark that encompasses four widely used structured knowledge forms: KG, Table, KG+Text, and Table+Text. We utilize a three-stage pipeline to construct SKA-Bench instances, which includes a question, an answer, positive knowledge units, and noisy knowledge units. To evaluate the SK understanding capabilities of LLMs in a fine-grained manner, we expand the instances into four fundamental ability testbeds: Noise Robustness, Order Insensitivity, Information Integration, and Negative Rejection. Empirical evaluations on 8 representative LLMs, including the advanced DeepSeek-R1, indicate that existing LLMs still face significant challenges in understanding structured knowledge, and their performance is influenced by factors such as the amount of noise, the order of knowledge units, and hallucination phenomenon. Our dataset and code are available at https://github.com/zjukg/SKA-Bench.

Large Language Models (LLMs), due to substantial computational requirements, are vulnerable to resource consumption attacks, which can severely degrade server performance or even cause crashes, as demonstrated by denial-of-service (DoS) attacks designed for LLMs. However, existing works lack mitigation strategies against such threats, resulting in unresolved security risks for real-world LLM deployments. To this end, we propose the Pluggable and Dynamic DoS-Defense Framework (PD³F), which employs a two-stage approach to defend against resource consumption attacks from both the input and output sides. On the input side, we propose the Resource Index to guide Dynamic Request Polling Scheduling, thereby reducing computing resource usage induced by malicious prompts under high-concurrency scenarios. On the output side, we introduce the Adaptive End-Based Suppression mechanism, which reduces excessive malicious generation. Experiments across six models demonstrate that PD³F significantly mitigates resource consumption attacks, improving users’ access capacity by up to 500% during adversarial load. PD³F represents a step toward the resilient and resource-aware deployment of LLMs against resource consumption attacks.

Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration, where the model follows stochastic and unguided reasoning paths—like walking without a map. This leads to unstable reasoning paths, lack of error correction, and limited learning from past experience. To address these issues, we propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement. First, we extract structured reasoning patterns from successful trajectories and reflective signals from failures. During inference, the model follows these guidelines step-by-step, with refinement applied after each step to correct errors and stabilize the reasoning process. Experiments on the Big-Bench Hard (BBH) benchmark show that our method consistently outperforms strong baselines across diverse reasoning tasks. Analysis reveals that stepwise execution, refinement, and experience-based learning improve stability and generalization. We further explore model collaboration during refinement, offering insights into cross-model interactions. Notably, structured reasoning guided by learned instructions matches or even surpasses knowledge distilled through SFT, highlighting its scalability and effectiveness.

The rapid increase in the parameter counts of Large Language Models (LLMs), which often reach into the billions or even trillions, presents significant challenges for their practical deployment, particularly in resource-constrained environments. To address this issue, we propose PIP (Perturbation-based Iterative Pruning), a novel double-view structured pruning method to optimize LLMs, which combines information from two different views: the unperturbed view and the perturbed view. With the calculation of gradient differences, PIP iteratively prunes those that struggle to distinguish between these two views. Our experiments show that PIP reduces the parameter count by approximately 20% while retaining over 85% of the original model’s accuracy across varied benchmarks. In some cases, the performance of the pruned model is within 5% of the unpruned version, demonstrating PIP’s ability to preserve key aspects of model effectiveness. Moreover, PIP consistently outperforms existing state-of-the-art (SOTA) structured pruning methods, establishing it as a leading technique for optimizing LLMs in constrained environments.

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Convolutional LoRA Aggregation for Unseen Tasks Adaptation
Xinhao Wu | Jialin Liu | Yutai Duan | Jie Liu

Recent studies have increasingly explored the combination of existing LoRA modules for effective adaptation to unseen tasks in data-scarce scenarios. However, current LoRA selection methods typically rely on a few task samples, making it difficult to capture the full scope of task-relevant information. Furthermore, even after selection, a knowledge gap remains between the selected LoRA modules and the target task, which existing coarse-grained LoRA aggregation strategies struggle to bridge. To address these challenges, we propose Selection and Convolution for LoRA aggregation (SC-LoRA), a two-stage framework that first selects appropriate LoRA modules based on parameter clustering and then aggregates them using a convolutional LoRA aggregator. Our LoRA selection strategy ensures comprehensive coverage of task-relevant LoRA modules by leveraging their distance in the parameter space. Building on this, the convolutional LoRA aggregator extracts useful knowledge in a fine-grained manner, seamlessly bridging the gap to the target task. Our experiments demonstrate that SC-LoRA excels in aggregating multiple LoRA modules for effective adaptation to unseen tasks.

Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities, highlighting the need for comprehensive evaluation frameworks that extend beyond task-specific benchmarks.However, existing benchmarks often focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities.To address this gap, we propose the Cognition-Domain-Task (CDT) framework, which comprehensively measures a model’s capabilities across three dimensions.We expand the scope of model capability definitions at the cognitive level by incorporating the Cattell-Horn-Carroll cognitive theory, refining the categorization of model capabilities.We apply CDT in two directions: dataset capability evaluation and data selection. Experiments show that our capability metrics correlate well with downstream performance and can support effective dataset analysis and construction. The experiments on data selection also show significant improvements in both general and specific benchmarks, achieving scores of 44.3 and 45.4, with an increase of 1.6 and 2.2 points over the baselines, respectively. These results validate the effectiveness and practicality of CDT. Source code and models are available at https://github.com/Alessa-mo/CDT.

The robustness and security of Large Language Models (LLMs) face increasing threats, especially in multilingual settings. A notable vulnerability is “jailbreaking” via translating harmful queries into rare or underrepresented languages, which often bypasses existing safeguards. In this work, we propose Multilingual Collaborative Defense (MCD), a novel learning method that optimizes a continuous soft safety prompt automatically to facilitate multilingual safeguarding of LLMs. MCD organically leverages collaborative signals from multiple languages by rotating each as the training “center,” allowing auxiliary languages to reinforce safety prompt learning and ensuring cross‐lingual consistency. As a result, MCD improves defense performance across all languages, reduces false refusals, and mitigates safety misalignment caused by corpus imbalance. To evaluate MCD, we construct multilingual versions of jailbreak benchmarks such as MaliciousInstruct and AdvBench, including zero-shot languages, to assess language transferability. Experiments show that MCD outperforms prior approaches in multilingual jailbreak defense while exhibiting strong cross-lingual generalization. Our code is available at https://github.com/HLiang-Lee/MCD.

Sentiment analysis of historical literature provides valuable insights for humanities research, yet remains challenging due to scarce annotations and limited generalization of models trained on modern texts. Prior work has primarily focused on two directions: using sentiment lexicons or leveraging large language models (LLMs) for annotation. However, lexicons are often unavailable for historical texts due to limited linguistic resources, and LLM-generated labels often reflect modern sentiment norms and fail to capture the implicit, ironic, or morally nuanced expressions typical of historical literature, resulting in noisy supervision. To address these issues, we introduce a role-guided annotation strategy that prompts LLMs to simulate historically situated perspectives when labeling sentiment. Furthermore, we design a prototype-aligned framework that learns sentiment prototypes from high-resource data and aligns them with low-resource representations via symmetric contrastive loss, improving robustness to noisy labels. Experiments across multiple historical literature datasets show that our method outperforms state-of-the-art baselines, demonstrating its effectiveness.

Meta-learning has proven to be a powerful paradigm for effectively improving the performance of low-resource speech recognition by learning generalizable knowledge across multiple tasks. However, multilingual meta learning also faces challenges such as task overfitting and learner overfitting, thereby reducing its ability to generalize to new tasks. To address these issues, we augment the meta-training task with “more data” during both training and evaluation phases. Concretely, we propose an interpolation-based task augmentation method called MetaMixSpeech, which includes both support augmentation and query augmentation. MetaMixSpeech enhances task diversity by linearly combining perturbed features from the support and query sets and performing the same linear interpolation on their corresponding losses. Experimental results on the FLEURS and Common Voice datasets demonstrate that MetaMixSpeech achieves a 6.35 % improvement in Word Error Rate (WER) compared to meta-learning approaches, effectively mitigating the overfitting problem and showcasing superior generalization across diverse datasets and language families.

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RECAST: Retrieval-Augmented Contextual ASR via Decoder-State Keyword Spotting
Ashish Mittal | Sunita Sarawagi | Preethi Jyothi

Contextual biasing in ASR systems is critical for recognizing rare, domain-specific terms but becomes impractical with large keyword dictionaries due to prompt size and latency constraints. We present RECAST–a lightweight retrieval-augmented approach that repurposes decoder states of a pretrained ASR model to retrieve relevant keywords without requiring audio exemplars. RECAST introduces a contrastively trained retriever that aligns decoder-state embeddings with textual keyword representations, enabling fast token-level retrieval over large dictionaries. Retrieved keywords are ranked and formatted into a prompt to guide a downstream speech language model. Trained solely on LibriSpeech and evaluated on out-of-domain benchmarks covering up to 4,000 keywords across diverse domains, RECAST consistently outperforms full-list prompt biasing and strong phonetic/text baselines. It achieves up to 54.3% relative reduction in entity WER and 41.3% overall WER improvement over the baseline, along with up to 2.5x higher recall in challenging settings. Furthermore, RECAST remains effective for diverse languages such as Hindi, demonstrating its scalability, language-agnostic design, and practicality for real-world contextual ASR.

Addressing the intellectual property protection challenges in commercial deployment of large language models (LLMs), existing black-box fingerprinting techniques face dual challenges from incremental fine-tuning erasure and feature-space defense due to their reliance on overfitting high-perplexity trigger patterns. We firstly reveal that, model editing in the fingerprint domain exhibits unique advantages including significantly lower false positive rates, enhanced harmlessness, and superior robustness. Building on this foundation, this paper innovatively proposes a Prefix-enhanced Fingerprint Editing Framework (PREE), which encodes copyright information into parameter offsets through dual-channel knowledge edit to achieve covert embedding of fingerprint features. Experimental results demonstrate that the proposed solution achieves the 90% trigger precision in mainstream architectures including LLaMA-3 and Qwen-2.5. The minimal parameter offset (change rate < 0.03) effectively preserves original knowledge representation while demonstrating strong robustness against incremental fine-tuning and multi-dimensional defense strategies, maintaining zero false positive rate throughout evaluations.

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Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing
Zichen Wu | Hsiu-Yuan Huang | Yunfang Wu

Multimodal Large Language Models (MLLMs) have shown substantial capabilities in integrating visual and textual information, yet frequently rely on spurious correlations, undermining their robustness and generalization in complex multimodal reasoning tasks. This paper addresses the critical challenge of superficial correlation bias in MLLMs through a novel causal mediation-based debiasing framework. Specially, we distinguishing core semantics from spurious textual and visual contexts via counterfactual examples to activate training-stage debiasing and employ a Mixture-of-Experts (MoE) architecture with dynamic routing to selectively engages modality-specific debiasing experts. Empirical evaluation on multimodal sarcasm detection and sentiment analysis tasks demonstrates that our framework significantly surpasses unimodal debiasing strategies and existing state-of-the-art models.

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Text-centric Alignment for Bridging Test-time Unseen Modality
Yun-Da Tsai | Ting-Yu Yen | Pei-Fu Guo | Zhe-Yan Li | Shou-De Lin

This paper addresses the challenge of handling unseen modalities and dynamic modality combinations at test time with our proposed text-centric alignment method. This training-free alignment approach unifies different input modalities into a single semantic text representation by leveraging in-context learning with Large Language Models and uni-modal foundation models. Our method significantly enhances the ability to manage unseen, diverse, and unpredictable modality combinations, making it suitable for both generative and discriminative models to adopt on top. Our extensive experiments primarily evaluate on discriminative tasks, demonstrating that our approach is essential for LLMs to achieve strong modality alignment performance. It also surpasses the limitations of traditional fixed-modality frameworks in embedding representations. This study contributes to the field by offering a flexible and effective solution for real-world applications where modality availability is dynamic and uncertain.

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HierPrompt: Zero-Shot Hierarchical Text Classification with LLM-Enhanced Prototypes
Qian Zhang | Qinliang Su | Wei Zhu | Pang Yachun

Hierarchical Text Classification is a challenging task which classifies texts into categories arranged in a hierarchy. Zero‐Shot Hierarchical Text Classification (ZS-HTC) further assumes only the availability of hierarchical taxonomy, without any training data. Existing works of ZS-HTC are typically built on the prototype-based framework by embedding the category names into prototypes, which, however, do not perform very well due to the ambiguity and impreciseness of category names. In this paper, we propose HierPrompt, a method that leverages hierarchy-aware prompts to instruct LLM to produce more representative and informative prototypes. Specifically, we first introduce Example Text Prototype (ETP), in conjunction with Category Name Prototype (CNP), to enrich the information contained in hierarchical prototypes. A Maximum Similarity Propagation (MSP) technique is also proposed to consider the hierarchy in similarity calculation. Then, the hierarchical prototype refinement module is utilized to (i) contextualize the category names for more accurate CNPs and (ii) produce detailed example texts for each leaf category to form ETPs. Experiments on three benchmark datasets demonstrate that HierPrompt substantially outperforms existing ZS‐HTC methods.

Routing large language models (LLMs) is a new paradigm that uses a router to recommend the best LLM from a pool of candidates for a given input. In this paper, our comprehensive analysis with more than 8,500 LLMs reveals a novel model-level scaling up phenomenon in Routing LLMs, i.e., a capable router can significantly enhance the performance of this paradigm as the number of candidates increases. This improvement can even surpass the performance of the best single model in the pool and many existing strong LLMs, confirming it a highly promising paradigm. However, the lack of comprehensive and open-source benchmarks for Routing LLMs has hindered the development of routers. In this paper, we introduce RouterEval, a benchmark tailored for router research, which includes over 200,000,000 performance records for 12 popular LLM evaluations across various areas such as commonsense reasoning, semantic understanding, etc., based on over 8,500 various LLMs. Using RouterEval, extensive evaluations of existing Routing LLM methods reveal that most still have significant room for improvement.

Large Reason Models (LRMs) extend long reasoning process to solve complex tasks. However, due to the lack of fine-grained control, they often suffer from overthinking and erroneous reasoning problems, risking accuracy loss. To address this issue, we introduce Reasoning Direction Steering (RDS) to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns. We develop a simple yet effective paradigm, Thinking Intervention, which explores two key dimensions - intervention positions and intervention styles - to achieve integration intervention throughout model reasoning processes. To validate the effectiveness of our approach, we conduct comprehensive experiments on multi-hop question answering tasks using state-of-the-art LRMs, including Qwen3-Series and R1-Series models. Experimental results demonstrate the efficacy of Thinking Intervention with 9.4% average improvement on R1-Series models and 1.9% improvement on Qwen3-Series models.

Recent advancements in large language models (LLMs) have enabled LLM-based agents to successfully tackle interactive planning tasks. However, despite their successes, existing approaches often suffer from planning hallucinations and require retraining for each new agent. To address these challenges, we propose the **M**eta **P**lan **O**ptimization (**MPO**) framework, , which enhances agent planning capabilities by directly incorporating explicit guidance. Unlike previous methods that rely on complex knowledge, which either require significant human effort or lack quality assurance, MPO leverages high-level general guidance through meta plans to assist agent planning and enables continuous optimization of the meta plans based on feedback from the agent’s task execution. Our experiments conducted on two representative tasks demonstrate that MPO significantly outperforms existing baselines. Moreover, our analysis indicates that MPO provides a plug-and-play solution that enhances both task completion efficiency and generalization capabilities in previous unseen scenarios.

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Exploring the Generalizability of Factual Hallucination Mitigation via Enhancing Precise Knowledge Utilization
Siyuan Zhang | Yichi Zhang | Yinpeng Dong | Hang Su

Large Language Models (LLMs) often struggle to align their responses with objective facts, resulting in the issue of factual hallucinations, which can be difficult to detect and mislead users without relevant knowledge. Although post-training techniques have been employed to mitigate the issue, existing methods usually suffer from poor generalization and trade-offs in other different capabilities. In this paper, we propose to address these by directly augmenting LLM’s fundamental ability to precisely leverage its knowledge and introduce PKUE (Precise Knowledge Utilization Enhancement), which fine-tunes the model on self-generated responses to precise and simple factual questions through preference optimization. Furthermore, we construct FactualBench, a comprehensive and precise factual QA dataset containing 181k Chinese data spanning 21 domains, to facilitate both evaluation and training. Extensive experiments demonstrate that PKUE significantly improves LLM overall performance, with consistent enhancement across factual tasks of various forms, general tasks beyond factuality, and tasks in different language.

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Learning What to Remember: Adaptive Probabilistic Memory Retention for Memory-Efficient Language Models
S M Rafiuddin | Muntaha Nujat Khan

Transformer attention scales quadratically with sequence length O(n²), limiting long-context use. We propose Adaptive Retention, a probabilistic, layer-wise token selection mechanism that learns which representations to keep under a strict global budget M. Retention is modeled with Bernoulli gates trained via a Hard-Concrete/variational relaxation and enforced with a simple top-M rule at inference, making the method differentiable and drop-in for standard encoders. Across classification, extractive QA, and long-document summarization, keeping only 30–50% of tokens preserves ≥ 95% of full-model performance while cutting peak memory by ∼ 35–45% and improving throughput by up to ∼ 1.8×. This architecture-agnostic approach delivers practical long-context efficiency without modifying base attention or task heads.

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Unlocking Smarter Device Control: Foresighted Planning with a World Model-Driven Code Execution Approach
Xiaoran Yin | Xu Luo | Hao Wu | Lianli Gao | Jingkuan Song

The automatic control of mobile devices is essential for efficiently performing complex tasks that involve multiple sequential steps. However, these tasks pose significant challenges due to the limited environmental information available at each step, primarily through visual observations. As a result, current approaches, which typically rely on reactive policies, focus solely on immediate observations and often lead to suboptimal decision-making. To address this problem, we propose Foresighted Planning with World Model-Driven Code Execution (FPWC),a framework that prioritizes natural language understanding and structured reasoning to enhance the agent’s global understanding of the environment by developing a task-oriented, refinable world model at the outset of the task. Foresighted actions are subsequently generated through iterative planning within this world model, executed in the form of executable code. Extensive experiments conducted in simulated environments and on real mobile devices demonstrate that our method outperforms previous approaches, particularly achieving a 44.4% relative improvement in task success rate compared to the state-of-the-art in the simulated environment.

Medical question answering fundamentally relies on accurate clinical knowledge. The dominant paradigm, Retrieval-Augmented Generation (RAG), acquires expertise conceptual knowledge from large-scale medical corpus to guide general-purpose large language models (LLMs) in generating trustworthy answers. However, existing retrieval approaches often overlook the patient-specific factual knowledge embedded in Electronic Health Records (EHRs), which limits the contextual relevance of retrieved conceptual knowledge and hinders its effectiveness in vital clinical decision-making. This paper introduces RGAR, a recurrence generation-augmented retrieval framework that synergistically retrieves both factual and conceptual knowledge from dual sources (i.e., EHRs and the corpus), allowing mutual refinement through iterative interaction. Across three factual-aware medical QA benchmarks, RGAR establishes new state-of-the-art performance among medical RAG systems. Notably, RGAR enables the Llama-3.1-8B-Instruct model to surpass the considerably larger GPT-3.5 augmented with traditional RAG. Our findings demonstrate the benefit of explicitly mining patient-specific factual knowledge during retrieval, consistently improving generation quality and clinical relevance.

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EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation
Ruobing Yao | Yifei Zhang | Shuang Song | Neng Gao | Chenyang Tu

Retrieval-Augmented Generation (RAG) compensates for the static knowledge limitations of Large Language Models (LLMs) by integrating external knowledge, producing responses with enhanced factual correctness and query-specific contextualization. However, it also introduces new attack surfaces such as corpus poisoning at the same time. Most of the existing defense methods rely on the internal knowledge of the model, which conflicts with the design concept of RAG. To bridge the gap, EcoSafeRAG uses sentence-level processing and bait-guided context diversity detection to identify malicious content by analyzing the context diversity of candidate documents without relying on LLM internal knowledge. Experiments show EcoSafeRAG delivers state-of-the-art security with plug-and-play deployment, simultaneously improving clean-scenario RAG performance while maintaining practical operational costs (relatively 1.2 × latency, 48%-80% token reduction versus Vanilla RAG).

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StereoDetect: Detecting Stereotypes and Anti-stereotypes the Correct Way Using Social Psychological Underpinnings
Kaustubh Shivshankar Shejole | Pushpak Bhattacharyya

Stereotypes are known to have very harmful effects, making their detection critically important. However, current research predominantly focuses on detecting and evaluating stereotypical biases, leaving the study of stereotypes in its early stages. Our study revealed that many works have failed to clearly distinguish between stereotypes and stereotypical biases, which has significantly slowed progress in advancing research in this area. Stereotype and Anti-stereotype detection is a problem that requires social knowledge; hence, it is one of the most difficult areas in Responsible AI. This work investigates this task, where we propose a five-tuple definition and provide precise terminologies disentangling stereotypes, anti‐stereotypes, stereotypical bias, and general bias. We provide a conceptual framework grounded in social psychology for reliable detection. We identify key shortcomings in existing benchmarks for this task of stereotype and anti-stereotype detection. To address these gaps, we developed *StereoDetect*, a well curated, definition‐aligned benchmark dataset designed for this task. We show that language models with fewer than 10 billion parameters frequently misclassify anti‐stereotypes and fail to recognize neutral overgeneralizations. We demonstrate StereoDetect’s effectiveness through multiple qualitative and quantitative comparisons with existing benchmarks and models fine-tuned on them.

Vision-language models (VLMs) excel in many downstream tasks but struggle with spatial reasoning, which is crucial for navigation and interaction with physical environments. Specifically, many spatial reasoning tasks rely on fundamental two-dimensional (2D) capabilities, yet our evaluation shows that state-of-the-art VLMs often produce implausible or incorrect solutions for composite spatial problems, including simple pathfinding tasks that humans solve effortlessly at a glance. To address this, we explore an effective approach to enhance 2D spatial reasoning in VLMs by training them solely on basic spatial capabilities. We first disentangle 2D spatial reasoning into three core components: direction comprehension, distance estimation, and localization. Our central hypothesis is that mastering these basic capabilities will significantly boost performance on more complex spatial tasks requiring advanced reasoning and combinatorial problem-solving, as well as generalize to real-world visual-spatial scenarios. To test this hypothesis, we introduce Sparkle, a framework that generates synthetic data to provide targeted supervision for VLMs across these three basic spatial capabilities, producing an instruction dataset for each capability. Our experiments demonstrate that VLMs fine-tuned with Sparkle achieve substantial improvements, not only on basic tasks but also in generalizing to composite and out-of-distribution real-world spatial reasoning tasks. These findings highlight that enhancing basic spatial capabilities through synthetic generalization effectively improves complex spatial reasoning, offering insights into systematic strategies for boosting VLMs’ spatial understanding. Source codes of Sparkle are available at https://github.com/YihongT/Sparkle.

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How Does Knowledge Selection Help Retrieval Augmented Generation?
Xiangci Li | Jessica Ouyang

Retrieval-augmented generation (RAG) is a powerful method for enhancing natural language generation by integrating external knowledge into a model’s output. While prior work has demonstrated the importance of improving knowledge retrieval for boosting generation quality, the role of knowledge selection, a.k.a. reranking or filtering, remains less clear. This paper empirically analyzes how knowledge selection influences downstream generation performance in RAG systems. By simulating different retrieval and selection conditions through a controlled mixture of gold and distractor knowledge, we assess the impact of these factors on generation outcomes. Our findings indicate that the downstream generator model’s capability, as well as the complexity of the task and dataset, significantly influence the impact of knowledge selection on the overall RAG system performance. In typical scenarios, improving the knowledge recall score is key to enhancing generation outcomes, with the knowledge selector providing limited benefit when a strong generator model is used on clear, well-defined tasks. For weaker generator models or more ambiguous tasks and datasets, the knowledge F1 score becomes a critical factor, and the knowledge selector plays a more prominent role in improving overall performance.

Personality is a crucial factor that shapes human communication patterns, thereby regulating the personalities of large language models (LLMs) holds significant potential in enhancing their user experiences. Previous approaches either relied on fine-tuning LLMs on specific corpora or required manually crafted prompts to evoke specific personalities from LLMs. However, the former is inefficient and costly, while the latter cannot precisely manipulate personality traits at a fine-grained level. To address these challenges, we propose UPLex, a method that uses an Unsupervisedly-Built Personalized Lexicon (UPL) during the decoding phase to manipulate LLM’s personality traits. UPLex can be constructed from a newly built situational judgment test dataset in an unsupervised fashion and used to modulate the personality expression of LLMs by dynamically altering their predicted probability of upcoming words in a pluggable fashion. Extensive experimentation demonstrates the remarkable effectiveness and pluggability of our method for fine-grained manipulation of LLMs’ personalities.

While Retrieval-Augmented Generation systems enhance Large Language Models by incorporating external knowledge, they still face persistent challenges in retrieval inefficiency and the inability of LLMs to filter out irrelevant information. We presentParetoRAG, an unsupervised framework that optimizes RAG systems through sentence-level refinement guided by the Pareto principle. By decomposing paragraphs into sentences and dynamically re-weighting core content while preserving contextual coherence, ParetoRAG achieves dual improvements in retrieval precision and generation quality without requiring additional training or API resources, while using only 40% of the tokens compared to traditional RAG approaches. This framework has been empirically validated across various datasets, LLMs, and retrievers. Furthermore, we show that ParetoRAG’s architectural improvements are orthogonally compatible with adaptive noise-robust models, enabling retrieval-augmented optimization and robust training to enhance generation quality mutually. This highlights complementary architectural refinements and noise mitigation, offering insights for integrating retrieval augmentation with robustness enhancement.

The rapid advancement of large language models (LLMs) has exacerbated the memory bottleneck due to the widening gap between model parameter scaling and hardware capabilities. While post-training quantization techniques effectively reduce memory overhead, existing methods predominantly rely on static quantization strategies, which struggle to adapt to dynamic workloads. To address this, we propose FlexQuant, a dynamic precision-switching framework that optimizes the trade-off between inference speed and accuracy. Leveraging model perplexity entropy and Kullback-Leibler divergence, FlexQuant enables fine-grained, layer-wise mixed-precision quantization and dynamically adjusts bit-widths during each token generation. FlexQuant provides a comprehensive analysis of quantization strategies, introduces a precision requirement model for optimal switching, and implements efficient fine-grained precision management. Evaluations demonstrate that FlexQuant achieves a 1.3× end-to-end speedup across diverse language tasks with negligible accuracy loss introduced. This framework offers a flexible and adaptive solution for efficient LLM deployment.

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ReLoop: “Seeing Twice and Thinking Backwards” via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding
Jianjiang Yang | Yanshu Li | Ziyan Huang

While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in open-ended visual question answering, they remain vulnerable to hallucinations. These are outputs that contradict or misrepresent input semantics, posing a critical challenge to the reliability and factual consistency. Existing methods often rely on external verification or post-hoc correction, lacking an internal mechanism to validate outputs directly during training. To bridge this gap, we propose ReLoop, a unified closed-loop training framework that encourages multimodal consistency for cross-modal understanding in MLLMs. ReLoop adopts a ring-shaped structure that integrates three complementary consistency feedback mechanisms, obliging MLLMs to “seeing twice and thinking backwards”. Specifically, ReLoop employs the frozen Consistency Feedback Plugin (CFP), comprising semantic reconstruction, visual description, and an attention supervision module for attention alignment. These components collectively enforce semantic reversibility, visual consistency, and interpretable attention, enabling the model to correct its outputs during training. Extensive evaluations and analyses demonstrate the effectiveness of ReLoop in reducing hallucination rates across multiple benchmarks, establishing a robust method for hallucination mitigation in MLLMs. We will release our source code and data in the camera-ready version. The code is available at: https://github.com/ZiyanHuang11/Reloop-hallucinations.

Execution failures are common in daily life when individuals perform procedural tasks, such as cooking or handicrafts making. Retrieving relevant procedural documents that align closely with both the content of steps and the overall execution sequence can help correct these failures with fewer modifications. However, existing retrieval methods, which primarily focus on declarative knowledge, often neglect the execution sequence structures inherent in procedural documents. To tackle this challenge, we introduce a new dataset Procedural Questions, and propose a retrieval model Graph-Fusion Procedural Document Retriever (GFPDR) which integrates procedural graphs with document representations. Extensive experiments demonstrate the effectiveness of GFPDR, highlighting its superior performance in procedural document retrieval compared to existing models.

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The Effect of Language Diversity When Fine-Tuning Large Language Models for Translation
David Stap | Christof Monz

Prior research diverges on language diversity in LLM fine-tuning: Some studies report benefits while others find no advantages. Through controlled fine-tuning experiments across 132 translation directions, we systematically resolve these disparities. We find that expanding language diversity during fine-tuning improves translation quality for both unsupervised and—surprisingly—supervised pairs, despite less diverse models being fine-tuned exclusively on these supervised pairs. However, benefits plateau or decrease beyond a certain diversity threshold. We show that increased language diversity creates more language-agnostic representations. These representational adaptations help explain the improved performance in models fine-tuned with greater diversity.

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David vs. Goliath: Cost-Efficient Financial QA via Cascaded Multi-Agent Reasoning
Chenghao Liu | Qian Liu | Ziqin Zhu | Hao Fei | Aniket Mahanti

Large language models (LLMs) have demonstrated remarkable reasoning capabilities, including in financial question answering (FQA). However, the performance in FQA remains limited, particularly in questions that require deep financial knowledge and complex numerical reasoning. While supervised fine-tuning and closed-source LLMs have shown promise, they are often constrained by high costs or computational inefficiency. In this paper, we propose a low-cost yet effective framework, named FinMAN (Financial multi-agent framework), that enables small LLMs (e.g., 8B) to perform complex reasoning tasks without relying on expensive models or task-specific fine-tuning. FinMAN improves formula selection, extraction, and calculation to help small-scale models solve FQA tasks more accurately, with a lightweight verification mechanism to correct common errors. Experimental results show that FinMAN outperforms the best open-source model on BizBench by 10.46% and achieves competitive performance to GPT-3.5 using significantly fewer parameters. Our code and data are publicly available at https://github.com/coenliu/MultiAgentFin.

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Benchmarking Uncertainty Metrics for LLM Target-Aware Search
Pei-Fu Guo | Yun-Da Tsai | Shou-De Lin

LLM search methods, such as Chain of Thought (CoT) and Tree of Thought (ToT), enhance LLM reasoning by exploring multiple reasoning paths. When combined with search algorithms like MCTS and Bandit methods, their effectiveness relies heavily on uncertainty estimation to prioritize paths that align with specific search objectives. However, it remains unclear whether existing LLM uncertainty metrics adequately capture the diverse types of uncertainty required to guide different search objectives. In this work, we introduce a framework for uncertainty benchmarking, identifying four distinct uncertainty types: Answer, Correctness, Aleatoric, and Epistemic Uncertainty. Each type serves different optimization goals in search. Our experiments demonstrate that current metrics often align with only a subset of these uncertainty types, limiting their effectiveness for objective-aligned search in some cases. These findings highlight the need for additional target-aware uncertainty estimators that can adapt to various optimization goals in LLM search.

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ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphs
Francesco Cazzaro | Justin Kleindienst | Sofia Márquez Gomez | Ariadna Quattoni

In recent years, the need for natural language interfaces to knowledge graphs has become increasingly important since they enable easy and efficient access to the information contained in them. In particular, property graphs (PGs) have seen increased adoption as a means of representing complex structured information. Despite their growing popularity in industry, PGs remain relatively underrepresented in semantic parsing research with a lack of resources for evaluation. To address this gap, we introduce ZOGRASCOPE, a benchmark designed specifically for PGs and queries written in Cypher. Our benchmark includes a diverse set of manually annotated queries of varying complexity and is organized into three partitions: iid, compositional and length. We complement this paper with a set of experiments that test the performance of different LLMs in a variety of learning settings.

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FG-PRM: Fine-grained Hallucination Detection and Mitigation in Language Model Mathematical Reasoning
Ruosen Li | Ziming Luo | Xinya Du

Hallucinations in large language models (LLMs) pose significant challenges in tasks requiring complex multi-step reasoning, such as mathematical problem-solving. Existing approaches primarily detect the presence of hallucinations but lack a nuanced understanding of their types and manifestations. In this paper, we first introduce a comprehensive taxonomy that categorizes the common hallucinations in mathematical reasoning tasks into six types. We then propose FG-PRM (Fine-Grained Process Reward Model), an augmented model designed to detect and mitigate hallucinations in a fine-grained, step-level manner. To address the limitations of manually labeling training data, we propose an automated method for generating fine-grained hallucination data using LLMs. Our FG-PRM demonstrates superior performance across two key tasks: 1) Fine-grained hallucination detection: classifying hallucination types for each reasoning step; and 2) Verification: ranking multiple LLM-generated outputs to select the most accurate solution. Our experiments show that FG-PRM excels in fine-grained hallucination detection and substantially boosts the performance of LLMs on GSM8K and MATH benchmarks. These results highlight the benefits of fine-grained supervision in enhancing the reliability and interpretability of LLM reasoning processes. Codes and datasets are available at: https://github.com/du-nlp-lab/FG-PRM.

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Recipe2Plan: Evaluating Planning Abilities of LLMs for Efficient and Feasible Multitasking with Time Constraints Between Actions
Zirui Wu | Xiao Liu | Jiayi Li | Lingpeng Kong | Yansong Feng

While Large Language Model-based agents have demonstrated substantial progress in task completion, existing evaluation benchmarks tend to overemphasize single-task performance, with insufficient attention given to the crucial aspects of multitask planning and execution efficiency required in real-world scenarios. To bridge this gap, we present Recipe2Plan, a novel benchmark framework based on real-world cooking scenarios. Unlike conventional benchmarks, Recipe2Plan challenges agents to optimize cooking time through parallel task execution while respecting temporal constraints i.e. specific actions need to be performed within a particular timeintervals following the preceding steps.Overly aggressive local parallelization may disrupt this constraint, potentially compromising the entire cooking process.This strict time constraint between actions raises a unique challenge for agents to balance between maximizing concurrent operations and adhering to critical timing constraints. Extensive experiments with state-of-the-art models reveal challenges in maintaining this balance between efficiency and feasibility. The results highlight the need for improved temporal awareness and global multitasking capabilities in large language models. We open-source our benchmark and code at https://github.com/WilliamZR/Recipe2Plan.

With the rapid development of large language models (LLMs), protecting intellectual property (IP) has become increasingly crucial. To tackle high costs and potential contamination in fingerprint integration, we propose LoRA-FP, a lightweight plug-and-play framework that encodes backdoor fingerprints into LoRA adapters via constrained fine-tuning. This enables seamless fingerprint transplantation through parameter fusion, eliminating full-parameter updates while maintaining integrity. Experiments demonstrate that LoRA-FP achieves superior robustness against various scenarios like incremental training and model fusion, while significantly reducing computational overhead compared to traditional approaches.

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AELC: Adaptive Entity Linking with LLM-Driven Contextualization
Fang Wang | Zhengwei Tao | Ming Wang | Minghao Hu | Xiaoying Bai

Entity linking (EL) focuses on accurately associating ambiguous mentions in text with corresponding entities in a knowledge graph. Traditional methods mainly rely on fine-tuning or training on specific datasets. However, they suffer from insufficient semantic comprehension, high training costs, and poor scalability. Large Language Models (LLMs) offer promising solutions for EL, but face key challenges: weak simple-prompt performance, costly fine-tuning, and limited recall and precision due to the lack of LLMs use in candidate generation. Building on this, we introduce a novel framework: **A**daptive **E**ntity **L**inking with LLM-Driven **C**ontextualization. AELC, for the first time, introduces the combination of high-density key information condensation prompt and tool-invocation strategy, using a unified format semantic filtering strategy and an adaptive iterative retrieval mechanism to dynamically optimize the candidate set, significantly enhancing both precision and coverage. Furthermore, we innovatively reformulate the EL task as a multiple-choice problem, enabling multi-round reasoning to substantially improve the model’s discriminative capability and robustness. Experiments on four public benchmark datasets demonstrate that AELC achieves state-of-the-art performance. Further ablation studies validate the effectiveness of each module.

Large Language Models (LLMs) have demonstrated promising capabilities in solving mathematical reasoning tasks, leveraging Chain-of-Thought (CoT) data as a vital component in guiding answer generation. Current paradigms typically generate CoT and answers directly for a given problem, diverging from human problem-solving strategies to some extent. Humans often solve problems by recalling analogous cases and leveraging their solutions to reason about the current task. Inspired by this cognitive process, we propose MetaLadder, a novel framework that explicitly prompts LLMs to recall and reflect on meta-problems, those structurally or semantically analogical problems, alongside their CoT solutions before addressing the target problem. Additionally, we introduce a problem-restating mechanism to enhance the model’s comprehension of the target problem by regenerating the original question, which further improves reasoning accuracy. Therefore, the model can achieve reasoning transfer from analogical problems, mimicking human-like “learning from examples” and generalization abilities. Extensive experiments on mathematical benchmarks demonstrate that our MetaLadder significantly boosts LLMs’ problem-solving accuracy, largely outperforming standard CoT-based methods (10.3% accuracy gain) and other methods.

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GLProtein: Global-and-Local Structure Aware Protein Representation Learning
Yunqing Liu | Wenqi Fan | Xiaoyong Wei | Li Qing

Proteins are central to biological systems, participating as building blocks across all forms of life. Despite advancements in understanding protein functions through protein sequence analysis, there remains potential for further exploration in integrating protein structural information. We argue that the structural information of proteins is not only limited to their 3D information but also encompasses information from amino acid molecules (local information) to protein-protein structure similarity (global information). To address this, we propose GLProtein, the first framework in protein pre-training that incorporates both global structural similarity and local amino acid details to enhance prediction accuracy and functional insights. GLProtein innovatively combines protein-masked modelling with triplet structure similarity scoring, protein 3D distance encoding and substructure-based amino acid molecule encoding. Experimental results demonstrate that GLProtein outperforms previous methods in several bioinformatics tasks, including predicting protein-protein interactions, contact prediction, and so on.

In-context learning (ICL) performance heavily relies on the quality and ordering of demonstrations. Iterative selection (IS) is a promising approach to address this issue, but existing IS methods face two key challenges: the oversimplification of process reward signals that guide intermediate steps (often using single-dimensional metrics) and the lack of outcome reward signals that directly optimize final-task accuracy (relying solely on binary terminal feedback like correct/incorrect predictions). To address these issues, we propose a reinforcement learning method R-Mix which models iterative demonstration selection as a Markov Decision Process (MDP), crafting hybrid reward signals — combining outcome-based accuracy signals (i.e., outcome rewards) with process-oriented signals (i.e, process rewards) like stepwise influence and label entropy improvement. Our analysis reveals a positive but trade-off relationship between outcome rewards and process rewards, underscoring the importance of both components for effective policy optimization. We further introduce a dual-head policy architecture that explicitly decouples input-semantic relevance and label-content compatibility. Experiments across NLP benchmarks demonstrate superior performance over state-of-the-art methods, with ablation studies validating the necessity of both reward components and architectural disentanglement. Our work has deeply explored the effective potential of ICL through demonstration selection.

Materials characterization is fundamental to acquiring materials information, revealing the processing-microstructure-property relationships that guide material design and optimization. While multimodal large language models (MLLMs) have recently shown promise in generative and predictive tasks within materials science, their capacity to understand real-world characterization imaging data remains underexplored. To bridge this gap, we present MatCha, the first benchmark for materials characterization image understanding, comprising 1,500 questions that demand expert-level domain expertise. MatCha encompasses four key stages of materials research comprising 21 distinct tasks, each designed to reflect authentic challenges faced by materials scientists. Our evaluation of state-of-the-art MLLMs on MatCha reveals a significant performance gap compared to human experts. These models exhibit degradation when addressing questions requiring higher-level expertise and sophisticated visual perception. Simple few-shot and chain-of-thought prompting struggle to alleviate these limitations. These findings highlight that existing MLLMs still exhibit limited adaptability to real-world materials characterization scenarios. We hope MatCha will facilitate future research in areas such as new material discovery and autonomous scientific agents. MatCha is available at https://github.com/FreedomIntelligence/MatCha.

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GRADE: Generating multi-hop QA and fine-gRAined Difficulty matrix for RAG Evaluation
Jeongsoo Lee | Daeyong Kwon | Kyohoon Jin

Retrieval-Augmented Generation (RAG) systems are widely adopted in knowledge-intensive NLP tasks, but current evaluations often overlook the structural complexity and multi-step reasoning required in real-world scenarios. These benchmarks overlook key factors such as the interaction between retrieval difficulty and reasoning depth. To address this gap, we propose GRADE, a novel evaluation framework that models task difficulty along two orthogonal dimensions: (1) reasoning depth, defined by the number of inference steps (hops), and (2) semantic distance between the query and its supporting evidence. We construct a synthetic multi-hop QA dataset from factual news articles by extracting knowledge graphs and augmenting them through semantic clustering to recover missing links, allowing us to generate diverse and difficulty-controlled queries. Central to our framework is a 2D difficulty matrix that combines generator-side and retriever-side difficulty. Experiments across multiple domains and models show that error rates strongly correlate with our difficulty measures, validating their diagnostic utility. GRADE enables fine-grained analysis of RAG performance and provides a scalable foundation for evaluating and improving multi-hop reasoning in real-world applications.

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FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction
Zhaohan Meng | Zaiqiao Meng | Ke Yuan | Iadh Ounis

Predicting drug-target interaction (DTI) is critical in the drug discovery process. Despite remarkable advances in recent DTI models through the integration of representations from diverse drug and target encoders, such models often struggle to capture the fine-grained interactions between drugs and protein, i.e. the binding of specific drug atoms (or substructures) and key amino acids of proteins, which is crucial for understanding the binding mechanisms and optimising drug design. To address this issue, this paper introduces a novel model, called FusionDTI, which uses a token-level **Fusion** module to effectively learn fine-grained information for **D**rug-**T**arget **I**nteraction. In particular, our FusionDTI model uses the SELFIES representation of drugs to mitigate sequence fragment invalidation and incorporates the structure-aware (SA) vocabulary of target proteins to address the limitation of amino acid sequences in structural information, additionally leveraging pre-trained language models extensively trained on large-scale biomedical datasets as encoders to capture the complex information of drugs and targets. Experiments on three well-known benchmark datasets show that our proposed FusionDTI model achieves the best performance in DTI prediction compared with eight existing state-of-the-art baselines. Furthermore, our case study indicates that FusionDTI could highlight the potential binding sites, enhancing the explainability of the DTI prediction.

The alignment of large language models (LLMs) aims to ensure their outputs adhere to human values, ethical standards, and legal norms. Traditional alignment methods often rely on resource-intensive fine-tuning (FT), which may suffer from knowledge degradation and face challenges in scenarios where the model accessibility or computational resources are constrained. In contrast, training-free (TF) alignment techniques—leveraging in-context learning, decoding-time adjustments, and post-generation corrections—offer a promising alternative by enabling alignment without heavily retraining LLMs, making them adaptable to both open-source and closed-source environments. This paper presents the first systematic review of TF alignment methods, categorizing them by stages of **pre-decoding**, **in-decoding**, and **post-decoding**. For each stage, we provide a detailed examination from the viewpoint of LLMs and multimodal LLMs (MLLMs), highlighting their mechanisms and limitations. Furthermore, we identify key challenges and future directions, paving the way for more inclusive and effective TF alignment techniques. By synthesizing and organizing the rapidly growing body of research, this survey offers a guidance for practitioners and advances the development of safer and more reliable LLMs.

While Vision-Language Models (VLMs) have achieved competitive performance in various tasks, their comprehension of the underlying structure and semantics of a scene remains understudied. To investigate the understanding of VLMs, we study their capability regarding object properties and relations in a controlled and interpretable manner. To this scope, we introduce CIVET, a novel and extensible framework for systemati**C** evaluat**I**on **V**ia controll**E**d s**T**imuli. CIVET addresses the lack of standardized systematic evaluation for assessing VLMs’ understanding, enabling researchers to test hypotheses with statistical rigor. With CIVET, we evaluate five state-of-the-art VLMs on exhaustive sets of stimuli, free from annotation noise, dataset-specific biases, and uncontrolled scene complexity. Our findings reveal that 1) current VLMs can accurately recognize only a limited set of basic object properties; 2) their performance heavily depends on the position of the object in the scene; 3) they struggle to understand basic relations among objects. Furthermore, a comparative evaluation with human annotators reveals that VLMs still fall short of achieving human-level accuracy.

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How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations
Yoshiki Takenami | Yin Jou Huang | Yugo Murawaki | Chenhui Chu

Cognitive biases, well studied in humans, can also be observed in LLMs, affecting their reliability in real-world applications. This paper investigates the anchoring effect in LLM-driven price negotiations. To this end, we instructed seller LLM agents to apply the anchoring effect and evaluated negotiations using not only an objective metric but also a subjective metric. Experimental results show that LLMs are influenced by the anchoring effect like humans. Additionally, we investigated the relationship between the anchoring effect and factors such as reasoning and personality. It was shown that reasoning models are less prone to the anchoring effect, suggesting that the long chain of thought mitigates the effect. However, we found no significant correlation between personality traits and susceptibility to the anchoring effect. These findings contribute to a deeper understanding of cognitive biases in LLMs and to the realization of safe and responsible application of LLMs in society.

End-to-end speech-to-speech (S2S) dialogue systems have recently garnered increasing research attention for their lower latency and more natural integration of nonverbal cues such as emotion and speaker identity. However, these systems face key challenges, particularly in incorporating external knowledge, a capability commonly addressed by Retrieval-Augmented Generation (RAG) in text-based large language models (LLMs). The core difficulty lies in the modality gap between input speech and retrieved textual knowledge, which hinders effective integration of information. To address this issue, we propose a novel end-to-end RAG framework that directly retrieves relevant textual knowledge from speech queries. Experimental results demonstrate that our method significantly improves the performance of end-to-end S2S dialogue systems while achieving higher retrieval efficiency. Although the overall performance still lags behind the SOTA cascaded models, our framework offers a promising direction for enhancing knowledge integration in end-to-end S2S systems. Our code and dataset are released.

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Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods
Yulin Chen | Haoran Li | Yuan Sui | Yangqiu Song | Bryan Hooi

With the development of technology, large language models (LLMs) have dominated the downstream natural language processing (NLP) tasks. However, because of the LLMs’ instruction-following abilities and inability to distinguish the instructions in the data content, such as web pages from search engines, the LLMs are vulnerable to prompt injection attacks. These attacks trick the LLMs into deviating from the original input instruction and executing the attackers’ target instruction. Recently, various instruction hierarchy defense strategies are proposed to effectively defend against prompt injection attacks via fine-tuning.In this paper, we explore more vicious attacks that nullify the prompt injection defense methods, even the instruction hierarchy: backdoor-powered prompt injection attacks, where the attackers utilize the backdoor attack for prompt injection attack purposes. Specifically, the attackers poison the supervised fine-tuning samples and insert the backdoor into the model. Once the trigger is activated, the backdoored model executes the injected instruction surrounded by the trigger. We construct a benchmark for comprehensive evaluation. Our experiments demonstrate that backdoor-powered prompt injection attacks are more harmful than previous prompt injection attacks, nullifying existing prompt injection defense methods, even the instruction hierarchy techniques.

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Path-enhanced Pre-trained Language Model for Knowledge Graph Completion
Hao Wang | Dandan Song | Zhijing Wu | Yuhang Tian | Pan Yang

Pre-trained language models (PLMs) have achieved remarkable knowledge graph completion(KGC) success. However, most methods derive KGC results mainly from triple-level and text-described learning, which lack the capability to capture long-term relational and structural information. Moreover, the absence of a visible reasoning process leads to poor interpretability and credibility of the completions. In this paper, we propose a path-enhanced pre-trained language model-based knowledge graph completion method (PEKGC), which employs multi-view generation to infer missing facts in triple-level and path-level simultaneously to address lacking long-term relational information and interpretability issues. Furthermore, a neighbor selector module is proposed to filter neighbor triples to provide the adjacent structural information. Besides, we propose a fact-level re-evaluation and a heuristic fusion ranking strategy for candidate answers to fuse multi-view predictions. Extensive experiments on the benchmark datasets demonstrate that our model significantly improves the performance of the KGC task.

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Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective
Zhihao Zhang | Sophia Yat Mei Lee | Dong Zhang | Shoushan Li | Guodong Zhou

Cross-lingual Named Entity Recognition (CL-NER) aims to transfer knowledge from high-resource languages to low-resource languages. However, existing zero-shot CL-NER (ZCL-NER) approaches primarily focus on Latin script language (LSL), where shared linguistic features facilitate effective knowledge transfer. In contrast, for non-Latin script language (NSL), such as Chinese and Japanese, performance often degrades due to deep structural differences. To address these challenges, we propose an entity-aligned translation (EAT) approach. Leveraging large language models (LLMs), EAT employs a dual-translation strategy to align entities between NSL and English. In addition, we fine-tune LLMs using multilingual Wikipedia data to enhance the entity alignment from source to target languages.

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Zero-Shot Cross-Domain Aspect-Based Sentiment Analysis via Domain-Contextualized Chain-of-Thought Reasoning
Chuming Shen | Wei Wei | Dong Wang | Zhong-Hao Wang

Cross-domain aspect-based sentiment analysis (ABSA) aims at learning specific knowledge from a source domain to perform various ABSA tasks on a target domain. Recent works mainly focus on how to use domain adaptation techniques to transfer the domain-agnostic features from the labeled source domain to the unlabeled target domain. However, it would be unwise to manually collect a large number of unlabeled data from the target domain, where such data may not be available owing to the facts like data security concerns in banking or insurance. To alleviate this issue, we propose ZeroABSA, a unified zero-shot learning framework for cross-domain ABSA that effectively eliminates dependency on target-domain annotations. Specifically, ZeroABSA consists of two novel components, namely, (1) A hybrid data augmentation module leverages large language models (LLMs) to synthesize high-quality, domain-adaptive target-domain data, by evaluating the generated samples across vocabulary richness, semantic coherence and sentiment/domain consistency, followed by iterative refinement; (2) A domain-aware chain-of-thought (COT) prompting strategy trains models on augmented data while explicitly modeling domain-invariant reasoning to bridge the well-known cross-domain gap. Extensive evaluations across four diverse domains demonstrate that ZeroABSA surpasses the-state-of-the-arts, which effectively advances the practicality of cross-domain ABSA in real-world scenarios where labeled target-domain data is unavailable.

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Tree of Agents: Improving Long-Context Capabilities of Large Language Models through Multi-Perspective Reasoning
Song Yu | Xiaofei Xu | Ke Deng | Li Li | Lin Tian

Large language models (LLMs) face persistent challenges when handling long-context tasks, most notably the “lost in the middle” issue, where information located in the middle of a long input tends to be underutilized. Some existing methods that reduce input have the risk of discarding key information, while others that extend context windows often lead to attention dispersion. To address these limitations, we propose Tree of Agents (TOA), a multi-agent reasoning framework that segments the input into chunks processed by independent agents. Each agent generates its local cognition, then agents dynamically exchange information for collaborative reasoning along tree-structured paths. TOA enables agents to probe different reasoning orders for multi-perspective understanding, effectively mitigating position bias and reducing hallucinations. To improve processing efficiency, we incorporate prefix-hash caching and adaptive pruning strategies, achieving significant performance improvements with comparable API overhead. Experiments show that TOA, powered by compact LLaMA3.1-8B, significantly outperforms multiple baselines and demonstrates comparable performance to the latest and much larger commercial models, such as Gemini1.5-pro, on various long-context tasks. Code is available at https://github.com/Aireduce952/Tree-of-Agents.

Large language models (LLMs) often reflect Western-centric biases, limiting their effectiveness in diverse cultural contexts. Although some work has explored cultural alignment, the potential for cross-cultural transfer, using alignment in one culture to improve performance in others, remains underexplored. This paper investigates cross-cultural transfer of commonsense reasoning within the Arab world, where linguistic and historical similarities coexist with local cultural differences. Using a culturally grounded commonsense reasoning dataset covering 13 Arab countries, we evaluate lightweight alignment methods such as in-context learning (ICL) and demonstration-based reinforcement (DITTO), alongside baselines like supervised fine-tuning (SFT) and direct preference Optimization (DPO). Our results show that merely 12 culture-specific examples from one country can improve performance in others by 10% on average, within multilingual models. In addition, we demonstrate that out-of-culture demonstrations from Indonesia and US contexts can match or surpass in-culture alignment for MCQ reasoning, highlighting cultural commonsense transferability beyond Arab world. These findings demonstrate that efficient cross-cultural alignment is possible and offer a promising approach to adapt LLMs to low-resource cultural settings.

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Enhancing Partially Relevant Video Retrieval with Robust Alignment Learning
Long Zhang | Peipei Song | Jianfeng Dong | Kun Li | Xun Yang

Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos partially relevant to a given query. The core challenge lies in learning robust query-video alignment against spurious semantic correlations arising from inherent data uncertainty: 1) query ambiguity, where the query incompletely characterizes the target video and often contains uninformative tokens, and 2) partial video relevance, where abundant query-irrelevant segments introduce contextual noise in cross-modal alignment. Existing methods often focus on enhancing multi-scale clip representations and retrieving the most relevant clip. However, the inherent data uncertainty in PRVR renders them vulnerable to distractor videos with spurious similarities, leading to suboptimal performance. To fill this research gap, we propose Robust Alignment Learning (RAL) framework, which explicitly models the uncertainty in data. Key innovations include: 1) we pioneer probabilistic modeling for PRVR by encoding videos and queries as multivariate Gaussian distributions. This not only quantifies data uncertainty but also enables proxy-level matching to capture the variability in cross-modal correspondences; 2) we consider the heterogeneous informativeness of query words and introduce learnable confidence gates to dynamically weight similarity. As a plug-and-play solution, RAL can be seamlessly integrated into the existing architectures. Extensive experiments across diverse retrieval backbones demonstrate its effectiveness.

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Multi-level Diagnosis and Evaluation for Robust Tabular Feature Engineering with Large Language Models
Yebin Lim | Susik Yoon

Recent advancements in large language models (LLMs) have shown promise in feature engineering for tabular data, but concerns about their reliability persist, especially due to variability in generated outputs. We introduce a multi-level diagnosis and evaluation framework to assess the robustness of LLMs in feature engineering across diverse domains, focusing on the three main factors: key variables, relationships, and decision boundary values for predicting target classes. We demonstrate that the robustness of LLMs varies significantly over different datasets, and that high-quality LLM-generated features can improve few-shot prediction performance by up to 10.52%. This work opens a new direction for assessing and enhancing the reliability of LLM-driven feature engineering in various domains.

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Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge Reasoning
Jianing Wang | Jin Jiang | Yang Liu | Mengdi Zhang | Xunliang Cai

In this paper, we introduce a new process prejudge strategy in LLM reasoning to demonstrate that bootstrapping with process prejudge allows the LLM to adaptively anticipate the errors encountered when advancing the subsequent reasoning steps, similar to people sometimes pausing to think about what mistakes may occur and how to avoid them, rather than relying solely on trial and error. Specifically, we define a prejudge node in the rationale, which represents a reasoning step, with at least one step that follows the prejudge node that has no paths toward the correct answer. To synthesize the prejudge reasoning process, we present an automated reasoning framework with a dynamic tree-searching strategy. This framework requires only one LLM to perform answer judging, response critiquing, prejudge generation, and thought completion. Furthermore, we develop a two-phase training mechanism with supervised fine-tuning (SFT) and reinforcement learning (RL) to further enhance the reasoning capabilities of LLMs. Experimental results from competition-level complex reasoning demonstrate that our method can teach the model to prejudge before thinking and significantly enhance the reasoning ability of LLMs .

With the development of large language models, fine-tuning has emerged as an effective method to enhance performance in specific scenarios by injecting domain-specific knowledge. In this context, model merging techniques provide a solution for fusing knowledge from multiple fine-tuning models by combining their parameters. However, traditional methods often encounter task interference when merging full fine-tuning models, and this problem becomes even more evident in parameter-efficient fine-tuning scenarios. In this paper, we introduce an improvement to the RegMean method, which indirectly leverages the training data to approximate the outputs of the linear layers before and after merging. We propose an adaptive merging method called FroM, which directly measures the model parameters using the Frobenius norm, without any training data. By introducing an additional hyperparameter for control, FroM outperforms baseline methods across various fine-tuning scenarios, alleviating the task interference problem.

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Dynamic Simulation Framework for Disinformation Dissemination and Correction With Social Bots
Boyu Qiao | Kun Li | Wei Zhou | Songlin Hu

In the “human-bot symbiotic” information ecosystem, social bots play key roles in spreading and correcting disinformation. Understanding their influence is essential for risk control and better governance. However, current studies often rely on simplistic user and network modeling, overlook the dynamic behavior of bots, and lack quantitative evaluation of correction strategies. To fill these gaps, we propose MADD, a Multi-Agent-based framework for Disinformation Dissemination. MADD constructs a more realistic propagation network by integrating the Barabási–Albert Model for scale-free topology and the Stochastic Block Model for community structures, while designing node attributes based on real-world user data. Furthermore, MADD incorporates both malicious and legitimate bots, with their controlled dynamic participation allows for quantitative analysis of correction strategies. We evaluate MADD using individual and group-level metrics. We experimentally verify the real-world consistency of MADD’s user attributes and network structure, and we simulate the dissemination of six disinformation topics, demonstrating the differential effects of fact-based and narrative-based correction strategies. Our code is publicly available at https://github.com/QQQQQQBY/BotInfluence.

Many studies focus on data annotation techniques for training effective PRMs. However, current methods encounter a significant issue when applied to long CoT reasoning processes: they tend to focus solely on the first incorrect step and all preceding steps, assuming that all subsequent steps are incorrect. These methods overlook the unique self-correction and reflection mechanisms inherent in long CoT, where correct reasoning steps may still occur after initial reasoning mistakes. To address this issue, we propose a novel data annotation method for PRMs specifically designed to score the long CoT reasoning process. Given that under the reflection pattern, correct and incorrect steps often alternate, we introduce the concepts of Error Propagation and Error Cessation, enhancing PRMs’ ability to identify both effective self-correction behaviors and reasoning based on erroneous steps. Leveraging an LLM-based judger for annotation, we collect 1.7 million data samples to train a 7B PRM and evaluate it at both solution and step levels. Experimental results demonstrate that compared to existing open-source PRMs and PRMs trained on open-source datasets, our PRM achieves superior performance across various metrics, including search guidance, BoN, and F1 scores. Compared to widely used MC-based annotation methods, our annotation approach not only achieves higher data efficiency but also delivers superior performance. Detailed analysis is also conducted to demonstrate the stability and generalizability of our method.

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PrAd: Prompt Adaptive Tuning for Decoder-only Language Models
Youneng Ma | Junyi He | Haojun Fei

Fine tuning pretrained language models for downstream NLP tasks, while effective, can be costly when the model size and the number of tasks increase, as it requires full parameter updates and a separate model served for each task. Parameter-efficient tuning (PET) addresses the issue by keeping the pretrained parameters fixed while introducing minimal task-specific parameters. There are two essential PET paradigms: prompt-based tuning and adapter-based tuning, each with distinct limitations. Prompt-based methods suffer from increased input lengths and sensitivity to weight initialization, whereas adapter approaches can substantially increase inference time. To overcome these limitations, we propose prompt adaptive tuning (PrAd), a general prompt-based tuning framework for decode-only models that delivers strong performance with high efficiency, even in multi-task scenarios. Unlike conventional prompt-based tuning which uses soft tokens to “wrap” inputs, PrAd employs adapters for flexible input transformation. While traditional adapter-based tuning adapts both the prompt and decoded tokens, PrAd only adapts the prompt. PrAd enables the creation of diverse prompt-based approaches while providing critical advantages for real-world use: (1) it can maintain original input lengths with easy initialization during training, like adapter-based methods; (2) it can reduce management costs while facilitating deployment and efficient batch inference of different tasks, like prompt-based tuning.; and (3) it introduces no additional inference latency in the decoding phase even when serving multiple tasks concurrently. Experiments on six diverse tasks demonstrate that PrAd can consistently attain comparable or better performance and higher inference efficiency.

Large language models (LLMs) offer a novel and convenient avenue for humans to acquire knowledge. However, LLMs are prone to providing “midguy” answers regardless of users’ knowledge background, thereby failing to meet each user’s personalized needs. To tackle the problem, we propose to generate personalized answers with LLMs based on users’ past question-answering records. We dynamically generate and update a user’s domain and global profiles as the user asks questions, and use the latest profile as the context to generate the answer for a newly-asked question. To save tokens, we propose to compress the domain profile into a set of keywords and use the keywords to prompt LLMs. We theoretically analyze the effectiveness of the compression strategy. Experimental results show that our method can generate more personalized answers than comparative methods. The code and dataset are available at https://github.com/DaSESmartEdu/PQA.

World models have been widely utilized in robotics, gaming, and autonomous driving. However, their applications to natural language tasks are relatively limited. In this paper, we construct the dialogue world model, which could predict future utterances and user beliefs, including emotion, sentiment, and intention. In this paper, we propose a framework called DreamCUB, which shows that this user belief modeling and the entire dialogue world model can be established by LLM post-training. By defining a POMDP, we apply model-based reinforcement learning to the dialogue system and solve it by maximizing the information bottleneck. Experiments show that the pretrained dialogue world model can achieve state-of-the-art performances on emotion classification and sentiment identification, while dialogue quality is also enhanced by joint training of policy, critic and dialogue world model. Further analysis reveals that DreamCUB holds a reasonable exploration-exploitation balance and also transfers well to out-of-domain scenarios such as empathetic dialogues.

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FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter
JunXi Wang | Yaxiong Wang | Lechao Cheng | Zhun Zhong

We present FakeSV-VLM in this paper, a new VLM-based framework for detecting fake news on short video platforms. Despite significant efforts to combat this issue due to the severe threat that fake news videos pose to public information security, existing methods still fall short in detection accuracy, often due to lack of knowledge to verify the news is real or not. However, large Vision Language Models (VLMs) have absorbed extensive real-world knowledge from massive multimodal datasets. Motivated by this, we adapt advanced VLMs for fake news detection in short videos. Upon close examination of news samples, we observe that short video samples can be categorized into four distinct scenarios: both video and text are real (for real samples), or both are fake, or either the video or text is fake (for fake samples). Inspired by this insight, we design four experts tailored to handle each scenario and integrate them into VLM via Mixture of Experts. Specifically, we develop the Progressive MoE Adapter (PMOE) module where detection experts first provide an initial analysis, followed by attribution experts for a comprehensive diagnosis, leading to a robust decision. Additionally, we also note the fake news videos often show inconsistency between two modalities. Consequently, we further design the Alignment-driven Event Checking (ADEC) module, which perceives the fake news by capturing the inconsistency between different modalities. Extensive experiments on two benchmark datasets, FakeSV and FakeTT, verify the superiority of our model. It significantly outperforms current state-of-the-art models by +3.32% and +5.02%, establishing a new benchmark in the field.

Large Language Models (LLMs) exhibit strong reasoning capabilities and are widely applied in event forecasting. However, studies have demonstrated that LLMs exhibit human-like cognitive biases, systematic patterns of deviation from rationality in decision-making. To explore the cognitive biases in event forecasting, we introduce CogForecast, a human-curated dataset comprising six topics. Experimental results on three LLMs reveal significant cognitive biases in LLM-based event forecasting methods. To address this issue, we propose MCA, a Multi-Cognition Agentic framework. Specifically, MCA leverages LLMs to act as multi-cognition event participants, performing perspective-taking based on the cognitive patterns of event participants to alleviate the inherent cognitive biases in LLMs and offer diverse analytical perspectives. Then, MCA clusters agents according to their predictions and derives a final answer through a group-level reliability scoring method. Experimental results on a dataset including eight event categories demonstrate the effectiveness of MCA. Using Llama-3.1-70B, MCA achieves an accuracy of 82.3% (79.5% for the human crowd). Additionally, we demonstrate that MCA can alleviate the cognitive biases in LLMs and investigate three influencing factors.

Peer review is essential for maintaining academic quality, but the increasing volume of submissions places a significant burden on reviewers. Large language models (LLMs) offer potential assistance in this process, yet their susceptibility to textual adversarial attacks raises reliability concerns. This paper investigates the robustness of LLMs used as automated reviewers in the presence of such attacks. We focus on three key questions: (1) The effectiveness of LLMs in generating reviews compared to human reviewers. (2) The impact of adversarial attacks on the reliability of LLM-generated reviews. (3) Challenges and potential mitigation strategies for LLM-based review. Our evaluation reveals significant vulnerabilities, as text manipulations can distort LLM assessments. We offer a comprehensive evaluation of LLM performance in automated peer reviewing and analyze its robustness against adversarial attacks. Our findings emphasize the importance of addressing adversarial risks to ensure AI strengthens, rather than compromises, the integrity of scholarly communication.

Texts generated by large language models (LLMs) are increasingly widespread online. Due to the lack of effective attribution mechanisms, the enforcement of copyright and the prevention of misuse remain significant challenges in the context of LLM-generated content. LLMs watermark emerges as a crucial technology to trace the source of AI-generated content. However, most existing watermarking methods reduce the fidelity of semantics. To address this issue, this paper introduces a novel watermarking framework. To enhance the fidelity of semantics, we propose low-entropy POS-guided token partitioning mechanism and z-score-driven dynamic bias mechanism. Moreover, to enhance the robustness against potential bias sparsity exploitation attack, we propose a relative position encoding (RPE) mechanism, which can uniformly distribute bias in the generated text. Evaluated across 6 baselines, 4 tasks, and 5 LLMs under 8 attacks, compared to the KGW, our watermark improves semantic fidelity by 24.53% (RC-PPL) and robustness by 3.75% (F1). Our code is publicly available, facilitating reproducibility in LLM watermarking research.

Large Language Models (LLMs) have revolutionized language processing and understanding, yet their performance is hampered by inaccuracies and outdated information. Model editing techniques offer a solution but face two key challenges: **(I)** Most methods inject knowledge by constructing rigid loss, which leads to poor compatibility when dealing with higher-order multi-hop problems. **(II)** Locate-then-edit vein, by altering pre-trained parameters, inevitably affect normal knowledge and even face the catastrophic forgetting. In this paper, we introduce **KGMET**, a framework that constructs knowledge graphs using available information to guide the direction of knowledge editing, enabling **consistent**, **aligned**, and **stable** information during **large-scale** editing scenario. Furthermore, *KGMET* goes beyond this by employing orthogonal constraints to block the interference of irrelevant information, ensuring the updates are both controllable and generalizable. Experiments on Multi-Conterfact, ZsRE, and MQuAKE datasets using *Llama-3-8B*, *GPT-J-6B*, and *GPT-2-XL* models showcase improvements over state-of-the-art methods, with ↑ 5%-17% in multi-hop tasks while remaining generalizable (at least ↑ 20% in fluency). Our code is available on Github.

Knowledge distillation for knowledge graph embedding (KGE) models effectively compresses KGE models by reducing their embedding dimensions. While existing methods distill knowledge from a high-dimensional teacher to a low-dimensional student, they typically rely on a single teacher embedding space, thereby overlooking valuable complementary knowledge from teachers in distinct embedding spaces. This paper introduces DTDES-KGE, a novel knowledge distillation framework that significantly enhances distillation performance by leveraging dual teachers in distinct embedding spaces. To overcome the challenge of spatial heterogeneity when integrating knowledge from dual teachers, we propose a spatial compatibility module for reconciliation. Additionally, we introduce a student-aware knowledge fusion mechanism to fuse the knowledge from dual teachers dynamically. Extensive experiments on two real-world datasets validate the effectiveness of DTDES-KGE.

Evaluating large language models (LLMs) in medicine is crucial because medical applications require high accuracy with little room for error. Current medical benchmarks have three main types: medical exam-based, comprehensive medical, and specialized assessments. However, these benchmarks have limitations in question design (mostly multiple-choice), data sources (often not derived from real clinical scenarios), and evaluation methods (poor assessment of complex reasoning). To address these issues, we present LLMEval-Medicine, a new benchmark covering five core medical areas, including 2,996 questions created from real-world electronic health records and expert-designed clinical scenarios. We also design an automated evaluation pipeline, incorporating expert-developed checklists into our LLM-as-Judge framework. Furthermore, our methodology validates machine scoring through human-machine agreement analysis, dynamically refining checklists and prompts based on expert feedback to ensure reliability. We evaluate 13 LLMs across three categories (specialized medical models, open-source models, and closed-source models) on LLMEval-Med, providing valuable insights for the safe and effective deployment of LLMs in medical domains.

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Watermark Smoothing Attacks against Language Models
Hongyan Chang | Hamed Hassani | Reza Shokri

Watermarking is a key technique for detecting AI-generated text. In this work, we study its vulnerabilities and introduce the Smoothing Attack, a novel watermark removal method. By leveraging the relationship between the model’s confidence and watermark detectability, our attack selectively smoothes the watermarked content, erasing watermark traces while preserving text quality. We validate our attack on open-source models ranging from 1.3 B to 30B parameters on 10 different watermarks, demonstrating its effectiveness. Our findings expose critical weaknesses in existing watermarking schemes and highlight the need for stronger defenses.

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PICD-Instruct: A Generative Instruction Learning Framework for Few-Shot Multi-Intent Spoken Language Understanding
Wenbin Hua | Rui Fan | Tingting He | Ming Dong

Few-shot multi-intent spoken language understanding (SLU) aims to identify users’ multiple intents and key slots using a tiny amount of annotated data. Recent advances in large language models (LLMs) have utilized instruction learning frameworks to model intent-slot interdependencies, typically requiring abundant data for effective training. However, in few-shot scenarios, these frameworks face challenges such as mismatches between the number of generated slots and input lengths, relational confusion in multi-intent scenarios and neglect of task-specific variations in intent counts across utterances. To overcome the challenges, we propose PICD-Instruct, a novel generative framework based on Basic Instructions (BI), Pairwise Interaction Instructions (PII) and Contrastive Distinct Instructions (CDI). Specifically, BI directs LLMs to generate entities along with associated words, thereby mitigating mismatches in quantitative correspondences. PII explicitly captures dual-task interdependencies by guiding LLMs to pair each intent with its related entities. CDI enhances understanding of utterances by guiding LLMs to determine whether two utterances share the same intent count. Experimental results on public datasets indicate that PICD-Instruct achieves state-of-the-art performance.

Despite advances in improving large language model (LLM) to refuse to answer malicious instructions, widely used LLMs remain vulnerable to jailbreak attacks where attackers generate instructions with distributions differing from safety alignment corpora. New attacks expose LLMs’ inability to recognize unseen malicious instructions, highlighting a critical distributional mismatch between training data and real-world attacks that forces developers into reactive patching cycles. To tackle this challenge, we propose **IMAGINE**, a synthesis framework that leverages embedding space distribution analysis to generate jailbreak-like instructions. This approach effectively fills the distributional gap between authentic jailbreak patterns and safety alignment corpora. IMAGINE follows an iterative optimization process that dynamically evolves text generation distributions across iterations, thereby augmenting the coverage of safety alignment data distributions through synthesized data examples. Based on the safety-aligned corpus enhanced through IMAGINE, our framework demonstrates significant decreases in attack success rate on Qwen2.5, Llama3.1, and Llama3.2 without compromising their utility.

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Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent?
Xi Ai | Mahardika Krisna Ihsani | Min-Yen Kan

Cross-lingual consistency should be considered to assess cross-lingual transferability, maintain the factuality of the model knowledge across languages, and preserve the parity of language model performance. We are thus interested in analyzing, evaluating, and interpreting cross-lingual consistency for factual knowledge.To facilitate our study, we examine multiple pretrained models and tuned models with code-mixed coreferential statements that convey identical knowledge across languages. Interpretability approaches are leveraged to analyze the behavior of a model in cross-lingual contexts, showing different levels of consistency in multilingual models, subject to language families, linguistic factors, scripts, and a bottleneck in cross-lingual consistency on a particular layer. Code-switching training and cross-lingual word alignment objectives show the most promising results, emphasizing the worthiness of cross-lingual alignment supervision and code-switching strategies for both multilingual performance and cross-lingual consistency enhancement. In addition, experimental results suggest promising result for calibrating consistency on test time via activation patching.

We introduce Llama-Krikri-8B, a cutting-edge Large Language Model tailored for the Greek language, built on Meta’s Llama 3.1-8B. Llama-Krikri-8B has been extensively trained on high-quality Greek data to ensure superior adaptation to linguistic nuances. With 8 billion parameters, it offers advanced capabilities while maintaining efficient computational performance. Llama-Krikri-8B supports both Modern Greek and English, and is also equipped to handle polytonic text and Ancient Greek. The chat version of Llama-Krikri-8B features a multi-stage post-training pipeline, utilizing both human and synthetic instruction and preference data, by applying techniques such as MAGPIE. In addition, for evaluation, we propose three novel public benchmarks for Greek. Our evaluation on existing as well as the proposed benchmarks shows notable improvements over comparable Greek and multilingual LLMs in both natural language understanding and generation as well as code generation.

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Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs
Quoc-An Nguyen | Xuan-Hung Le | Thi-Minh-Thu Vu | Hoang-Quynh Le

Scientific summarization remains a challenging task due to the complex characteristics of internal structure and its external relations to other documents. To address this, our proposed model constructs a heterogeneous graph to represent a document and its relevant external citations. This heterogeneous graph enables the model to exploit information across multiple granularities, ranging from fine-grained textual components to the global document structure, and from internal content to external citation context, which facilitates context-aware representations and effectively reduces redundancy. In addition, we develop an effective encoder based on a multi-granularity graph attention mechanism and the triplet loss objective to enhance representation learning performance. Experimental results across three different scenarios consistently demonstrate that our model outperforms existing approaches. Source code is available at: https://github.com/quocanuetcs/CiteHeteroSum.

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Detecting Continuously Evolving Scam Calls under Limited Annotation: A LLM-Augmented Expert Rule Framework
Haoyu Ma | Qinliang Su | Minhua Huang | Wu Kai

The increasing prevalence of scam calls, particularly on online platforms for recruitment, ride-hailing, and delivery services, has become a significant social and economic issue. Traditional approaches to scam call detection rely on labeled data and assume a static distribution of scam narratives. However, scammers continuously evolve their tactics, making these methods less effective. In this paper, we propose a novel approach leveraging large language models (LLMs) to detect continuously evolving scam calls. By abstracting scam and normal call rules based on expert knowledge, we develop a hierarchical few-shot prompting framework. This framework consists of a discrimination module to identify scam characteristics, a reflection module to reduce false positives by comparing with normal call features, and a summary step to synthesize the final detection results. Our method is evaluated on real-world and synthesized datasets, demonstrating superior performance in detecting evolving scam calls with minimal labeled data. Furthermore, we show that the framework is highly adaptable to new scam detection scenarios, requiring only modifications to the expert rules.

This study investigates the position bias in information retrieval, where models tend to overemphasize content at the beginning of passages while neglecting semantically relevant information that appears later. To analyze the extent and impact of position bias, we introduce a new evaluation framework consisting of two position-aware retrieval benchmarks (SQuAD-PosQ, FineWeb-PosQ) and an intuitive diagnostic metric, the Position Sensitivity Index (PSI), for quantifying position bias from a worst-case perspective. We conduct a comprehensive evaluation across the full retrieval pipeline, including BM25, dense embedding models, ColBERT-style late-interaction models, and full-interaction reranker models. Our experiments show that when relevant information appears later in the passage, dense embedding models and ColBERT-style models suffer significant performance degradation (an average drop of 15.6%). In contrast, BM25 and reranker models demonstrate greater robustness to such positional variation. These findings provide practical insights into model sensitivity to the position of relevant information and offer guidance for building more position-robust retrieval systems. Code and data are publicly available at: https://github.com/NovaSearch-Team/position-bias-in-IR.

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GenPoE: Generative Passage-level Mixture of Experts for Knowledge Enhancement of LLMs
Xuebing Liu | Shanbao Qiao | Seung-Hoon Na

Typically, parametric adaptation methods such as domain-adaptive pretraining (DAP) and retrieval-augmented generation (RAG) have been considered effective approaches for adapting large language models (LLMs) to new knowledge or domains. To unify positive effects of parametric adaptation and RAG, this paper proposes GenPoE, i.e., “generative’’ passage-level mixture of experts (MoEs) for enhancing knowledge of LLMs. The key component is its novel MoE-generating hypernetwork which takes in-context retrieved passages and generates their “expert’’ parameters, where these generated parameters are then integrated into LLMs by forming expert networks. With its use of “generated’’ parameters, GenPoE does not require a separate parameter training or finetuning stage, which is often costly. By parameterizing passages into expert networks, GenPoE likely exhibits robustness even when the retrieved passages are irrelevant. Experiment results in two open-domain question answering (QA) tasks present that GenPoE shows improved performances over other passage-level knowledge editing, and its combination of RAG produces superior performances over RAG. Our data and code will be available at https://github.com/Liu-Xuebing/GenPoE.

Listwise ranking based on Large Language Models (LLMs) has achieved state-of-the-art performance in Information Retrieval (IR).However, their effectiveness often depends on LLMs with massive parameter scales and computationally expensive sliding window processing, leading to substantial efficiency bottlenecks. In this paper, we propose a Collaborative Ranking framework (CoRanking) for LLM-based listwise ranking.Specifically, we strategically combine an efficient small reranker and an effective large reranker for collaborative ranking.The small reranker performs initial passage ranking, effectively filtering the passage set to a condensed top-k list (e.g., top-20 passages), and the large reranker (with stronger ranking capability) then reranks only this condensed subset rather than the full list, significantly improving efficiency. We further address that directly passing the top-ranked passages from the small reranker to the large reranker is suboptimal because of the LLM’s strong positional bias in processing input sequences. To resolve this issue, we propose a passage order adjuster learned by RL that dynamically reorders the top passages returned by the small reranker to better align with the large LLM’s input preferences. Our extensive experiments across three IR benchmarks demonstrate that CoRanking achieves superior efficiency, reducing ranking latency by approximately 70% while simultaneously improving effectiveness, compared to the standalone large reranker.

Retrieval-augmented generation (RAG) has become a fundamental paradigm for addressing the challenges faced by large language models in handling real-time information and domain-specific problems. Traditional RAG systems primarily rely on the in-context learning (ICL) capabilities of the large language model itself. Still, in-depth research on the specific capabilities needed by the RAG generation model is lacking, leading to challenges with inconsistent document quality and retrieval system imperfections. Even the limited studies that fine-tune RAG generative models often lack a granular focus on RAG tasks or a deeper utilization of chain-of-thought processes. To address this, we propose that RAG models should possess three progressively hierarchical abilities (1) Filtering: the ability to select relevant information; (2) Combination: the ability to combine semantic information across paragraphs; and (3) RAG-specific reasoning: the ability to further process external knowledge using internal knowledge. Thus, we introduce our new RAG instruction fine-tuning method, Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (HIRAG) incorporates a “think before answering” strategy. This method enhances the model’s open-book examination capability by utilizing multi-level progressive chain-of-thought. Experiments show that the HIRAG training strategy significantly improves the model’s performance on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA.

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Towards Personalized Conversational Sales Agents: Contextual User Profiling for Strategic Action
Tongyoung Kim | Jeongeun Lee | SooJin Yoon | SungHwan Kim | Dongha Lee

Conversational Recommender Systems (CRSs) aim to engage users in dialogue to provide tailored recommendations. While traditional CRSs focus on eliciting preferences and retrieving items, real-world e-commerce interactions involve more complex decision-making, where users consider multiple factors beyond simple attributes. To capture this complexity, we introduce Conversational Sales (CSALES), a novel task that integrates preference elicitation, recommendation, and persuasion within a unified conversational framework. To support realistic and systematic evaluation, we present CSUSER, an evaluation protocol with LLM-based user simulator grounded in real-world behavioral data by modeling fine-grained user profiles for personalized interaction. We also propose CSI, a conversational sales agent that proactively infers contextual user profiles and strategically selects actions through conversation. Comprehensive experiments show that CSI significantly improves both recommendation success and persuasive effectiveness across diverse user profiles.

Web agents powered by Large Language Models (LLMs) show promise for next-generation AI, but their limited reasoning in uncertain, dynamic web environments hinders robust deployment. In this paper, we identify key reasoning skills essential for effective web agents, i.e., reflection & lookahead, branching, and rollback, and curate trajectory data that exemplifies these abilities by reconstructing the agent’s (inference-time) reasoning algorithms into chain-of-thought rationales. We conduct experiments in the agent self-improving benchmark, OpenWebVoyager, and demonstrate that distilling salient reasoning patterns into the backbone LLM via simple fine-tuning can substantially enhance its performance. Our approach yields significant improvements across multiple benchmarks, including WebVoyager, Mind2web-live, and SimpleQA (web search), highlighting the potential of targeted reasoning skill enhancement for web agents.

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Interesting Culture: Social Relation Recognition from Videos via Culture De-confounding
Yuxuan Zhang | Yangfu Zhu | Haorui Wang | Bin Wu

Social relationship recognition, as one of the fundamental tasks in video understanding, contributes to the construction and application of multi-modal knowledge graph. Previous works have mainly focused on two aspects: generating character graphs and multi-modal fusion. However, they often overlook the impact of cultural differences on relationship recognition. Specifically, relationship recognition models are susceptible to being misled by training data from a specific cultural context. This can result in the learning of culture-specific spurious correlations, ultimately restricting the ability to recognize social relationships in different cultures. Therefore, we employ a customized causal graph to analyze the confounding effects of culture in the relationship recognition task. We propose a Cultural Causal Intervention (CCI) model that mitigates the influence of culture as a confounding factor in the visual and textual modalities. Importantly, we also construct a novel video social relation recognition (CVSR) dataset to facilitate discussion and research on cultural factors in video tasks. Extensive experiments conducted on several datasets demonstrate that the proposed model surpasses state-of-the-art methods.

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ThinkSwitcher: When to Think Hard, When to Think Fast
Guosheng Liang | Longguang Zhong | Ziyi Yang | Xiaojun Quan

Large reasoning models (LRMs) excel at solving complex tasks by leveraging long chain-of-thought (CoT) reasoning. However, this often leads to overthinking on simple tasks, resulting in unnecessary computational overhead. We observe that LRMs inherently possess the capability for efficient short CoT reasoning, which can be reliably elicited through prompt design. To leverage this capability, we propose ThinkSwitcher, a framework that enables a single LRM to dynamically switch between short and long CoT modes based on task complexity. ThinkSwitcher introduces a lightweight switching module trained with supervision signals derived from the relative performance of each reasoning mode across tasks. Experiments on multiple reasoning benchmarks show that ThinkSwitcher reduces computational cost by 20-30% while maintaining high accuracy on complex tasks. This demonstrates the effectiveness of ThinkSwitcher as a scalable and efficient solution for unified LRM deployment.

Retrieval-Augmented Generation (RAG) enhances large language models by grounding their outputs in external knowledge. Recent advances in Graph-based RAG (GRAG) frameworks, such as GraphRAG, LightRAG, and HippoRAG2, integrate knowledge graphs into the retrieval process to improve multi-hop reasoning and semantic coherence. While effective in monolingual settings, these methods remain underexplored in cross-lingual scenarios and face limitations in semantic granularity and entity alignment. In this work, we propose MaGiX, the first GRAG framework tailored for English–Vietnamese cross-lingual question answering. MaGiX constructs a multi-granular cross-lingual knowledge graph using fine-grained attribute descriptions and cross-synonym edges, and incorporates a custom multilingual embedding model trained with contrastive learning for semantic alignment. During retrieval, MaGiX leverages graph-based reasoning and a semantic-aware reranking strategy to enhance cross-lingual relevance. Experiments across five benchmarks show that MaGiX substantially outperforms prior GRAG systems in both retrieval accuracy and generation quality, advancing structured retrieval for multilingual QA.

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LexTime: A Benchmark for Temporal Ordering of Legal Events
Claire Barale | Leslie Barrett | Vikram Sunil Bajaj | Michael Rovatsos

Understanding temporal relationships and accurately reconstructing the event timeline is important for case law analysis, compliance monitoring, and legal summarization. However, existing benchmarks lack specialized language evaluation, leaving a gap in understanding how LLMs handle event ordering in legal contexts. We introduce LexTime, a dataset designed to evaluate LLMs’ event ordering capabilities in legal language, consisting of 512 instances from U.S. Federal Complaints with annotated event pairs and their temporal relations. Our findings show that (1) LLMs are more accurate on legal event ordering than on narrative texts (up to +10.5%); (2) longer input contexts and implicit events boost accuracy, reaching 80.8% for implicit-explicit event pairs; (3) legal linguistic complexities and nested clauses remain a challenge. While performance is promising, specific features of legal texts remain a bottleneck for legal temporal event reasoning, and we propose concrete modeling directions to better address them.

In competitive programming task, problem statements are often embedded within elaborate narrative backgrounds, requiring deep understanding of the underlying solutions to successfully complete the tasks. Current code generation models primarily focus on token-level semantic modeling, highly susceptible to distractions from irrelevant narrative statements. Inspired by RAG, retrieving reference code with similar solutions may help enhance model performance on difficult problems. However, existing retrieval models also emphasize surface-level semantic similarity, neglecting the deeper solution-level logical similarities that are critical in competitive programming. Therefore, designing ranking models capable of accurately identifying and retrieving problems and corresponding codes remains an urgent research problem in competitive code generation. In this paper, we propose SolveRank, a solution-aware ranking model empowered by synthetic data for competitive programming tasks. Specifically, we leverage the DeepSeek-R1 model to generate logically equivalent but differently phrased new problems, verified by GPT-4o for solution consistency. Then, we train SolveRank with these as positive samples and BM25/random-retrieved problems as negatives. During inference, SolveRank retrieves relevant problems and corresponding code from the corpus to assist a downstream code generator. Experiments on the xCodeEval dataset demonstrate that SolveRank outperforms SOTA ranking methods in precision and recall metrics, and boosts code generation performance for difficult problems.

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X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Jailbreak Attacks without Compromising Usability
Xiaoya Lu | Dongrui Liu | Yi Yu | Luxin Xu | Jing Shao

With the widespread application of large language models (LLMs) across various domains, techniques for enhancing their security have progressed rapidly. In this paper, we reveal that although existing defense methods can improve the robustness of LLMs against jailbreaks, they compromise usability, i.e., reducing general capabilities or causing the over-refusal problem. From the perspective of LLM mechanism interpretability, we discover that these methods fail to establish a boundary that exactly distinguishes safe and harmful feature representations. Therefore, boundary-safe representations close to harmful representations are inevitably disrupted, leading to a decline in usability. To address this issue, we propose X-Boundary to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary. In this way, harmful representations can be precisely erased without disrupting safe ones. Experimental results show that X-Boundary achieves state-of-the-art defense performance against both single-turn and multi-turn jailbreak attacks, while reducing the over-refusal rate by about 20% and maintaining nearly complete general capability. Furthermore, we theoretically prove and empirically verify that X-Boundary can accelerate the convergence process during training.

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Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack
Sagiv Antebi | Edan Habler | Asaf Shabtai | Yuval Elovici

Large language models (LLMs) have become essential tools for digital task assistance. Their training relies heavily on the collection of vast amounts of data, which may include copyright-protected or sensitive information. Recent studies on detecting pretraining data in LLMs have primarily focused on sentence- or paragraph-level membership inference attacks (MIAs), usually involving probability analysis of the target model’s predicted tokens. However, these methods often exhibit poor accuracy, failing to account for the semantic importance of textual content and word significance. To address these shortcomings, we propose Tag&Tab, a novel approach for detecting data used in LLM pretraining. Our method leverages established natural language processing (NLP) techniques to tag keywords in the input text, a process we term Tagging. Then, the LLM is used to obtain probabilities for these keywords and calculate their average log-likelihood to determine input text membership, a process we refer to as Tabbing. Our experiments on four benchmark datasets (BookMIA, MIMIR, PatentMIA, and the Pile) and several open-source LLMs of varying sizes demonstrate an average increase in AUC scores ranging from 5.3% to 17.6% over state-of-the-art methods. Tag&Tab not only sets a new standard for data leakage detection in LLMs, but its outstanding performance is a testament to the importance of words in MIAs on LLMs.

Large language models (LLMs) have demonstrated an impressive ability to role-play humans and replicate complex social dynamics. However, large-scale LLM-driven simulations still face significant challenges in high time and computational costs. We observe that there exists redundancy in current agent communication: when expressing the same intention, agents tend to use lengthy and repetitive language, whereas humans naturally prefer concise expressions. To this end, we propose EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation. Inspired by how human language evolves through interactions, we induce a more compact language by identifying and preserving core communicative concepts at the vocabulary level and evolving efficient expression patterns at the sentence level through natural selection. We apply the induced language in various social simulations. Experimental results demonstrate that EcoLANG reduces token consumption by over 20%, enhancing efficiency without sacrificing simulation accuracy.

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Revealing the Inherent Instructability of Pre-Trained Language Models
Seokhyun An | Minji Kim | Hyounghun Kim

Instruction tuning—supervised fine-tuning using instruction-response pairs—is a key step in making pre-trained large language models (LLMs) instructable. Meanwhile, LLMs perform multitask learning during their pre-training, acquiring extensive knowledge and capabilities. We hypothesize that the pre-training stage can enable them to develop the ability to comprehend and address instructions. To verify this, we propose Response Tuning (RT), which removes the instruction and its corresponding mapping to the response from instruction tuning. Instead, it focuses solely on establishing a response distribution. Our experiments demonstrate that RT models, trained only on responses, can effectively respond to a wide range of instructions akin to their instruction-tuned counterparts. In addition, we observe that the models can recognize and reject unsafe queries after learning a safety policy only from the response data. Furthermore, we find that these observations extend to an in-context learning setting. These findings support our hypothesis, highlighting the extensive inherent capabilities of pre-trained LLMs.

Media framing refers to the emphasis on specific aspects of perceived reality to shape how an issue is defined and understood. Its primary purpose is to shape public perceptions often in alignment with the authors’ opinions and stances. However, the interaction between stance and media frame remains largely unexplored. In this work, we apply an interdisciplinary approach to conceptualize and computationally explore this interaction with internet memes on climate change. We curate CLIMATEMEMES, the first dataset of climate-change memes annotated with both stance and media frames, inspired by research in communication science. CLIMATEMEMES includes 1,184 memes sourced from 47 subreddits, enabling analysis of frame prominence over time and communities, and sheds light on the framing preferences of different stance holders. We propose two meme understanding tasks: stance detection and media frame detection. We evaluate LLaVA-NeXT and Molmo in various setups, and report the corresponding results on their LLM backbone. Human captions consistently enhance performance. Synthetic captions and human-corrected OCR also help occasionally. Our findings highlight that VLMs perform well on stance, but struggle on frames, where LLMs outperform VLMs. Finally, we analyze VLMs’ limitations in handling nuanced frames and stance expressions on climate change internet memes.

Personality assessment is essential for developing user-centered systems, playing a critical role across domains including hiring, education, and personalized system design. With the integration of conversational AI systems into daily life, automatically assessing human personality through natural language interaction has gradually gained more attention. However, existing personality assessment datasets based on natural language generally lack consideration of interactivity. Therefore, we propose Personality-1260, a Chinese dataset containing 1260 interaction rounds between humans and agents with different personalities, aiming to support research on personality assessment. Based on this dataset, we designed experiments to explore the effects of different interaction rounds and agent personalities on personality assessment. Results show that fewer interaction rounds perform better in most cases, and agents with different personalities stimulate different expressions of users’ personalities. These findings provide guidance for the design of interactive personality assessment systems.

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TailorRPA: A Retrieval-Based Framework for Eliciting Personalized and Coherent Role-Playing Agents in General Domain
Zhenpeng Gao | Xiaofen Xing | Xiangmin Xu

Recent advancements of general domain oriented Role-playing Agents (RPAs) have enabled the agents to maintain character properties in a wide spectrum of daily tasks beyond mere scenario based chit-chatting. Nonetheless, current works lacks consideration of replicating internal properties of characters like fine-grained memories, and failed to take account of aligning with the knowledge boundary of each character, resulting in degraded personalization and proneness to character hallucination in general domain. To address these problems, we draw inspirations from the context effect theory and propose a retrieval-based framework TailorRPA to harvest tailored general domain instructions to improve integration of fine-grained memories and incorporate general-domain protective queries to help shape the character-wise knowledge boundary, alleviating character hallucination. Based on the framework, we developed a role-playing dataset TailorGen, comprising both role-specific and general-domain instructions. Through empirical experiments, we proved the superiority of TailorRPA in eliciting general domain role-playing capabilities and alleviating character hallucination compared to baseline methods, and explored the existence of character hallucination in state-of-the-art proprietary models through empirical experiments, underlining the importance of our work.

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SCE: Semantic Consistency Enhanced Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning
Yanwen Huang | Yao Liu | Qiao Liu | Rui Hou | Tingting Dai

Multi-hop reasoning with reinforcement learning has proven effective in discovering inference paths in incomplete knowledge graphs. However, a major challenge remains: spurious paths (incorrect reasoning paths that accidentally lead to correct answers) often arise due to reward mechanisms that prioritize final results over reasoning quality. While existing approaches attempt to mitigate this issue using external rules, they often neglect the internal semantic consistency between the target triple and the intermediate triples along the reasoning path. In this paper, we propose a novel framework, Semantic Consistency Enhanced Reinforcement Learning (SCE), which incorporates semantic consistency into the reward function to guide multi-hop reasoning. Experimental results demonstrate that SCE outperforms strong baseline methods and facilitates the discovery of more interpretable reasoning paths.

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ReGraphRAG: Reorganizing Fragmented Knowledge Graphs for Multi-Perspective Retrieval-Augmented Generation
Soohyeong Kim | Seok Jun Hwang | JungHyoun Kim | Jeonghyeon Park | Yong Suk Choi

Recent advancements in Retrieval-Augmented Generation (RAG) have improved large language models (LLMs) by incorporating external knowledge at inference time. Graph-based RAG systems have emerged as promising approaches, enabling multi-hop reasoning by organizing retrieved information into structured graphs. However, when knowledge graphs are constructed from unstructured documents using LLMs, they often suffer from fragmentation—resulting in disconnected subgraphs that limit inferential coherence and undermine the advantages of graph-based retrieval. To address these limitations, we propose ReGraphRAG, a novel framework designed to reconstruct and enrich fragmented knowledge graphs through three core components: Graph Reorganization, Perspective Expansion, and Query-aware Reranking. Experiments on four benchmarks show that ReGraphRAG outperforms state-of-the-art baselines, achieving over 80% average diversity win rate. Ablation studies highlight the key contributions of graph reorganization and especially perspective expansion to performance gains. Our code is available at: https://anonymous.4open.science/r/ReGraphRAG-7B73

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GASE: Generatively Augmented Sentence Encoding
Manuel Frank | Haithem Afli

We propose a training-free approach to improve sentence embeddings leveraging test-time compute by applying generative text models for data augmentation at inference time. Unlike conventional data augmentation that utilises synthetic training data, our approach does not require access to model parameters or the computational resources typically required for fine-tuning state-of-the-art models. Generatively Augmented Sentence Encoding variates the input text by paraphrasing, summarising, or extracting keywords, followed by pooling the original and synthetic embeddings.Experimental results on the Massive Text Embedding Benchmark for Semantic Textual Similarity (STS) demonstrate performance improvements across a range of embedding models using different generative models for augmentation. We find that generative augmentation leads to larger performance improvements for embedding models with lower baseline performance. These findings suggest that integrating generative augmentation at inference time adds semantic diversity and can enhance the robustness and generalisability of sentence embeddings for embedding models. Our results show that performance gains depend on the embedding model and the dataset.

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The “r” in “woman” stands for rights. Auditing LLMs in Uncovering Social Dynamics in Implicit Misogyny
Arianna Muti | Chris Emmery | Debora Nozza | Alberto Barrón-Cedeño | Tommaso Caselli

Persistent societal biases like misogyny express themselves more often implicitly than through openly hostile language.However, previous misogyny studies have focused primarily on explicit language, overlooking these more subtle forms. We bridge this gap by examining implicit misogynistic expressions in English and Italian. First, we develop a taxonomy of social dynamics, i.e., the underlying communicative intent behind misogynistic statements in social media data. Then, we test the ability of nine LLMs to identify the social dynamics as a multi-label classification and text span selection: first LLMs must choose social dynamics given a prefixed list, then they have to explicitly identify the text spans that triggered their decisions. We also investigate the extent of using different learning settings: zero and few-shot, and prescriptive. Our analysis suggests that LLMs struggle to follow instructions and reason in all settings, mostly relying on semantic associations, recasting claims of emergent abilities.

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Fact Verification on Knowledge Graph via Programmatic Graph Reasoning
Yuanzhen Hao | Desheng Wu

Fact verification on knowledge graphs (KGs) uses the structured representation of entities and relations as evidence for validating claims. Previous methods for KG-based fact verification predominantly use natural language inference (NLI) models to predict entailment between claims and KG triples, based on implicit reasoning. We propose Programmatic Graph Reasoning (PGR), a novel framework that integrates large language models (LLMs) for fact verification on KGs. PGR explicitly encodes the reasoning process as a graph reasoning program composed of predefined functions to verify claims step by step. These functions are executed sequentially for graph reasoning and final result prediction. By making the graph reasoning process explicit, PGR ensures more precise and transparent reasoning steps compared to implicit methods. Experimental results on the FactKG dataset demonstrate that PGR achieves state-of-the-art performance with 86.82% accuracy, outperforming all the baseline models. Further analysis confirms the interpretability and effectiveness of our method in handling complex graph reasoning.

Large language models (LLMs) have demonstrated remarkable capabilities in natural language tasks, yet their performance in dynamic, real-world financial environments remains underexplored. Existing approaches are confined to historical backtesting, where trading actions cannot influence market prices, and agents train on static data. To overcome this limitation, we present the Agent Trading Arena, a virtual zero-sum stock market in which LLM-based agents engage in competitive, mult-agent trading and directly impact price dynamics. By simulating realistic bid-ask interactions, our platform enables agents to train in scenarios that closely mirror live markets, thereby narrowing the gap between training and evaluation. Experiments show that LLMs struggle with numerical reasoning when given plain-text data, tending to overfit local patterns and recent values. In contrast, chart-based visualizations significantly boost both numerical reasoning and trading performance. Moreover, integrating a reflection module yields further improvements, especially with visual inputs. Finally, evaluations of the NASDAQ and CSI datasets demonstrate the superiority of our method, particularly under high volatility. All code and data are available at https://github.com/wekjsdvnm/Agent-Trading-Arena.

Customer reviews on e-commerce platforms capture critical affective signals that drive purchasing decisions. However, no existing research has explored the joint task of emotion detection and explanatory span identification in e-commerce reviews - a crucial gap in understanding what triggers customer emotional responses. To bridge this gap, we propose a novel joint task unifying Emotion detection and Opinion Trigger extraction (EOT), which explicitly models the relationship between causal text spans (opinion triggers) and affective dimensions (emotion categories) grounded in Plutchik’s theory of 8 primary emotions.In the absence of labeled data, we introduce EOT-X, a human-annotated collection of 2,400 reviews with fine-grained emotions and opinion triggers. We evaluate 23 Large Language Models (LLMs) and present EOT-DETECT, a structured prompting framework with systematic reasoning and self-reflection. Our framework surpasses zero-shot and chain-of-thought techniques, across e-commerce domains.

The generative large language models (LLMs) are increasingly used for data augmentation tasks, where text samples are paraphrased (or generated anew) and then used for downstream model fine-tuning. This is useful, especially for low-resource settings. For better augmentations, LLMs are prompted with examples (few-shot scenarios). Yet, the samples are mostly selected randomly, and a comprehensive overview of the effects of other (more ”informed”) sample selection strategies is lacking. In this work, we compare sample selection strategies existing in the few-shot learning literature and investigate their effects in LLM-based textual augmentation in a low-resource setting. We evaluate this on in-distribution and out-of-distribution model performance. Results indicate that while some ”informed” selection strategies increase the performance of models, especially for out-of-distribution data, it happens only seldom and with marginal performance increases. Unless further advances are made, a default of random sample selection remains a good option for augmentation practitioners.

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BanglaByT5: Byte-Level Modelling for Bangla
Pramit Bhattacharyya | Arnab Bhattacharya

Large language models (LLMs) have achievedremarkable success across various natural lan-guage processing tasks. However, most LLMmodels use traditional tokenizers like BPE andSentencePiece, which fail to capture the finernuances of a morphologically rich languagelike Bangla (Bengali). In this work, we introduce BanglaByT5, the first byte-level encoder-decoder model explicitly tailored for Bangla.Built upon a small variant of Google’s ByT5architecture, BanglaByT5 is pre-trained on a14GB curated corpus combining high-qualityliterary and newspaper articles. Through zero-shot and supervised evaluations across gen-erative and classification tasks, BanglaByT5demonstrates competitive performance, surpassing several multilingual and larger models.Our findings highlight BanglaByT5’s potentialas a lightweight yet powerful tool for BanglaNLP, particularly in resource-constrained orscalable environments. BanglaByT5 is pub-licly available for download from https://huggingface.co/Vacaspati/BanglaByT5.

Cross-lingual topic modeling aims to uncover shared semantic themes across languages. Several methods have been proposed to address this problem, leveraging both traditional and neural approaches. While previous methods have achieved some improvements in topic diversity, they often struggle to ensure high topic coherence and consistent alignment across languages. We propose XTRA (Cross-Lingual Topic Modeling with Topic and Representation Alignments), a novel framework that unifies Bag-of-Words modeling with multilingual embeddings. XTRA introduces two core components: (1) representation alignment, aligning document-topic distributions via contrastive learning in a shared semantic space; and (2) topic alignment, projecting topic-word distributions into the same space to enforce cross-lingual consistency. This dual mechanism enables XTRA to learn topics that are interpretable (coherent and diverse) and well-aligned across languages. Experiments on multilingual corpora confirm that XTRA significantly outperforms strong baselines in topic coherence, diversity, and alignment quality.

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CodeContests+: High-Quality Test Case Generation for Competitive Programming
Zihan Wang | Siyao Liu | Yang Sun | Ming Ding | Hongyan Li

Competitive programming, due to its high reasoning difficulty and precise correctness feedback, has become a key task for both training and evaluating the reasoning capabilities of large language models (LLMs). However, while a large amount of public problem data, such as problem statements and solutions, is available, the test cases of these problems are often difficult to obtain. Therefore, test case generation is a necessary task for building large-scale datasets, and the quality of the test cases directly determines the accuracy of the evaluation. In this paper, we introduce an LLM-based agent system that creates high-quality test cases for competitive programming problems. We apply this system to the CodeContests dataset and propose a new version with improved test cases, named CodeContests+. We evaluated the quality of test cases in CodeContests+. First, we used 1.72 million submissions with pass/fail labels to examine the accuracy of these test cases in evaluation. The results indicated that CodeContests+ achieves significantly higher accuracy than CodeContests, particularly with a notably higher True Positive Rate (TPR). Subsequently, our experiments in LLM Reinforcement Learning (RL) further confirmed that improvements in test case quality yield considerable advantages for RL.

Direct Preference Optimization (DPO) is a widely used offline preference optimization algorithm that enhances the simplicity and training stability of reinforcement learning through reward function reparameterization from PPO. Recently, SimPO (Simple Preference Optimization) and CPO (Contrastive Preference Optimization) have proposed reference-free preference optimization methods to simplify DPO’s training process. We observe that these reference-free methods exhibit higher training efficiency but are prone to overoptimization, leading to performance degradation. To address these issues, we propose Self Preference Optimization (SPO). SPO employs the SiLU function to replace the conventional logsigmoid loss function. The SiLU function attains its minimum at a finite value, preventing the model from excessively amplifying the chosen-rejected sample probability ratio and thereby mitigating overoptimization problem. We theoretically demonstrate that the SPO loss is an upper bound of the DPO loss, implying that optimizing the SPO objective implicitly optimizes the DPO objective. We evaluate SPO’s effectiveness across multiple benchmarks including AlpacaEval 2 and MT-Bench. Experimental results show that SPO achieves a 7% improvement over SimPO in length-controlled win rate on AlpacaEval 2, while demonstrating superior performance on MT-Bench.

Long-context language models (LCLMs), characterized by their extensive context window, are becoming popular. However, despite the fact that they are nearly perfect at standard long-context retrieval tasks, our evaluations demonstrate they fail in some basic cases. Later, we find they can be well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts. This result emphasizes the potential necessity of solving specific long-context tasks using long-CoT methods, while previous long-context benchmarks always ignore the necessity of long reasoning for long-context tasks and treat them as direct QA tasks. Our code and datasets are available at https://github.com/yuyijiong/hard_retrieval_for_llm

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Benchmarking Critical Questions Generation: A Challenging Reasoning Task for Large Language Models
Blanca Calvo Figueras | Rodrigo Agerri

The task of Critical Questions Generation (CQs-Gen) aims to foster critical thinking by enabling systems to generate questions that expose underlying assumptions and challenge the validity of argumentative reasoning structures. Despite growing interest in this area, progress has been hindered by the lack of suitable datasets and automatic evaluation standards. This paper presents a comprehensive approach to support the development and benchmarking of systems for this task. We construct the first large-scale dataset including ~5K manually annotated questions. We also investigate automatic evaluation methods and propose reference-based techniques as the strategy that best correlates with human judgments. Our zero-shot evaluation of 11 LLMs establishes a strong baseline while showcasing the difficulty of the task. Data and code plus a public leaderboard are provided to encourage further research, not only in terms of model performance, but also to explore the practical benefits of CQs-Gen for both automated reasoning and human critical thinking.

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ResearchArena: Benchmarking Large Language Models’ Ability to Collect and Organize Information as Research Agents
Hao Kang | Chenyan Xiong

Large language models (LLMs) excel across many natural language processing tasks but face challenges in domain-specific, analytical tasks such as conducting research surveys. This study introduces ResearchArena, a benchmark designed to evaluate LLMs’ capabilities in conducting academic surveys—a foundational step in academic research. ResearchArena models the process in three stages: (1) information discovery, identifying relevant literature; (2) information selection, evaluating papers’ relevance and impact; and (3) information organization, structuring knowledge into hierarchical frameworks such as mind-maps. Notably, mind-map construction is treated as a bonus task, reflecting its supplementary role in survey-writing. To support these evaluations, we construct an offline environment of 12M full-text academic papers and 7.9K survey papers. To ensure ethical compliance, we do not redistribute copyrighted materials; instead, we provide code to construct the environment from the Semantic Scholar Open Research Corpus (S2ORC). Preliminary evaluations reveal that LLM-based approaches underperform compared to simpler keyword-based retrieval methods, though recent reasoning models such as DeepSeek-R1 show slightly better zero-shot performance. These results underscore significant opportunities for advancing LLMs in autonomous research. We open-source the code to construct the ResearchArena benchmark at https://github.com/cxcscmu/ResearchArena.

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LLMs are Privacy Erasable
Zipeng Ye | Wenjian Luo

The capabilities of large language models (LLMs) are advancing at an remarkable pace, along with a surge in cloud services that are powered by LLMs. Their convenience has gradually transformed the routines people work. However, for services such as document summarizing, editing, and so on, users need to upload relevant files or context to obtain the desired services, which may inadvertently expose their privacy. This paper aims to address the challenging balance between the convenience of LLMs services and user privacy concerns. Specifically, based on the structural and functional characteristics of LLMs, we have developed a strategy that safeguards user prompt while accessing LLM cloud services, even in scenarios where advanced reconstruction attacks are adopted. We comprehensively evaluate the efficacy of our method across prominent LLM benchmarks. The empirical results show that our method not only effectively thwarts reconstruction attacks but also, in certain tasks, even improves model performance, surpassing the outcomes reported in official model cards.

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How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models
Abdelrahman Abdallah | Bhawna Piryani | Jamshid Mozafari | Mohammed Ali | Adam Jatowt

In this work, we present a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods, encompassing large language model (LLM)-based, lightweight contextual, and zero-shot approaches, with respect to their performance in information retrieval tasks. We evaluate in total 22 methods, including 40 variants (depending on used LLM) across several established benchmarks, including TREC DL19, DL20, and BEIR, as well as a novel dataset designed to test queries unseen by pretrained models. Our primary goal is to determine, through controlled and fair comparisons, whether a performance disparity exists between LLM-based rerankers and their lightweight counterparts, particularly on novel queries, and to elucidate the underlying causes of any observed differences. To disentangle confounding factors, we analyse the effects of training data overlap, model architecture, and computational efficiency on reranking performance. Our findings indicate that while LLM-based rerankers demonstrate superior performance on familiar queries, their generalisation ability to novel queries varies, with lightweight models offering comparable efficiency. We further identify that the novelty of queries significantly impacts reranking effectiveness, highlighting limitations in existing approaches.

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DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation
Abdelrahman Abdallah | Jamshid Mozafari | Bhawna Piryani | Adam Jatowt

Large Language Models (LLMs) have transformed listwise document reranking by enabling global reasoning over candidate sets, yet single models often struggle to balance fine-grained relevance scoring with holistic cross-document analysis. We propose DeepAgentRank (DeAR), an open-source framework that decouples these tasks through a dual-stage approach, achieving superior accuracy and interpretability. In Stage 1, we distill token-level relevance signals from a frozen 13B LLaMA teacher into a compact 3, 8B student model using a hybrid of cross-entropy, RankNet, and KL divergence losses, ensuring robust pointwise scoring. In Stage 2, we attach a second LoRA adapter and fine-tune on 20K GPT-4o-generated chain-of-thought permutations, enabling listwise reasoning with natural-language justifications. Evaluated on TREC-DL19/20, eight BEIR datasets, and NovelEval-2306, DeAR surpasses open-source baselines by +5.1 nDCG@5 on DL20 and achieves 90.97 nDCG@10 on NovelEval, outperforming GPT-4 by +3.09. Without fine-tuning on Wikipedia, DeAR also excels in open-domain QA, achieving 54.29 Top-1 accuracy on Natural Questions, surpassing baselines like MonoT5, UPR, and RankGPT. Ablations confirm that dual-loss distillation ensures stable calibration, making DeAR a highly effective and interpretable solution for modern reranking systems.

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CANDY: Benchmarking LLMs’ Limitations and Assistive Potential in Chinese Misinformation Fact-Checking
Ruiling Guo | Xinwei Yang | Chen Huang | Tong Zhang | Yong Hu

The effectiveness of large language models (LLMs) to fact-check misinformation remains uncertain, despite their growing use. To this end, we present CANDY, a benchmark designed to systematically evaluate the capabilities and limitations of LLMs in fact-checking Chinese misinformation. Specifically, we curate a carefully annotated dataset of ~20k instances. Our analysis shows that current LLMs exhibit limitations in generating accurate fact-checking conclusions, even when enhanced with chain-of-thought reasoning and few-shot prompting. To understand these limitations, we develop a taxonomy to categorize flawed LLM-generated explanations for their conclusions and identify factual fabrication as the most common failure mode. Although LLMs alone are unreliable for fact-checking, our findings indicate their considerable potential to augment human performance when deployed as assistive tools in scenarios. Our dataset and code can be accessed at https://github.com/SCUNLP/CANDY.

Large language models (LLMs) exhibit remarkable text-generation capabilities, yet struggle with factual consistency, motivating growing interest in factuality verification. Existing factuality verification methods typically follow a Decompose-Then-Verify paradigm, which improves granularity but suffers from poor scalability and efficiency. We propose a novel Decompose-Embed-Interact paradigm that shifts factuality verification from costly text-level reasoning to efficient alignment in embedding space, effectively mitigating the scalability bottlenecks and computational inefficiencies inherent to prior approaches. While the proposed paradigm promises scalable verification, its implementation faces three practical challenges: efficient decomposition, factually faithful embedding, and accurate verification in embedding space. To address these challenges, we introduce E-Verify, a lightweight framework that resolves them through three specially designed modules, each aligned with a specific stage of the paradigm and designed to preserve scalability and efficiency. Experiments demonstrate that E-Verify significantly improves both decomposition and verification efficiency while maintaining competitive accuracy. These results confirm that the proposed paradigm enables scalable and fine-grained factuality verification with minimal performance trade-offs.

Large language models (LLMs) enhance security through alignment when widely used, but remain susceptible to jailbreak attacks capable of producing inappropriate content. Jailbreak detection methods show promise in mitigating jailbreak attacks through the assistance of other models or multiple model inferences. However, existing methods entail significant computational costs. In this paper, we first present a finding that the difference in output distributions between jailbreak and benign prompts can be employed for detecting jailbreak prompts. Based on this finding, we propose a Free Jailbreak Detection (FJD) which prepends an affirmative instruction to the input and scales the logits by temperature to distinguish between jailbreak and benign prompts through the confidence of the first token. Furthermore, we enhance the detection performance of FJD through the integration of virtual instruction learning. Extensive experiments on aligned LLMs show that our FJD can effectively detect jailbreak prompts with almost no additional computational costs during LLM inference.

Large reasoning models (LRMs) achieve remarkable performance via long reasoning chains, but often incur excessive computational overhead due to redundant reasoning, especially on simple tasks. In this work, we systematically quantify the upper bounds of LRMs under both Long-Thinking and No-Thinking modes, and uncover the phenomenon of “Internal Self-Recovery Mechanism” where models implicitly supplement reasoning during answer generation. Building on this insight, we propose Adaptive Self-Recovery Reasoning (ASRR), a framework that suppresses unnecessary reasoning and enables implicit recovery. By introducing accuracy-aware length reward regulation, ASRR adaptively allocates reasoning effort according to problem difficulty, achieving high efficiency with negligible performance sacrifice. Experiments across multiple benchmarks and models show that, compared with GRPO, ASRR reduces reasoning budget by up to 32.5% (1.5B) and 25.7% (7B) with minimal accuracy loss (1.2% and 0.6% pass@1), and significantly boosts harmless rates on safety benchmarks (up to +21.7%). Our results highlight the potential of ASRR for enabling efficient, adaptive, and safer reasoning in LRMs.

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Plugging Schema Graph into Multi-Table QA: A Human-Guided Framework for Reducing LLM Reliance
Xixi Wang | Miguel Costa | Jordanka Kovaceva | Shuai Wang | Francisco C. Pereira

Large language models (LLMs) have shown promise in table Question Answering (Table QA). However, extending these capabilities to multi-table QA remains challenging due to unreliable schema linking across complex tables. Existing methods based on semantic similarity work well only on simplified hand-crafted datasets and struggle to handle complex, real-world scenarios with numerous and diverse columns. To address this, we propose a graph-based framework that leverages human-curated relational knowledge to explicitly encode schema links and join paths. Given a natural language query, our method searches on graph to construct interpretable reasoning chains, aided by pruning and sub-path merging strategies to enhance efficiency and coherence. Experiments on both standard benchmarks and a realistic, large-scale dataset demonstrate the effectiveness of our approach. To our knowledge, this is the first multi-table QA system applied to truly complex industrial tabular data.

Large language models (LLMs) based Agents are increasingly pivotal in simulating and understanding complex human systems and interactions. We propose the AI-Agent School (AAS) system, built around a self-evolving mechanism that leverages agents for simulating complex educational dynamics. Addressing the fragmented issues in teaching process modeling and the limitations of agents performance in simulating diverse educational participants, AAS constructs the Zero-Exp strategy, employs a continuous “experience-reflection-optimization” cycle, grounded in a dual memory base comprising experience and knowledge bases and incorporating short-term and long-term memory components. Through this mechanism, agents autonomously evolve via situated interactions within diverse simulated school scenarios. This evolution enables agents to more accurately model the nuanced, multi-faceted teacher-student engagements and underlying learning processes found in physical schools. Experiment confirms that AAS can effectively simulate intricate educational dynamics and is effective in fostering advanced agent cognitive abilities, providing a foundational stepping stone from the “Era of Experience” to the “Era of Simulation” by generating high-fidelity behavioral and interaction data.

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Retrieval-Augmented Machine Translation with Unstructured Knowledge
Jiaan Wang | Fandong Meng | Yingxue Zhang | Jie Zhou

Retrieval-augmented generation (RAG) introduces additional information to enhance large language models (LLMs). In machine translation (MT), previous work typically retrieves in-context examples from paired MT corpora, or domain-specific knowledge from knowledge graphs, to enhance MT models. However, a large amount of world knowledge is organized in unstructured documents, and might not be fully paired across different languages. In this paper, we study retrieval-augmented MT using unstructured documents. Specifically, we build RAGtrans, the first benchmark to train and evaluate LLMs’ retrieval-augmented MT ability. RAGtrans contains 169K MT samples collected via GPT-4o and human translators. Besides, documents from various languages are also provided to supply the knowledge to these samples. Based on RAGtrans, we further propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation. The method uses existing multilingual corpora to create auxiliary training objectives without additional labeling requirements. Extensive experiments show that the method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLEU and 2.1-2.7 COMET scores in En-De. We also conclude the critical difficulties that current LLMs face with this task.

Large Language Models (LLMs), e.g. ChatGPT, have been widely adopted in real-world dialogue applications. However, LLMs’ robustness, especially in handling long complex dialogue sessions, including frequent motivation transfer, sophisticated cross-turn dependency, is criticized all along. Nevertheless, no existing benchmarks can fully reflect these weaknesses. We present MARS-Bench, a Multi-turn Athletic Real-world Scenario Dialogue Benchmark, designed to remedy the gap. MARS-Bench is constructed from play-by-play text commentary so to feature realistic dialogues specifically designed to evaluate three critical aspects of multi-turn conversations: ultra multi-turn, interactive multi-turn, and cross-turn tasks. Extensive experiments on MARS-Bench also reveal that closed-source LLMs significantly outperform open-source alternatives, explicit reasoning significantly boosts LLMs’ robustness on handling long complex dialogue sessions, and LLMs indeed face significant challenge when handling motivation transfer and sophisticated cross-turn dependency. Moreover, we provide mechanistic interpretability on how attention sinks due to special tokens lead to LLMs’ performance degradation when handling long complex dialogue sessions based on attention visualization experiment in Qwen2.5-7B-Instruction.

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UTMath: A Benchmark for Math Evaluation with Unit Test
Bo Yang | Qingping Yang | Yingwei Ma | Runtao Liu

The evaluation of mathematical reasoning capabilities constitutes a critical pathway toward achieving Artificial General Intelligence (AGI). Prevailing benchmarks including MATH and AIME mainly feature single-instantiation problems with fixed numbers, permitting pattern matching instead of principled deductive reasoning and leaving generalization on isomorphic problem variants untested. To address these limitations, we propose the UTMath Benchmark, employing rigorous unit testing methodology that simultaneously quantifies solution accuracy and solution space generality. It comprises 1,053 problems spanning 9 mathematical domains, each accompanied by an average of 68 varied test cases. With answer possibilities per problem on average, UTMath sets new standards for robust reasoning while preventing memorization. UTMath is highly challenging, with the best-performing model, o1-mini, solving only 32.57% of the problems, followed by o1-preview at 27.16%, and GPT-4o at 26.93%. We further propose Reasoning-to-Code Thoughts (RCoT), a prompting strategy that decouples symbolic reasoning from code synthesis. RCoT guides LLMs to first derive formal reasoning structures before generating executable code, producing generalizable solutions rather than situation-specific answers. To help the community push mathematical reasoning further, we release UTMath-Train (70k samples), a companion training set generated under the same protocol. Our benchmark can be accessed via the following link: [UTMath](https://utmathhomepage.github.io/)

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The Green KNIGHT: Green Machine Translation with Knowledge-Distilled, Narrow, Inexpensive, Greedy, Hybrid Transformers
Andreas Guta | Frithjof Petrick | Peter Polák

State-of-the-art neural machine translation (NMT) models deliver high-quality translations at the expense of high inference latency and energy consumption, requiring vast GPU fleets and contributing significantly to carbon emissions. To democratize and “green” NMT, we introduce the Green KNIGHT, a hardware-agnostic collection of recipes to optimize translation speed and energy consumption, with only a moderate trade-off in quality. On high-resource En→De and En→Ko benchmarks, we achieve up to 117× CPU speedup and 98.2% energy savings with 9% relative BLEU decrease. On WMT 2014 En→De and En→Fr benchmarks, we obtain up to 140× speedup with 98.7% energy savings, while staying within 10–12% relative BLEU decrease. Our results demonstrate that efficient and environmentally conscious NMT can be realized through optimizations built on well-understood, off-the-shelf techniques with no custom low-level code required, making our approach immediately deployable in real-world translation pipelines.

With the widespread applications of large language models (LLMs), aligning LLMs with human values has emerged as a critical challenge. For alignment, we always expect LLMs to be honest, positive, harmless, etc. And LLMs appear to be capable of generating the desired outputs after the alignment tuning process, such as the preference tuning via reinforcement learning from human feedback (RLHF). However, it also raises a question about **after alignment, do LLMs genuinely obtain a value distinction between positives and negatives, beyond the generation of positive outputs?** In this work, we start by investigating this question from the token distribution perspective. Our findings reveal that compared to the unaligned versions, LLMs after alignment exhibit a larger logits gap between positive and negative tokens at each generation step, which suggests that LLMs do obtain a value distinction of positives and negatives after alignment. Meanwhile, it also motivates us to achieve alignment by directly constructing such value distinction, thus alleviating the excessive reliance on computational resources required by training-time alignment. Specifically, we propose a representation editing method that intervenes the last hidden representation by amplifying the logits difference between positive and negative tokens (defined as anchor words). Experimental results demonstrate that the proposed method not only achieves effective alignment, but also requires fewer computational resources compared to training-time alignment methods

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MCiteBench: A Multimodal Benchmark for Generating Text with Citations
Caiyu Hu | Yikai Zhang | Tinghui Zhu | Yiwei Ye | Yanghua Xiao

Multimodal Large Language Models (MLLMs) have advanced in integrating diverse modalities but frequently suffer from hallucination. A promising solution to mitigate this issue is to generate text with citations, providing a transparent chain for verification. However, existing work primarily focuses on generating citations for text-only content, leaving the challenges of multimodal scenarios largely unexplored. In this paper, we introduce MCiteBench, the first benchmark designed to assess the ability of MLLMs to generate text with citations in multimodal contexts. Our benchmark comprises data derived from academic papers and review-rebuttal interactions, featuring diverse information sources and multimodal content. Experimental results reveal that MLLMs struggle to ground their outputs reliably when handling multimodal input. Further analysis uncovers a systematic modality bias and reveals how models internally rely on different sources when generating citations, offering insights into model behavior and guiding future directions for multimodal citation tasks.

This paper focuses on the task of generating concept sememe trees to study whether Large Language Models (LLMs) can understand and generate domain conceptual knowledge. Concept sememe tree is a hierarchical structure that represents lexical meaning by combining sememes and their relationships.To this end, we introduce the Neighbor Semantic Structure (NSS) and Chain-of-Thought (CoT) prompting method to evaluate the effectiveness of various LLMs in generating accurate and comprehensive sememe trees across different domains. The NSS, guided by conceptual metaphors, identifies terms that exhibit significant external systematicity within a hierarchical relational network and incorporates them as examples in the learning process of LLMs. Meanwhile, the CoT prompting method guides LLMs through a systematic analysis of a term’s intrinsic core concepts, essential attributes, and semantic relationships, enabling the generation of concept sememe trees.We conduct experiments using datasets drawn from four authoritative terminology manuals and evaluate different LLMs. The experimental results indicate that LLMs possess the capability to capture and represent the conceptual knowledge aspects of domain-specific terms. Moreover, the integration of NSS examples with a structured CoT process allows LLMs to explore domain conceptual knowledge more profoundly, leading to the generation of highly accurate concept sememe trees.

Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable of completing the entire research process. This framework accepts a human-provided research idea and progresses through three stages–literature review, experimentation, and report writing–in order to produce research, including a code repository and a research report, while enabling users to provide feedback and guidance at each stage. We deploy Agent Laboratory with various state-of-the-art LLMs and invite multiple researchers to assess its quality by participating in a survey, providing human feedback to guide the research process, and then evaluate the final paper. We found that: (1) Agent Laboratory driven by o1-preview generates the best research outcomes; (2) The generated machine learning code is able to achieve state-of-the-art performance compared to existing methods; (3) Incorporating human involvement improves the overall quality of research; (4) Agent Laboratory reduces research expenses, achieving an 84% decrease compared to previous autonomous research methods. We hope Agent Laboratory enables researchers to allocate more effort toward creative ideation rather than low-level coding and writing, ultimately accelerating scientific discovery.

Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. In this paper, we introduce a new RAG approach, called HiRAG, which utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems in the indexing and retrieval processes. Our extensive experiments demonstrate that HiRAG achieves significant performance improvements over the state-of-the-art baseline methods.

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Regularized Contrastive Decoding with Hard Negative Samples for LLM Hallucination Mitigation
Haonan Sheng | Dou Hu | Lingwei Wei | Wei Zhou | Songlin Hu

Large language models are prone to generate hallucinations, which can undermine their reliability in high-stakes applications. Some works on LLM hallucination mitigation use the model’s internal signals to contrast different output during inference stage. However, these works often focus on simple forms of hallucinations, and struggle to effectively mitigate hallucinations. To address the issue, this paper exploits hard negative samples to construct a factually weaker model for improving contrastive decoding. We propose a new inference-time method, Regularized Contrastive Decoding (RCD), to capture correct hallucination signals for mitigating hallucinations in LLMs. RCD learns more diverse hallucination patterns via adversarial-aware fine-tuning and mitigates hallucinations via contrastive decoding. Experiments on four hallucination benchmarks demonstrate that our method achieves better LLM hallucination mitigation performance. Further analysis shows RCD generalizes well across different model sizes, task formats, perturbation methods and training data sizes.

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CharacterCraft: Bridging the Literature-Reality Dialogue Gap for Practical Role-Playing Agents
Xuyan Yin | Xinran Yang | Zihao Li | Lixin Zou | Chenliang Li

Recent advancements in large language models (LLMs) have given rise to the emergence of role-playing agents (RPAs). The development of high-quality dialogue datasets is critical for advancing RPAs. However, existing datasets have two main issues: (1) the bias between query distributions and real-world user language usage, and (2) the challenge of ensuring responses accurately reflect character traits.To address these issues, we propose CharacterCraft, a novel framework designed for practical RPAs, comprising a tailored Chinese role-playing dataset and a robust evaluation method. First, we develop a specialized model for Chinese dialogue extraction, achieving state-of-the-art performance. Using this model, we then extract a large amount of character dialogue from novels, ensuring high data quality (issue 2).To mitigate the literature-reality dialogue bias in extracted dialogue (issue 1), we introduce an iterative augmentation-reconstruction method, which revises queries to better align with common language usage. Additionally, we propose a context-aware memory retrieval module for fine-grained alignment with the character and introduce a reference-guided LLM-as-a-judge evaluation method for more reliable assessments by comparing their responses to source material dialogues.Our automated pipeline produces a large-scale high-quality Chinese role-playing dataset with 21,392 samples and 121,418 utterances. The experimental results demonstrate the effectiveness of our framework and reveal the limitations of existing RPAs when faced with diverse scenes.Our repository is at https://github.com/yin214/CharacterCraft.

Personalized alignments towards individual users have been a long-standing goal in large language models (LLMs). We introduce Drift, a novel framework that personalizes LLMs at decoding time with implicit user preferences. Unlike traditional Reinforcement Learning from Human Feedback (RLHF), which relies on vast annotated datasets and expensive gradient updates, Drift operates in a training-free manner by steering a frozen LLM through few-shot preference modeling. Our approach represents user preferences as a composition of interpretable and predefined attributes, and employs a zero-shot rewarding mechanism based on contrastive system prompts. Experiments on both a synthetic persona dataset Perspective and a real human-annotated dataset PRISM demonstrate that Drift achieves performance comparable to standard RLHF methods while using only 50–100 examples. Our results show that Drift delivers not only computationally efficient but also interpretable personalization.

Understanding the core dimensions of conceptual semantics is fundamental to uncovering how meaning is organized in language and the brain. Existing approaches often rely on predefined semantic dimensions that offer only broad representations, overlooking finer conceptual distinctions. This paper proposes a novel framework to investigate the subdimensions underlying coarse-grained semantic dimensions. Specifically, we introduce a Disentangled Continuous Semantic Representation Model (DCSRM) that decomposes word embeddings from large language models into multiple sub-embeddings, each encoding specific semantic information. Using these subembeddings, we identify a set of interpretable semantic subdimensions. To assess their neural plausibility, we apply voxel-wise encoding models to map these subdimensions to brain activation. Our work offers more fine-grained interpretable semantic subdimensions of conceptual meaning. Further analyses reveal that semantic dimensions are structured according to distinct principles, with polarity emerging as a key factor driving their decomposition into subdimensions. The neural correlates of the identified subdimensions support their cognitive and neuroscientific plausibility.

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Identifying Aspects in Peer Reviews
Sheng Lu | Ilia Kuznetsov | Iryna Gurevych

Peer review is central to academic publishing, but the growing volume of submissions is straining the process. This motivates the development of computational approaches to support peer review. While each review is tailored to a specific paper, reviewers often make assessments according to certain *aspects* such as Novelty, which reflect the values of the research community. This alignment creates opportunities for standardizing the reviewing process, improving quality control, and enabling computational support. While prior work has demonstrated the potential of aspect analysis for peer review assistance, the notion of aspect remains poorly formalized. Existing approaches often derive aspects from review forms and guidelines, yet data-driven methods for aspect identification are underexplored. To address this gap, our work takes a bottom-up approach: we propose an operational definition of aspect and develop a data-driven schema for deriving aspects from a corpus of peer reviews. We introduce a dataset of peer reviews augmented with aspects and show how it can be used for community-level review analysis. We further show how the choice of aspects can impact downstream applications, such as LLM-generated review detection. Our results lay a foundation for a principled and data-driven investigation of review aspects, and pave the path for new applications of NLP to support peer review.

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Tree-Structured Non-Autoregressive Decoding for Sequence-to-Sequence Text Generation
Pengyu Ji | Yufei Liu | Xiang Hu | Kewei Tu

Autoregressive Transformer (AT) dominates sequence-to-sequence generation tasks but suffers from high inference latency due to sequential token generation. Non-Autoregressive Transformer (NAT) improves inference efficiency by parallelizing token prediction, yet degrades generation quality. To address these limitations, we propose Tree-structured Non-Autoregressive Decoding (TNAD), a novel paradigm that bridges autoregressive and non-autoregressive decoding. TNAD generates a sentence through a top-down, layer-wise expansion of its constituency parse tree, enabling parallel generation within each layer while preserving contextual dependencies across layers. Experimental results on machine translation and paraphrase generation demonstrate that TNAD outperforms AT in efficiency and NAT in generation quality, thus offering a new alternative to AT and NAT in the trade-off between efficiency and quality. Our code is publicly available at https://github.com/jipy0222/TNAD.

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Towards More Efficient Post-training via Fourier Domain Adapter Framework
Yijia Fan | Jusheng Zhang | Keze Wang

We introduce Fourier Domain Adapter (FDA), a novel and parameter-efficient framework for fine-tuning large-scale pre-trained language models. FDA reparameterizes the core projection operation of the adapter module directly in the Fourier domain. This involves transforming the input features via discrete Fourier transform (DFT), applying sparse learnable complex modulations in frequency space, and then back-transforming via inverse DFT, supplemented by highly compact auxiliary linear layers. This approach significantly reduces the number of trainable parameters while enhancing the model’s ability to capture salient frequency-based semantic information. Comprehensive experiments on GLUE, E2E NLG, and instruction tuning benchmarks show that our FDA consistently outperforms existing parameter-efficient fine-tuning (PEFT) methods. It can achieve better performance with nearly 100x fewer training parameters than traditional fine-tuning methods such as LoRA and AdapterH. Our results demonstrate that FDA is a robust and efficient solution for developing efficient and powerful language models.

Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial information for question answering. Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing, which typically retrieves knowledge strictly necessary for answer generation, thus often suffer from low coverage due to rigid schema requirements and semantic ambiguity. We present KERAG, a novel KG-based RAG pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information. Our retrieval-filtering-summarization approach, combined with fine-tuned LLMs for Chain-of-Thought reasoning on knowledge sub-graphs, reduces noises and improves QA for both simple and complex questions. Experiments demonstrate that KERAG surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%.

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Not All Features Deserve Attention: Graph-Guided Dependency Learning for Tabular Data Generation with Language Models
Zheyu Zhang | Shuo Yang | Bardh Prenkaj | Gjergji Kasneci

Large Language Models (LLMs) have shown strong potential for tabular data generation by modeling textualized feature-value pairs. However, tabular data inherently exhibits sparse feature-level dependencies, where many feature interactions are structurally insignificant. This creates a fundamental mismatch as LLMs’ self-attention mechanism inevitably distributes focus across all pairs, diluting attention on critical relationships, particularly in datasets with complex dependencies or semantically ambiguous features. To address this limitation, we propose GraDe (Graph-Guided Dependency Learning), a novel method that explicitly integrates sparse dependency graphs into LLMs’ attention mechanism. GraDe employs a lightweight dynamic graph learning module guided by externally extracted functional dependencies, prioritizing key feature interactions while suppressing irrelevant ones. Our experiments across diverse real-world datasets demonstrate that GraDe outperforms existing LLM-based approaches by up to 12% on complex datasets while achieving competitive results with state-of-the-art approaches in synthetic data quality. Our method is minimally intrusive yet effective, offering a practical solution for structure-aware tabular data modeling with LLMs.

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CCG: Rare-Label Prediction via Neural SEM–Driven Causal Game
Yijia Fan | Jusheng Zhang | Kaitong Cai | Jing Yang | Keze Wang

Multi-label classification (MLC) faces persistent challenges from label imbalance, spurious correlations, and distribution shifts, especially in rare label prediction. We propose the Causal Cooperative Game (CCG) framework, which models MLC as a multi-player cooperative process. CCG integrates explicit causal discovery via Neural Structural Equation Models, a counterfactual curiosity reward to guide robust feature learning, and a causal invariance loss to ensure generalization across environments, along with targeted rare label enhancement. Extensive experiments on benchmark datasets demonstrate that CCG significantly improves rare label prediction and overall robustness compared to strong baselines. Ablation and qualitative analyses further validate the effectiveness and interpretability of each component. Our work highlights the promise of combining causal inference and cooperative game theory for more robust and interpretable multi-label learning.

While text-based emotion recognition methods have achieved notable success, real-world dialogue systems often demand a more nuanced emotional understanding than any single modality can offer. Multimodal Emotion Recognition in Conversations (MERC) has thus emerged as a crucial direction for enhancing the naturalness and emotional understanding of human-computer interaction. Its goal is to accurately recognize emotions by integrating information from various modalities such as text, speech, and visual signals. This survey offers a systematic overview of MERC, including its motivations, core tasks, representative methods, and evaluation strategies. We further examine recent trends, highlight key challenges, and outline future directions. As interest in emotionally intelligent systems grows, this survey provides timely guidance for advancing MERC research.

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When Allies Turn Foes: Exploring Group Characteristics of LLM-Based Multi-Agent Collaborative Systems Under Adversarial Attacks
Jiahao Zhang | Baoshuo Kan | Tao Gong | Fu Lee Wang | Tianyong Hao

This paper investigates the group characteristics in multi-agent collaborative systems under adversarial attacks. Adversarial agents are tasked with generating counterfactual answers to a given collaborative problem, while collaborative agents normally interact with other agents to solve the given problem. To simulate real-world collaboration scenarios as closely as possible, we evaluate the collaborative system in three different collaboration scenarios and design three different communication strategies and different group structures. Furthermore, we explored several methods to mitigate adversarial attacks, all of which have been proven effective through our experiments. To quantify the robustness of collaborative systems against such attacks, a novel metric, System Defense Index (SDI), is introduced. Finally, we conducted an in-depth analysis from the perspective of group dynamics on how adversarial agents affect multi-agent collaborative systems, which reveals similarities between the agent collaboration process and human collaboration process. The code will be made available after publication.

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EditID: Training-Free Editable ID Customization for Text-to-Image Generation
Guandong Li | Zhaobin Chu

We propose EditID, a training-free approach based on the DiT architecture, which achieves highly editable customized IDs for text to image generation. Existing text-to-image models for customized IDs typically focus more on ID consistency while neglecting editability. It is challenging to alter facial orientation, character attributes, and other features through prompts. EditID addresses this by deconstructing the text-to-image model for customized IDs into an image generation branch and a character feature branch. The character feature branch is further decoupled into three modules: feature extraction, feature fusion, and feature integration. By introducing a combination of mapping features and shift features, along with controlling the intensity of ID feature integration, EditID achieves semantic compression of local features across network depths, forming an editable feature space. This enables the successful generation of high-quality images with editable IDs while maintaining ID consistency, achieving excellent results in the IBench evaluation, which is an editability evaluation framework for the field of customized ID text-to-image generation that quantitatively demonstrates the superior performance of EditID. EditID is the first text-to-image solution to propose customizable ID editability on the DiT architecture, meeting the demands of long prompts and high-quality image generation.

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OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration
Jusheng Zhang | Yijia Fan | Kaitong Cai | Xiaofei Sun | Keze Wang

This paper introduces OSC (Orchestrating Cognitive Synergy), a knowledge-aware adaptive collaboration framework designed to enhance cognitive synergy in multi-agent systems with large language models. While prior work has advanced agent selection and result aggregation, efficient linguistic interactions for deep collaboration among expert agents remain a critical bottleneck. OSC addresses this gap as a pivotal intermediate layer between selection and aggregation, introducing Collaborator Knowledge Models (CKM) to enable each agent to dynamically perceive its collaborators’ cognitive states. Through real-time cognitive gap analysis, agents adaptively adjust communication behaviors, including content focus, detail level, and expression style, using learned strategies. Experiments on complex reasoning and problem-solving benchmarks demonstrate that OSC significantly improves task performance and communication efficiency, transforming “parallel-working individuals” into a “deeply collaborative cognitive team”.

Recent researches on video large language models (VideoLLM) predominantly focus on model architectures and training datasets, leaving the interaction format between the user and the model under-explored. In existing works, users often interact with VideoLLMs by using the entire video and a query as input, after which the model generates a response. This interaction format constrains the application of VideoLLMs in scenarios such as live-streaming comprehension where videos do not end and responses are required in a real-time manner, and also results in unsatisfactory performance on time-sensitive tasks that requires localizing video segments. In this paper, we focus on a video-text duet interaction format. This interaction format is characterized by the continuous playback of the video, and both the user and the model can insert their text messages at any position during the video playback. When a text message ends, the video continues to play, akin to the alternative of two performers in a duet. We construct MMDuetIT, a video-text training dataset designed to adapt VideoLLMs to video-text duet interaction format. We also introduce the Multi-Answer Grounded Video Question Answering (MAGQA) task to benchmark the real-time response ability of VideoLLMs. Trained on MMDuetIT, MMDuet demonstrates that adopting the video-text duet interaction format enables the model to achieve significant improvements in various time-sensitive tasks (76% CIDEr on YouCook2 dense video captioning, 90% mAP on QVHighlights highlight detection and 25% R@0.5 on Charades-STA temporal video grounding) with minimal training efforts, and also enable VideoLLMs to reply in a real-time manner as the video plays.

Recently, textual graph-based retrieval-augmented generation (GraphRAG) has gained popularity for addressing hallucinations in large language models when answering domain-specific questions. Most existing studies assume that generated answers should comprehensively integrate all relevant information from the textual graph. However, this assumption may not always hold when certain information needs to be vetted or even blocked (e.g., due to safety concerns). In this paper, we target two sides of textual graph understanding and question answering: (1) normal question Answering (A-side): following standard practices, this task generates accurate responses using all relevant information within the textual graph; and (2) Blocked question answering (B-side): A new paradigm where the GraphRAG model must effectively infer and exclude specific relevant information in the generated response. To address these dual tasks, we propose TAONA, a novel GraphRAG model with two variants: (1) TAONA-A for A-side task, which incorporates a specialized GraphEncoder to learn graph prompting vectors; and (2) TAONA-B for B-side task, employing semi-supervised node classification to infer potential blocked graph nodes. Extensive experiments validate TAONA’s superior performance for both A-side and B-side tasks.

Refusal is a key safety behavior in aligned language models, yet the internal mechanisms driving refusals remain opaque. In this work, we conduct a mechanistic study of refusal in instruction-tuned LLMs using sparse autoencoders to identify latent features that causally mediate refusal behaviors. We apply our method to two open-source chat models and intervene on refusal-related features to assess their influence on generation, validating their behavioral impact across multiple harmful datasets. This enables a fine-grained inspection of how refusal manifests at the activation level and addresses key research questions such as investigating upstream-downstream latent relationship and understanding the mechanisms of adversarial jailbreaking techniques. We also establish the usefulness of refusal features in enhancing generalization for linear probes to out-of-distribution adversarial samples in classification tasks.

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Where Did That Come From? Sentence-Level Error-Tolerant Attribution
Ori Ernst | Aviv Slobodkin | Meng Cao | Sihui Wei | Jackie CK Cheung

Attribution is the process of identifying which parts of the source support a generated output. While attribution can help users verify content and assess faithfulness, existing task definitions typically exclude unsupported or hallucinated content leaving them unattributed, overlooking the potential to increase faithfulness certainty, locate the error, and fix it easier.In this paper, we propose a new definition for sentence-level error-tolerant attribution, which extends attribution to include incorrect or hallucinated content. We introduce a benchmark for this task and evaluate a range of models on it. Our results show that sentence-level error-tolerant attribution improves the quality of both automatic and manual faithfulness evaluations, reducing annotation time by 30% in long-document settings, and facilitates hallucination fixing. We also find that unfaithful outputs are often linked to sentences that appear later in the source or contain non-literal language, pointing to promising avenues for hallucination mitigation. Our approach offers a better user experience along with improved faithfulness evaluation, with better understanding of model behavior.

In Embedding Based Retrieval (EBR), Approximate Nearest Neighbor (ANN) algorithms are widely adopted for efficient large-scale search. However, recent studies reveal a query out-of-distribution (OOD) issue, where query and base embeddings follow mismatched distributions, significantly degrading ANN performance. In this work, we empirically verify the generality of this phenomenon and provide a quantitative analysis. To mitigate the distributional gap, we introduce a distribution regularizer into the encoder training objective, encouraging alignment between query and base embeddings. Extensive experiments across multiple datasets, encoders, and ANN indices show that our method consistently improves retrieval performance.

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Can LLMs Find a Needle in a Haystack? A Look at Anomaly Detection Language Modeling
Leslie Barrett | Vikram Sunil Bajaj | Robert John Kingan

Anomaly detection (AD), also known as Outlier Detection, is a longstanding problem in machine learning, which has recently been applied to text data. In these datasets, a textual anomaly is a part of the text that does not fit the overall topic of the text. Some recent approaches to textual AD have used transformer models, achieving positive results but with trade-offs in pre-training time and inflexibility with respect to new domains. Others have used linear models which are fast and more flexible but not always competitive on certain datasets. We introduce a new approach based on Large Pre-trained Language Models in three modalities. Our findings indicate that LLMs beat baselines when AD is presented as an imbalanced classification problem regardless of the concentration of anomalous samples. However, their performance is markedly worse on unsupervised AD, suggesting that the concept of “anomaly” may somehow elude the LLM reasoning process.

Large Multimodal Models (LMMs) have demonstrated strong performance on vision-language benchmarks, yet current evaluations predominantly focus on single-image reasoning. In contrast, real-world scenarios always involve understanding sequences of images. A typical scenario is comic strips understanding, which requires models to perform nuanced visual reasoning beyond surface-level recognition. To address this gap, we introduce STRIPCIPHER , a benchmark designed to evaluate the model ability on understanding implicit narratives in silent comics. STRIPCIPHER is a high-quality, human-annotated dataset featuring fine-grained annotations and comprehensive coverage of varying difficulty levels. It comprises three tasks: visual narrative comprehension, contextual frame prediction, and temporal narrative reordering. % , covering various difficulty. Notably, evaluation results on STRIPCIPHER reveals a significant gap between current LMMs and human performance—e.g., GPT-4o achieves only 23.93% accuracy in the reordering task, 56.07% below human levels. These findings underscore the limitations of current LMMs in implicit visual narrative understanding and highlight opportunities for advancing sequential multimodal reasoning.

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Enhancing Multi-Agent Debate System Performance via Confidence Expression
Zijie Lin | Bryan Hooi

Generative Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks. Recent research has introduced Multi-Agent Debate (MAD) systems, which leverage multiple LLMs to simulate human debate and thereby improve task performance. However, while some LLMs may possess superior knowledge or reasoning capabilities for specific tasks, they often struggle to clearly communicate this advantage during debates, in part due to a lack of confidence expression. Moreover, inappropriate confidence expression can cause agents in MAD systems to either stubbornly maintain incorrect beliefs or converge prematurely on suboptimal answers, ultimately reducing debate effectiveness and overall system performance. To address these challenges, we propose incorporating confidence expression into MAD systems to allow LLMs to explicitly communicate their confidence levels. To validate this approach, we develop ConfMAD, a MAD framework that integrates confidence expression throughout the debate process. Experimental results demonstrate the effectiveness of our method, and we further analyze how confidence influences debate dynamics, offering insights into the design of confidence-aware MAD systems.

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The Face of Persuasion: Analyzing Bias and Generating Culture-Aware Ads
Aysan Aghazadeh | Adriana Kovashka

Text-to-image models are appealing for customizing visual advertisements and targeting specific populations. We investigate this potential by examining the demographic bias within ads for different ad topics, and the disparate level of persuasiveness (judged by models) of ads that are identical except for gender/race of the people portrayed. We also experiment with a technique to target ads for specific countries.

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SIFT: Grounding LLM Reasoning in Contexts via Stickers
Zihao Zeng | Xuyao Huang | Boxiu Li | Zhijie Deng

This paper identifies that misinterpreting the context can be a significant issue during the reasoning process of large language models, spanning from smaller models like Llama3.2-3B-Instruct to cutting-edge ones like DeepSeek-R1. We introduce a novel, post-training approach called **Stick to the Facts (SIFT)** to tackle this. SIFT leverages increasing inference-time compute to ground LLM reasoning in contexts. At the core of SIFT lies the Sticker, which is generated by the model itself to explicitly emphasize the key information within the context. Given the Sticker, SIFT generates two predictions—one from the Sticker alone and one from the query augmented with the Sticker. If they differ, the Sticker is sequentially refined via forward optimization (to better align the extracted facts with the query) and inverse generation (to conform with the model’s inherent tendencies) for more faithful reasoning outcomes. Studies across diverse models (from 3B to 100B+) and benchmarks (e.g., MATH, AIME) reveal consistent performance improvements. Notably, SIFT improves the pass@1 accuracy of DeepSeek-R1 on AIME2024 from 78.33% to **85.67%** and that on AIME2025 from 69.8% to **77.33%**. Code will be public after acceptance.

Existing work has shown that o1-level performance can be achieved with limited data distillation, but most existing methods focus on unidirectional supervised fine-tuning (SFT), overlooking the intricate interplay between diverse reasoning patterns. In this paper, we construct r1k, a high-quality reverse reasoning dataset derived by inverting 1,000 forward examples from s1k, and examine how SFT and Direct Preference Optimization (DPO) affect alignment under bidirectional reasoning objectives. SFT on r1k yields a 1.6%–6.8% accuracy improvement over s1k across evaluated benchmarks. However, naively mixing forward and reverse data during SFT weakens the directional distinction. Although DPO can partially recover this distinction, it also suppresses less preferred reasoning paths by shifting the probability mass toward irrelevant outputs. These findings suggest that mixed reasoning data introduce conflicting supervision signals, underscoring the need for robust and direction-aware alignment strategies. Our code and data are available at: https://github.com/16demi/ReasonAlign-analysis.

Unlearning aims to remove copyrighted, sensitive, or private content from large language models (LLMs) without a full retraining. In this work, we develop a multi-task unlearning benchmark LUME that features three tasks: (1) unlearn synthetically generated creative short novels, (2) unlearn synthetic biographies with sensitive information, and (3) unlearn a collection of public biographies. We further release two fine-tuned LLMs of 1B and 7B parameter sizes as the target models. We conduct detailed evaluations of several recently-proposed algorithms and present results on carefully crafted metrics to understand their behavior and limitations.

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How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages
Siyang Wu | Zhewei Sun

Slang is a commonly used type of informal language that poses a daunting challenge to NLP systems. Recent advances in large language models (LLMs), however, have made the problem more approachable. While LLM agents are becoming more widely applied to intermediary tasks such as slang detection and slang interpretation, their generalizability and reliability are heavily dependent on whether these models have captured structural knowledge about slang that align well with human attested slang usages. To answer this question, we contribute a systematic comparison between human and machine-generated slang usages. Our evaluative framework focuses on three core aspects: 1) Characteristics of the usages that reflect systematic biases in how machines perceive slang, 2) Creativity reflected by both lexical coinages and word reuses employed by the slang usages, and 3) Informativeness of the slang usages when used as gold-standard examples for model distillation. By comparing human-attested slang usages from the Online Slang Dictionary (OSD) and slang generated by GPT-4o and Llama-3, we find significant biases in how LLMs perceive slang. Our results suggest that while LLMs have captured significant knowledge about the creative aspects of slang, such knowledge does not align with humans sufficiently to enable LLMs for extrapolative tasks such as linguistic analyses.

While chains-of-thought (CoT) have advanced complex reasoning in multimodal large language models (MLLMs), existing methods remain confined to text or static visual domains, often faltering in dynamic spatial reasoning tasks. To bridge this gap, we present GRASSLAND, a novel maze navigation benchmark designed to evaluate dynamic spatial reasoning. Our experiments show that augmenting textual reasoning chains with dynamic visual drafts, overlaid on input images, significantly outperforms conventional approaches, offering new insights into spatial reasoning in evolving environments. To generalize this capability, we propose D2R (Dynamic Draft-Augmented Reasoning), a training-free framework that seamlessly integrates textual CoT with corresponding visual drafts into MLLMs. Extensive evaluations demonstrate that D2R consistently enhances performance across diverse tasks, establishing a robust baseline for dynamic spatial reasoning without requiring model fine-tuning.

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MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework
Md Shahidul Salim | Lian Fu | Arav Adikesh Ramakrishnan | Zonghai Yao | Hong Yu

We present MedCOD (Medical Chain-of-Dictionary), a hybrid framework designed to improve English-to-Spanish medical translation by integrating domain-specific structured knowledge into large language models (LLMs). MedCOD integrates domain-specific knowledge from both the Unified Medical Language System (UMLS) and the LLM-as-Knowledge-Base (LLM-KB) paradigm to enhance structured prompting and fine-tuning. We constructed a parallel corpus of 2,999 English-Spanish MedlinePlus articles and a 100-sentence test set annotated with structured medical contexts. Four open-source LLMs (Phi-4, Qwen2.5-14B, Qwen2.5-7B, and LLaMA-3.1-8B) were evaluated using structured prompts that incorporated multilingual variants, medical synonyms, and UMLS-derived definitions, combined with LoRA-based fine-tuning. Experimental results demonstrate that MedCOD significantly improves translation quality across all models. For example, Phi-4 with MedCOD and fine-tuning achieved BLEU 44.23, chrF++ 28.91, and COMET 0.863, surpassing strong baseline models like GPT-4o and GPT-4o-mini. Ablation studies confirm that both MedCOD prompting and model adaptation independently contribute to performance gains, with their combination yielding the highest improvements. These findings highlight the potential of structured knowledge integration to enhance LLMs for medical translation tasks.

Patients must possess the knowledge necessary to actively participate in their care. To this end, we developed NoteAid-Chatbot, a conversational AI designed to help patients better understand their health through a novel framework of learning as conversation. We introduce a new learning paradigm that leverages a multi-agent large language model (LLM) and reinforcement learning (RL) framework—without relying on costly human-generated training data. Specifically, NoteAid-Chatbot was built on a lightweight 3-billion-parameter LLaMA 3.2 model using a two-stage training approach: initial supervised fine-tuning on conversational data synthetically generated using medical conversation strategies, followed by RL with rewards derived from patient understanding assessments in simulated hospital discharge scenarios. Our evaluation, which includes comprehensive human-aligned assessments and case studies, demonstrates that NoteAid-Chatbot exhibits key emergent behaviors critical for patient education—such as clarity, relevance, and structured dialogue—even though it received no explicit supervision for these attributes. Our results show that even simple Proximal Policy Optimization (PPO)-based reward modeling can successfully train lightweight, domain-specific chatbots to handle multi-turn interactions, incorporate diverse educational strategies, and meet nuanced communication objectives. Our Turing test demonstrates that NoteAid-Chatbot surpasses non-expert human. Although our current focus is on healthcare, the framework we present illustrates the feasibility and promise of applying low-cost, PPO-based RL to realistic, open-ended conversational domains—broadening the applicability of RL-based alignment methods.

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A Knapsack by Any Other Name: Presentation impacts LLM performance on NP-hard problems
Alex Duchnowski | Ellie Pavlick | Alexander Koller

To investigate the effect of problem presentation on LLMs’ ability to solve optimization problems, we introduce the dataset of Everyday Hard Optimization Problems (EHOP), a collection of NP-hard problems expressed in natural language. EHOP includes problem formulations that could be found in computer science textbooks (e.g., graph coloring), versions that are dressed up as problems that could arise in real life (e.g., party planning), and variants with inverted rules. We find that state-of-the-art LLMs, across multiple prompting strategies, systematically solve textbook problems more accurately than their real-life and inverted counterparts. While reasoning models are more capable, they nonetheless show high variance across problem presentations, suggesting they lack a truly robust reasoning mechanism. We argue that this constitutes evidence that LLMs are still heavily dependent on what was seen in training and struggle to generalize to novel problems.

As the use of large language model (LLM) agents continues to grow, their safety vulnerabilities have become increasingly evident. Extensive benchmarks evaluate various aspects of LLM safety by defining the safety relying heavily on general standards, overlooking user-specific standards. However, safety standards for LLM may vary based on a user-specific profiles rather than being universally consistent across all users. This raises a critical research question: Do LLM agents act safely when considering user-specific safety standards? Despite its importance for safe LLM use, no benchmark datasets currently exist to evaluate the user-specific safety of LLMs. To address this gap, we introduce U-SafeBench, a benchmark designed to assess user-specific aspect of LLM safety. Our evaluation of 20 widely used LLMs reveals current LLMs fail to act safely when considering user-specific safety standards, marking a new discovery in this field. To address this vulnerability, we propose a simple remedy based on chain-of-thought, demonstrating its effectiveness in improving user-specific safety.

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Jailbreak Attack Initializations as Extractors of Compliance Directions
Amit LeVi | Rom Himelstein | Yaniv Nemcovsky | Avi Mendelson | Chaim Baskin

Safety-aligned LLMs respond to prompts with either compliance or refusal, each corresponding to distinct directions in the model’s activation space. Recent studies have shown that initializing attacks via self-transfer from other prompts significantly enhances their performance. However, the underlying mechanisms of these initializations remain unclear, and attacks utilize arbitrary or hand-picked initializations. This work presents that each gradient-based jailbreak attack and subsequent initialization gradually converge to a single compliance direction that suppresses refusal, thereby enabling an efficient transition from refusal to compliance. Based on this insight, we propose CRI, an initialization framework that aims to project unseen prompts further along compliance directions. We demonstrate our approach on multiple attacks, models, and datasets, achieving an increased attack success rate (ASR) and reduced computational overhead, highlighting the fragility of safety-aligned LLMs.

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Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction
Xinmeng Hou | Lingyue Fu | Chenhao Meng | Kounianhua Du | Hai Hu

Aspect-Opinion Pair Extraction (AOPE) and Aspect Sentiment Triplet Extraction (ASTE) have drawn growing attention in NLP. However, most existing approaches extract aspects and opinions independently, optionally adding pairwise relations, often leading to error propagation and high time complexity. To address these challenges and being inspired by transition-based dependency parsing, we propose the first transition-based model for AOPE and ASTE that performs aspect and opinion extraction jointly, which also better captures position-aware aspect-opinion relations and mitigates entity-level bias. By integrating contrastive-augmented optimization, our model delivers more accurate action predictions and jointly optimizes separate subtasks in linear time. Extensive experiments on four commonly used ASTE/AOPE datasets show that, our proposed transition-based model outperform previous models on two out of the four datasets when trained on a single dataset. When multiple training sets are used, our proposed method achieves new state-of-the-art results on all datasets. We show that this is partly due to our model’s ability to benefit from transition actions learned from multiple datasets and domains.Our code is available at https://github.com/Paparare/trans_aste.

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A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare
Manar Aljohani | Jun Hou | Sindhura Kommu | Xuan Wang

The application of large language models (LLMs) in healthcare holds significant promise for enhancing clinical decision-making, medical research, and patient care. However, their integration into real-world clinical settings raises critical concerns around trustworthiness, particularly around dimensions of truthfulness, privacy, safety, robustness, fairness, and explainability. These dimensions are essential for ensuring that LLMs generate reliable, unbiased, and ethically sound outputs. While researchers have recently begun developing benchmarks and evaluation frameworks to assess LLM trustworthiness, the trustworthiness of LLMs in healthcare remains underexplored, lacking a systematic review that provides a comprehensive understanding and future insights. This survey addresses that gap by providing a comprehensive review of current methodologies and solutions aimed at mitigating risks across key trust dimensions. We analyze how each dimension affects the reliability and ethical deployment of healthcare LLMs, synthesize ongoing research efforts and identify critical gaps in existing approaches. We also identify emerging challenges posed by evolving paradigms, such as multi-agent collaboration, multi-modal reasoning, and the development of small open-source medical models. Our goal is to guide future research toward more trustworthy, transparent, and clinically viable LLMs.

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Self-Correction Makes LLMs Better Parsers
Ziyan Zhang | Yang Hou | Chen Gong | Zhenghua Li

Large language models (LLMs) have achieved remarkable success across various natural language processing (NLP) tasks. However, recent studies suggest that they still face challenges in performing fundamental NLP tasks essential for deep language understanding, particularly syntactic parsing. In this paper, we conduct an in-depth analysis of LLM parsing capabilities, delving into the underlying causes of why LLMs struggle with this task and the specific shortcomings they exhibit. We find that LLMs may be limited in their ability to fully leverage grammar rules from existing treebanks, restricting their capability to generate syntactic structures. To help LLMs acquire knowledge without additional training, we propose a self-correction method that leverages grammar rules from existing treebanks to guide LLMs in correcting previous errors. Specifically, we automatically detect potential errors and dynamically search for relevant rules, offering hints and examples to guide LLMs in making corrections themselves. Experimental results on three datasets using various LLMs demonstrate that our method significantly improves performance in both in-domain and cross-domain settings.

The use of large language models (LLMs) as judges, particularly in preference comparisons, has become widespread, but this reveals a notable bias towards longer responses, undermining the reliability of such evaluations. To better understand such bias, we propose to decompose the preference evaluation metric, specifically the win rate, into two key components: desirability and information mass, where the former is length-independent and related to trustworthiness such as correctness, toxicity, and consistency, and the latter is length-dependent and represents the amount of information in the response. We empirically demonstrated the decomposition through controlled experiments and found that response length impacts evaluations by influencing information mass. To derive a reliable evaluation metric that assesses content quality without being confounded by response length, we propose AdapAlpaca, a simple yet effective adjustment to win rate measurement. Specifically, AdapAlpaca ensures a fair comparison of response quality by aligning the lengths of reference and test model responses under equivalent length intervals.

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Investigating Controversy Framing across Topics on Social Media
Maxwell Weinzierl | Sanda M. Harabagiu

Controversial discourse is abundant on social media. Understanding how controversial problems are framed in online discourse is crucial for gaining insights into public opinion formation and for addressing misinformation and polarization. This paper presents a novel method for discovering and articulating framing of controversial problems, enabling the investigation of how controversy is framed across several diverse topics. The promising results, made possible by recent advances in Large Language Models, indicate that discovering framings across topics is feasible. The discovered frames offer valuable insights into how and why controversial problems are discussed on social media.

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HEAL: Hybrid Enhancement with LLM-based Agents for Text-attributed Hypergraph Self-supervised Representation Learning
Ruochang Li | Xiao Luo | Zhiping Xiao | Wei Ju | Ming Zhang

This paper studies the problem of text-attributed hypergraph self-supervised representation learning, which aims to generate discriminative representations of hypergraphs without any annotations for downstream tasks. However, real-world hypergraphs could contain incomplete signals, which could deteriorate the representation learning procedure, especially under label scarcity. Towards this end, we introduce a new perspective that leverages large language models to enhance hypergraph self-supervised learning and propose a novel data-centric approach named Hybrid Hypergraph Enhancement with LLM-based Agents (HEAL). The core of our HEAL is to generate informative nodes and hyperedges through multi-round interaction with LLM-based agents. In particular, we first retrieve similar samples for each node to facilitate the node expansion agent for different views. To generate challenging samples, we measure the gradients for each augmented view and select the most informative one using an evaluation agent. From the structural view, we adopt a topology refinement agent to incorporate new hyperedges for the recovery of missing structural signals. The enhanced hypergraphs would be incorporated into a self-supervised learning framework for discriminative representations. Extensive experiments on several datasets validate the effectiveness of our HEAL in comparison with extensive baselines.

While the Mamba architecture demonstrates superior inference efficiency and competitive performance on short-context natural language processing (NLP) tasks, empirical evidence suggests its capacity to comprehend long contexts is limited compared to transformer-based models. In this study, we investigate the long-context efficiency issues of the Mamba models and propose ReMamba, which enhances Mamba’s ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process, incurring minimal additional inference costs overhead. Experimental results on the LongBench and L-Eval benchmarks demonstrate ReMamba’s efficacy, improving over the baselines by 3.2 and 1.6 points, respectively, and attaining performance almost on par with same-size transformer models.

Generative large language models ( LLMs) have achieved remarkable success in various industrial applications, owing to their promising In-Context Learning capabilities. However, the issue of long context in complex tasks poses a significant barrier to their wider adoption, manifested in two main aspects: (i) The excessively long context leads to high costs and inference delays. (ii) A substantial amount of task-irrelevant information introduced by long contexts exacerbates the “lost in the middle” problem. Existing methods compress context by removing redundant tokens using metrics such as self-information or perplexity ( PPL ), which is inconsistent with the objective of retaining the most important tokens when conditioning on a given query. In this study, we introduce information bottleneck theory (IB) to model the problem, offering a novel perspective that thoroughly addresses the essential properties required for context compression. Additionally, we propose a cross-attention-based approach to approximate mutual information in IB, which can be flexibly replaced with suitable alternatives in different scenarios. Extensive experiments on four datasets demonstrate that our method achieves a 25% increase in compression rate compared to the state-of-the-art, while maintaining question answering performance. In particular, the context compressed by our method even outperform the full context in some cases.

The self-attention mechanism is key to the success of transformers in recent large language models (LLMs). However, the quadratic computational cost, O(n²), with respect to the input sequence length n poses a significant obstacle to further improvement and scalability in longer contexts.In this work, we leverage the convolution-like structure of attention matrices to develop an efficient approximation method for attention computation using convolution matrices. We propose a \mathsf{conv} basis system, analogous to the rank basis, and show that any lower triangular matrix can be decomposed as a sum of structured convolution matrices in this basis. We then design a fast algorithm to approximate the attention matrix using a sum of k convolution matrices. This enables us to compute attention during inference via Fast Fourier Transforms (FFT) in O(knd log n) time, where d is the hidden dimension, achieving nearly linear time complexity, n^1+o(1), in practical scenarios where kd = n^o(1). Furthermore, both training forward and backward gradient computations can be performed in n^1+o(1) time as well.We provide theoretical guarantees on runtime and approximation error and conduct preliminary experiments to evaluate the effectiveness of our approach. We hope this new paradigm for accelerating attention computation in transformer models facilitates their application to longer contexts.

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Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs
Kangda Wei | Hasnat Md Abdullah | Ruihong Huang

Large Language Models (LLMs) often exhibit gender bias, resulting in unequal treatment of male and female subjects across different contexts. To address this issue, we propose a novel data generation framework that fosters exploratory thinking in LLMs. Our approach prompts models to generate story pairs featuring male and female protagonists in structurally identical, morally ambiguous scenarios, then elicits and compares their moral judgments. When inconsistencies arise, the model is guided to produce balanced, gender-neutral judgments. These story-judgment pairs are used to fine-tune or optimize the models via Direct Preference Optimization (DPO). Experimental results show that our method significantly reduces gender bias while preserving or even enhancing general model capabilities. We will release the code and generated data.

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Beyond the Textual: Generating Coherent Visual Options for MCQs
Wanqiang Wang | Longzhu He | Wei Zheng

Multiple-choice questions (MCQs) play a crucial role in fostering deep thinking and knowledge integration in education. However, previous research has primarily focused on generating MCQs with textual options, but it largely overlooks the visual options. Moreover, generating high-quality distractors remains a major challenge due to the high cost and limited scalability of manual authoring. To tackle these problems, we propose a Cross-modal Options Synthesis (CmOS), a novel framework for generating educational MCQs with visual options. Our framework integrates Multimodal Chain-of-Thought (MCoT) reasoning process and Retrieval-Augmented Generation (RAG) to produce semantically plausible and visually similar answer and distractor. It also includes a discrimination module to identify content suitable for visual options. Experimental results on test tasks demonstrate the superiority of CmOS in content discrimination, question generation and visual option generation over existing methods across various subjects and educational levels.

Large language models (LLMs) exhibit exceptional capabilities across various tasks but also pose risks by generating harmful content. Existing safety mechanisms, while improving model safety, often lead to overly cautious behavior and fail to fully leverage LLMs’ internal cognitive processes. Inspired by humans’ reflective thinking capability, we first show that LLMs can similarly perform internal assessments about safety in their internal states. Building on this insight, we propose **SafeSwitch**, a dynamic framework that regulates unsafe outputs by utilizing the prober-based internal state monitor that actively detects harmful intentions, and activates a safety head that leads to safer and more conservative responses only when necessary. SafeSwitch reduces harmful outputs by approximately 80% on harmful queries while maintaining strong utility, reaching a Pareto optimal among several methods. Our method is also advantageous over traditional methods in offering more informative, context-aware refusals, and achieves these benefits while only tuning less than 6% of the original parameters. SafeSwitch demonstrates large language models’ capacity for self-awareness and reflection regarding safety, offering a promising approach to more nuanced and effective safety controls.

Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design.

Persona agents, which are LLM agents conditioned to act according to an assigned persona, enable contextually rich and user-aligned interactions across domains like education and healthcare.However, evaluating how faithfully these agents adhere to their personas remains a significant challenge, particularly in free-form settings that demand consistency across diverse, persona-relevant environments.We introduce PersonaGym, the first dynamic evaluation framework for persona agents, and PersonaScore, a human-aligned automatic metric grounded in decision theory that enables comprehensive large-scale evaluation. Our evaluation of 10 leading LLMs across 200 personas and 10,000 questions reveals significant advancement opportunities.For example, GPT-4.1 had the exact same PersonaScore as LLaMA-3-8b despite being a more recent and advanced closed-source model. Importantly, increased model size and complexity do not necessarily enhance persona agent capabilities, underscoring the need for algorithmic and architectural innovation toward faithful, performant persona agents.

Proteins are critical for various molecular functions, relying on their precise tertiary structures. This structure-sequence relationship is complex and degenerate, meaning multiple sequences can fold into a similar structure. The challenges in protein prediction, design, and modification increase with sequence complexity, while research on RNA-protein interactions, especially RNA-binding proteins (RBPs), is gaining importance. Large-scale pre-trained language models (LLMs) have shown promising results in handling biological sequences by treating them as natural language, though integrating spatial structures remains complex due to the need for specialized visual and 3D modeling approaches. We introduce a method to integrate protein 3D structural data within a sequence processing framework, converting 3D coordinates into discrete structure tokens using a VQ-VAE-like network. This simplifies the handling of 3D data, avoiding complex pipelines and facilitating a unified sequence-to-sequence processing model. Our approach demonstrates strong performance across a range of tasks, achieving high sequence recovery in inverse folding and protein-conditioned RNA design. These outstanding results demonstrate significant potential for application in complex biological systems research.

Recent reasoning models show the ability to reflect, backtrack, and self-validate their reasoning, which is crucial in spotting mistakes and arriving at accurate solutions. A natural question that arises is how effectively models can perform such self-reevaluation. We tackle this question by investigating how well reasoning models identify and recover from four types of unhelpful thoughts: uninformative rambling thoughts, thoughts irrelevant to the question, thoughts misdirecting the question as a slightly different question, and thoughts that lead to incorrect answers. We show that models are effective at identifying most unhelpful thoughts but struggle to recover from the same thoughts when these are injected into their thinking process, causing significant performance drops. Models tend to naively continue the line of reasoning of the injected irrelevant thoughts, which showcases that their self-reevaluation abilities are far from a general “meta-cognitive” awareness. Moreover, we observe non/inverse-scaling trends, where larger models struggle more than smaller ones to recover from short irrelevant thoughts, even when instructed to reevaluate their reasoning. We demonstrate the implications of these findings with a jailbreak experiment using irrelevant thought injection, showing that the smallest models are the least distracted by harmful-response-triggering thoughts. Overall, our findings call for improvement in self-reevaluation of reasoning models to develop better reasoning and safer systems.

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From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval
Dohyeon Lee | Yeonseok Jeong | Seung-won Hwang

Chain-of-Thought (CoT) prompting enables complex reasoning in large language models (LLMs), including applications in information retrieval (IR). However, it often leads to overthinking, where models produce excessively long and semantically redundant traces with little or no benefit. We identify two key challenges in IR: redundant trajectories that revisit similar states and misguided reasoning that diverges from user intent. To address these, we propose State Machine Reasoning (SMR), a transition-based reasoning framework composed of discrete actions (REFINE, RERANK, STOP) that support early stopping and fine-grained control. Experiments on the BEIR and BRIGHT benchmarks show that improves retrieval performance (nDCG@10) by 3.4% while reducing token usage by 74.4%. It generalizes across LLMs and retrievers without requiring task-specific tuning, offering a practical alternative to conventional CoT reasoning.

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Locate-then-Merge: Neuron-Level Parameter Fusion for Mitigating Catastrophic Forgetting in Multimodal LLMs
Zeping Yu | Sophia Ananiadou

Although multimodal large language models (MLLMs) have achieved impressive performance, the multimodal instruction tuning stage often causes catastrophic forgetting of the base LLM’s language ability, even in strong models like Llama3. To address this, we propose Locate-then-Merge, a training-free parameter fusion framework that first locates important parameters and then selectively merges them. We further introduce Neuron-Fusion, a neuron-level strategy that preserves the influence of neurons with large parameter shifts—neurons likely responsible for newly acquired visual capabilities—while attenuating the influence of neurons with smaller changes that likely encode general-purpose language skills. This design enables better retention of visual adaptation while mitigating language degradation. Experiments on 13 benchmarks across both language and visual tasks show that Neuron-Fusion consistently outperforms existing model merging methods. Further analysis reveals that our method effectively reduces context hallucination in generation.

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Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities
Qirun Dai | Dylan Zhang | Jiaqi W. Ma | Hao Peng

Selecting appropriate training data is crucial for instruction fine-tuning of large language models (LLMs), which aims to (1) elicit strong capabilities, and (2) achieve balanced performance across different tasks. Influence-based methods show promise in achieving (1), by estimating the contribution of each training example to the model’s predictions, but often struggle with (2). Our systematic investigation reveals that this underperformance can be attributed to an inherent bias, where some tasks intrinsically have greater influence than others. As a result, data selection is often biased towards these tasks, not only hurting the model’s performance on others but also, counterintuitively, harming performance on these high-influence tasks themselves. To address this, we propose BIDS, a Balanced and Influential Data Selection algorithm. BIDS first normalizes influence scores of the training data, and then iteratively chooses the training example with the highest influence on the most underrepresented task. Experiments with both Llama-3 and Mistral-v0.3 on seven benchmarks spanning five diverse capabilities show that BIDS consistently outperforms both state-of-the-art influence-based algorithms and other non-influence-based frameworks. Surprisingly, training on a 15% subset selected by BIDS can even outperform full-dataset training with a much more balanced performance. Our analysis highlights the importance of both instance-level normalization and iterative optimization of selected data for balanced learning of diverse capabilities.

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Diagnosing Moral Reasoning Acquisition in Language Models: Pragmatics and Generalization
Guangliang Liu | Zimo Qi | Xitong Zhang | Lei Jiang | Kristen Johnson

Ensuring that Large Language Models (LLMs) return just responses which adhere to societal values is crucial for their broader application. Prior research has shown that LLMs often fail to perform satisfactorily on tasks requiring moral cognizance, such as ethics-based judgments. While current approaches have focused on fine-tuning LLMs with curated datasets to improve their capabilities on such tasks, choosing the optimal learning paradigm to enhance the ethical responses of LLMs remains an open research debate. In this work, we aim to address this fundamental question: can current learning paradigms enable LLMs to acquire sufficient moral reasoning capabilities? Drawing from distributional semantics theory and the pragmatic nature of moral discourse, our analysis indicates that performance improvements follow a mechanism similar to that of semantic-level tasks, and therefore remain affected by the pragmatic nature of morals latent in discourse, a phenomenon we name the pragmatic dilemma. We conclude that this pragmatic dilemma imposes significant limitations on the generalization ability of current learning paradigms, making it the primary bottleneck for moral reasoning acquisition in LLMs.

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Discourse Heuristics For Paradoxically Moral Self-Correction
Guangliang Liu | Zimo Qi | Xitong Zhang | Kristen Johnson

Moral self-correction has emerged as a promising approach for aligning the output of Large Language Models (LLMs) with human moral values. However, moral self-correction techniques are subject to two primary paradoxes. First, despite empirical and theoretical evidence to support the effectiveness of self-correction, this LLM capability only operates at a superficial level. Second, while LLMs possess the capability of self-diagnosing immoral aspects of their output, they struggle to identify the cause of this moral inconsistency during their self-correction process. To better understand and address these paradoxes, we analyze the discourse constructions in fine-tuning corpora designed to enhance moral self-correction, uncovering the existence of the heuristics underlying effective constructions. We demonstrate that moral self-correction relies on discourse constructions that reflect heuristic shortcuts, and that the presence of these heuristic shortcuts during self-correction leads to inconsistency when attempting to enhance both self-correction and self-diagnosis capabilities jointly. Building on our findings, we propose a method to strengthen moral self-correction through heuristics extracted from curated datasets, underscoring that its generalization is primarily constrained by situational context.

Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like the Model Context Protocol (MCP). This extension could introduce new security vulnerabilities. We present a systematic investigation of LLM vulnerabilities to hidden adversarial prompts through malicious font injection in external resources like webpages, where attackers manipulate code-to-glyph mapping to inject deceptive content which are invisible to users. We evaluate two critical attack scenarios: (1) malicious content relay and (2) sensitive data leakage through MCP-enabled tools. Our experiments reveal that indirect prompts with injected malicious font can bypass LLM safety mechanisms through external resources, achieving varying success rates based on data sensitivity and prompt design. Our research underscores the urgent need for enhanced security measures in LLM deployments when processing external content.

Code large language models (LLMs) enhance programming by understanding and generating code across languages, offering intelligent feedback, bug detection, and code updates through reflection, improving development efficiency and accessibility. While benchmarks (e.g. HumanEval/LiveCodeBench) evaluate code generation and real-world relevance, previous works ignores the scenario of modifying code in repositories. Considering challenges remaining in improving reflection capabilities and avoiding data contamination in dynamic benchmarks, we introduce , a challenging benchmark for evaluating code understanding and generation in multi-file repository contexts, featuring 1,888 rigorously filtered test cases across 6 programming languages to ensure diversity, correctness, and high difficulty. Further, we create , a large-scale, quality-filtered instruction-tuning dataset derived from diverse sources, used to train through a two-turn dialogue process involving code generation and error-driven repair. The leaderboard evaluates over 40 LLMs to reflect the model performance of repository-based code reflection.

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Reinforcement Learning with Supervised Alignment
João Luís Lins | Jia Xu

Supervised fine-tuning (SFT) is a widely used and highly effective method for adapting Large Language Models (LLMs) to specific tasks. However, it often suffers from overfitting, causing models to excel on fine-tuned data but struggle with unseen or rare real-world inputs. While recent methods like Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with AI Feedback (RLAIF) aim to align LLMs with human values and tasks, they face challenges such as the high cost of human labeling or instabilities and biases inherent in using LLMs as judges. To address these issues, we propose a novel approach called Reinforcement Learning from supervised Alignment (RLA), which constructs a supervised alignment to train the reward model for reinforcement learning. Using only 100,000 MS MARCO samples, our method outperforms RLAIF by a relative margin ranging from +5.38% to +131.8%. It also significantly enhances the baseline Llama3 LLM, achieving up to +55% improvement on in-domain tasks and up to +16% on out-of-domain tasks. While RLA slightly underperforms supervised fine-tuning (SFT) on in-domain benchmarks, it surpasses SFT by up to 50 times on out-of-domain and cross-task evaluations, demonstrating strong generalization capabilities.

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EmByte: Decomposition and Compression Learning for Small yet Private NLP
Shenglan Li | Jia Xu | Mengjiao Zhang

Recent breakthroughs in natural language processing (NLP) have come with escalating model sizes and computational costs, posing significant challenges for deployment in real-time and resource-constrained environments. We introduce EMBYTE, a novel byte-level tokenization model that achieves substantial embedding compression while preserving NLP accuracy and enhancing privacy. At the core of EMBYTE is a new Decompose-and-Compress (DeComp) learning strategy that decomposes subwords into fine-grained byte embeddings and then compresses them via neural projection. DeComp enables EMBYTE to be shrunk down to any vocabulary size (e.g., 128 or 256), drastically reducing embedding parameter count by up to 94% compared to subword-based models without increasing sequence length or degrading performance. Moreover, EMBYTE is resilient to privacy threats such as gradient inversion attacks, due to its byte-level many-to-one mapping structure. Empirical results on GLUE, machine translation, sentiment analysis, and language modeling tasks show that EMBYTE matches or surpasses the performance of significantly larger models, while offering improved efficiency. This makes EMBYTE a lightweight and generalizable NLP solution, well-suited for deployment in privacy-sensitive or low-resource environments.

Open-ended text generation faces a critical challenge: balancing coherence with diversity in LLM outputs. While contrastive search-based decoding strategies have emerged to address this trade-off, their practical utility is often limited by hyperparameter dependence and high computational costs. We introduce GUARD, a self-adaptive decoding method that effectively balances these competing objectives through a novel “Glocal” uncertainty-driven framework. GUARD combines global entropy estimates with local entropy deviations to integrate both long-term and short-term uncertainty signals. We demonstrate that our proposed global entropy formulation effectively mitigates abrupt variations in uncertainty, such as sudden overconfidence or high entropy spikes, and provides theoretical guarantees of unbiasedness and consistency. To reduce computational overhead, we incorporate a simple yet effective token-count-based penalty into GUARD. Experimental results demonstrate that GUARD achieves a good balance between text diversity and coherence, while exhibiting substantial improvements in generation speed. In a more nuanced comparison study across different dimensions of text quality, both human and LLM evaluators validated its remarkable performance. Our code is available at https://github.com/YecanLee/GUARD.

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Efficiently Editing Mixture-of-Experts Models with Compressed Experts
Yifei He | Yang Liu | Chen Liang | Hany Hassan Awadalla

Mixture-of-Experts (MoE) models have become a key approach for scaling large language models efficiently by activating only a subset of experts during training and inference. Typically, the number of activated experts presents a trade-off: fewer experts reduce computational costs, while more experts improve performance. Recent studies reveal that not all activated experts contribute equally to model performance, with some providing minimal utility, particularly when finetuning pretrained MoE models for specialized downstream tasks. The co-existence of significant and redundant parameters in experts provides us an opportunity to reduce the number of activated experts while maintaining model performance. In this work, we propose the concept of compressed experts, lightweight modules that serve as compact representations of full experts. Our approach preserves the most important experts while replacing other auxiliary activated experts with compressed experts. The reduction of active parameters significantly lowers inference costs while achieving comparable performance. Extensive experiments on models including Phi-MoE and OLMoE demonstrate that compressed experts recover over 90% of full expert performance across various tasks while reducing more than 30% active parameters and saving 20% in inference costs. This approach enables efficient deployment of MoE models in resource-constrained settings and facilitates scaling to larger models with manageable overhead.

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FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval
Ying Li | Mengyu Wang | Miguel de Carvalho | Sotirios Sabanis | Tiejun Ma

Financial disclosures such as 10-K filings pose challenging retrieval problems because of their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We present Financial Mapping-Guided Enhanced Answer Retrieval, a retrieval framework tailored to financial documents. FinGEAR combines a finance lexicon for Item-level guidance (FLAM), dual hierarchical indices for within-Item search (Summary Tree and Question Tree), and a two-stage cross-encoder reranker. This design aligns retrieval with disclosure structure and terminology, enabling fine-grained, query-aware context selection. Evaluated on full 10-Ks with the FinQA dataset, FinGEAR delivers consistent gains in precision, recall, F1, and relevancy, improving F1 by up to 56.7% over flat RAG, 12.5% over graph-based RAGs, and 217.6% over prior tree-based systems, while also increasing downstream answer accuracy with a fixed reader. By jointly modeling section hierarchy and domain lexicon signals, FinGEAR improves retrieval fidelity and provides a practical foundation for high-stakes financial analysis.

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FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering
Amirhossein Abaskohi | Spandana Gella | Giuseppe Carenini | Issam H. Laradji

Multimodal multihop question answering (MMQA) requires reasoning over images and text from multiple sources, an essential task for many real-world applications. Despite advances in visual question answering, this multihop setting remains underexplored due to a lack of quality datasets. Existing methods focus on single-hop, single-modality, or short texts, limiting real-world applications like interpreting educational documents with long, multimodal content. To fill this gap, we introduce FM2DS, the first framework for creating a high-quality dataset for MMQA. Our approach consists of a 5-stage pipeline that involves acquiring relevant multimodal documents from Wikipedia, synthetically generating high-level questions and answers, and validating them through rigorous criteria to ensure data quality. We evaluate our methodology by training models on our synthesized dataset and testing on two benchmarks: MultimodalQA and WebQA. Our results demonstrate that, with an equal sample size, models trained on our synthesized data outperform those trained on human-collected data by 1.9 in exact match (EM) score on average. Additionally, we introduce M2QA-Bench with 1k samples, the first benchmark for MMQA on long documents, generated using FM2DS and refined by human annotators.

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SQUARE: Unsupervised Retrieval Adaptation via Synthetic Data
Jinsung Yoon | Junhao Zeng | Sercan O Arik

Pre-trained retrieval models often face challenges in zero-shot retrieval for knowledge-based question answering, as different tasks rely on different corpora. We introduce SQUARE (Synthetic QUery-based Adaptive REtrieval), a novel method for corpus-specific unsupervised retrieval customization. SQUARE leverages LLMs to generate grounded synthetic question-answer pairs from the corpus, which are then used to fine-tune the retriever. A filtering mechanism based on the synthetic answers is employed to ensure high quality of tuning data. Extensive experiments on various datasets demonstrate superior performance of SQUARE compared to zero-shot retrieval and other customization methods, highlighting the value of corpus adaptation for effective retrieval.

Recent advancements in multimodal representation learning for electrocardiogram (ECG) have moved onto learning representations by aligning ECG signals with their paired free-text reports. However, current methods often result in suboptimal alignment of ECG signals with their corresponding text reports, thereby limiting diagnostic accuracy. This is primarily due to the complexity and unstructured nature of medical language, which makes it challenging to effectively align ECG signals with the corresponding text reports. Additionally, these methods are unable to handle arbitrary combinations of ECG leads as inputs, which poses a challenge since 12-lead ECGs may not always be available in under-resourced clinical environments.In this work, we propose the **Knowledge-enhanced Multimodal ECG Representation Learning (K-MERL)** framework to address these challenges. K-MERL leverages large language models (LLMs) to extract structured knowledge from free-text reports, enhancing the effectiveness of ECG multimodal learning. Furthermore, we design a lead-aware ECG encoder to capture lead-specific spatial-temporal characteristics of 12-lead ECGs, with dynamic lead masking. This novel encoder allows our framework to handle arbitrary lead inputs, rather than being limited to a fixed set of full 12 leads, which existing methods necessitate.We evaluate K-MERL on six external ECG datasets and demonstrate its superior capability. K-MERL not only outperforms all existing methods in zero-shot classification and linear probing tasks using 12 leads, but also achieves state-of-the-art (SOTA) results in partial-lead settings, with an average improvement of **16%** in AUC score on zero-shot classification compared to previous SOTA multimodal methods. All data and code will be released upon acceptance.

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Seeing Race, Feeling Bias: Emotion Stereotyping in Multimodal Language Models
Mahammed Kamruzzaman | Amanda Cercas Curry | Alba Cercas Curry | Flor Miriam Plaza-del-Arco

Large language models (LLMs) are increasingly used to predict human emotions, but previous studies show that these models reproduce gendered emotion stereotypes. Emotion stereotypes are also tightly tied to race and skin tone (consider for example the trope of the angry black woman), but previous work has thus far overlooked this dimension. In this paper, we address this gap by introducing the first large-scale multimodal study of racial, gender, and skin-tone bias in emotion attribution, revealing how modality (text, images) and their combination shape emotion stereotypes in Multimodal LLMs (MLLMs). We evaluate four open-source MLLMs using 2.1K emotion-related events paired with 400 neutral face images across three different prompt strategies. Our findings reveal varying biases in MLLMs representations of different racial groups: models reproduce racial stereotypes across modalities, with textual cues being particularly noticeable. Models also reproduce colourist trends, with darker skin tones showing more skew. Our research highlights the need for future rigorous evaluation and mitigation strategies that account for race, colorism, and gender in MLLMs.

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AdaptMerge: Inference Time Adaptive Visual and Language-Guided Token Merging for Efficient Large Multimodal Models
Zahidul Islam | Mrigank Rochan

Recent advances in Large Multimodal Models (LMMs) have showcased impressive visual understanding and vision-language reasoning capabilities, yet their computational cost hinders practical deployment, especially in resource-constrained settings. A key bottleneck is the large number of visual tokens generated by its vision encoders, which increases latency and memory demands. Existing token reduction methods often require costly fine-tuning or apply fixed token reduction ratios, ignoring image complexity and vision-language interactions. We propose AdaptMerge, a training-free, inference-time token merging strategy that adaptively reduces visual tokens by leveraging feature diversity and language-guided relevance. By dynamically adjusting to image complexity and ensuring multimodal coherence, AdaptMerge significantly lowers floating-point operations while improving performance. Extensive experiments on Google’s latest Gemma 3 models (4B and 12B parameters) across four challenging benchmarks demonstrate that AdaptMerge outperforms state-of-the-art token reduction techniques, achieving both reduced computational costs and improved performance, thereby providing a practical pathway to more efficient LMMs.

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Federated Retrieval-Augmented Generation: A Systematic Mapping Study
Abhijit Chakraborty | Chahana Dahal | Vivek Gupta

Federated Retrieval-Augmented Generation (Federated RAG) combines Federated Learning (FL),which enables distributed model training without exposing raw data, with Retrieval-Augmented Generation (RAG), which improves the factual accuracy of language models by grounding outputs in external knowledge. As large language models are increasingly deployed in privacy-sensitive domains such as healthcare, finance, and personalized assistance, Federated RAG offers a promising framework for secure, knowledge-intensive natural language processing (NLP). To the best of our knowledge, this paper presents the first systematic mapping study of Federated RAG, covering literature published between 2020 and 2025. Following Kitchenham’s guidelines for evidence-based software engineering, we develop a structured classification of research focuses, contribution types, and application domains. We analyze architectural patterns, temporal trends, and key challenges, including privacy-preserving retrieval, cross-client heterogeneity, and evaluation limitations. Our findings synthesize a rapidly evolving body of research, identify recurring design patterns, and surface open questions, providing a foundation for future work at the intersection of RAG and federated systems.

Pun generation seeks to creatively modify linguistic elements in text to produce humour or evoke double meanings. It also aims to preserve coherence and contextual appropriateness, making it useful in creative writing and entertainment across various media and contexts. This field has been widely studied in computational linguistics, while there are currently no surveys that specifically focus on pun generation. To bridge this gap, this paper provides a comprehensive review of pun generation datasets and methods across different stages, including traditional approaches, deep learning techniques, and pre-trained language models. Additionally, we summarise both automated and human evaluation metrics used to assess the quality of pun generation. Finally, we discuss the research challenges and propose promising directions for future work.

We present a study to benchmark representative watermarking methods in cross-lingual settings. The current literature mainly focuses on the evaluation of watermarking methods for the English language. However, the literature for evaluating watermarking in cross-lingual settings is scarce. This results in overlooking important adversary scenarios in which a cross-lingual adversary could be in, leading to a gray area of practicality over cross-lingual watermarking. In this paper, we evaluate four watermarking methods in four different and vocabulary rich languages. Our experiments investigate the quality of text under different watermarking procedure and the detectability of watermarks with practical translation attack scenarios. Specifically, we investigate practical scenarios that an adversary with cross-lingual knowledge could take, and evaluate whether current watermarking methods are suitable for such scenarios. Finally, from our findings, we draw key insights about watermarking in cross-lingual settings.

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HDiff: Confidence-Guided Denoising Diffusion for Robust Hyper-relational Link Prediction
Xiangfeng Luo | Ruoxin Zheng | Jianqiang Huang | Hang Yu

Although Hyper-relational Knowledge Graphs (HKGs) can model complex facts better than traditional KGs, the Hyper-relational Knowledge Graph Completion (HKGC) is more sensitive to inherent noise, particularly struggling with two prevalent HKG-specific noise types: Intra-fact Inconsistency and Cross-fact Association Noise.To address these challenges, we propose **HDiff**, a novel conditional denoising diffusion framework for robust HKGC that learns to reverse structured noise corruption. HDiff integrates a **Consistency-Enhanced Global Encoder (CGE)** using contrastive learning to enforce intra-fact consistency and a **Context-Guided Denoiser (CGD)** performing iterative refinement. The CGD features dual conditioning leveraging CGE’s global context and local confidence estimates, effectively combatting both noise types. Extensive experiments demonstrate that HDiff substantially outperforms state-of-the-art HKGC methods, highlighting its effectiveness and significant robustness, particularly under noisy conditions.

CLIP’s success has demonstrated that prompt tuning can achieve robust cross-modal semantic alignment for tasks ranging from open-domain recognition to fine-grained classification. However, redundant or weakly relevant feature components introduce noise and incur unnecessary computational costs. In this work, we propose Spotlighter, a lightweight token-selection framework that simultaneously enhances accuracy and efficiency in prompt tuning. Spotlighter evaluates each visual token’s activation from both sample-wise and semantic-wise perspectives and retains only the top-scoring tokens for downstream prediction. A class-specific semantic memory bank of learned prototypes refines this selection, ensuring semantic representativeness and compensating for discarded features. To further prioritize informative signals, we introduce a two-level ranking mechanism that dynamically weights token–prototype interactions. Across 11 few-shot benchmarks, Spotlighter outperforms CLIP by up to 11.19% in harmonic mean accuracy and achieves up to 0.8K additional FPS, with only 21 extra parameters. These results establish Spotlighter as an effective and scalable baseline for prompt tuning.

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Offloaded Reasoning: Efficient Inference for Large Language Models via Modular Reasoning and Refinement
Ishan Jindal | Jayant Taneja | Badrinath Chandana | Vikas Kapur | Sachin Dev Sharma

Large language models (LLMs) demonstrate strong reasoning capabilities but are expensive to run at inference time, limiting their practical deployment. We propose Offloaded Reasoning (OR), a modular strategy where a lightweight model generates intermediate reasoning traces that are then used by a larger model to produce the final answer. We further introduce Offloaded Reasoning with Refinement (ORR), where the large model first edits or improves the reasoning trace before answering. Unlike token-level acceleration methods, OR and ORR operate at the reasoning level and require no retraining of the large model. Experiments on GSM8K and Math500 show that OR achieves up to 8x faster inference than full large-model reasoning with minimal accuracy loss, while ORR recovers or exceeds full accuracy at substantially lower cost. Our results highlight the potential of modular, delegation-based reasoning for building more efficient and adaptable LLM systems.

Recent advances in large reasoning models have enabled complex, step-by-step reasoning but often introduce significant overthinking, resulting in verbose and redundant outputs that hinder efficiency. In this study, we examine whether explicit self-reflection, signaled by tokens such as “Wait” and “Hmm”, is necessary for advanced reasoning. We propose NoWait, a simple yet effective approach that disables explicit self-reflection by suppressing these tokens during inference. Extensive experiments on ten benchmarks across textual, visual, and video reasoning tasks show that NoWait reduces chain-of-thought trajectory length by up to 27%–51% in five R1-style model series, without compromising model utility. NoWait thus offers a plug-and-play solution for efficient and utility-preserving multimodal reasoning.

With the continuous development of language models and the widespread availability of various types of accessible interfaces, large language models (LLMs) have been applied to an increasing number of fields. However, due to the vast amounts of data and computational resources required for model development, protecting the model’s parameters and training data has become an urgent and crucial concern. Due to the revolutionary training and application paradigms of LLMs, many new attacks on language models have emerged in recent years. In this paper, we define these attacks as “reverse engineering” (RE) techniques on LMs and aim to provide an in-depth analysis of reverse engineering of language models. We illustrate various methods of reverse engineering applied to different aspects of a model, while also providing an introduction to existing protective strategies. On the one hand, it demonstrates the vulnerabilities of even black box models to different types of attacks; on the other hand, it offers a more holistic perspective for the development of new protective strategies for models.

Clinical trials are pivotal yet costly processes, often spanning multiple years and requiring substantial expenses, motivating predictive models to identify likely-to-fail drugs early and save resources. Recent approaches leverage deep learning to integrate multimodal data for clinical outcome prediction; however, they rely heavily on manually designed modality-specific encoders, limiting their adaptability to new modalities and ability to effectively share information across modalities. To address these challenges, we propose a multimodal mixture-of-experts (LIFTED) framework. Specifically, LIFTED transforms modality-specific data into natural language descriptions, encoded via unified, noise-resilient encoders. A sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities, extracting consistent representations. Finally, another mixture-of-experts module dynamically integrates these modality representations, emphasizing critical information. Experiments show that LIFTED significantly outperforms baseline methods in predicting clinical trial outcomes across all phases, highlighting the effectiveness of our proposed approach.

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Addition in Four Movements: Mapping Layer-wise Information Trajectories in LLMs
Yao Yan

Arithmetic offers a compact test of whether large language models compute or memorize. We study multi-digit addition in LLaMA-3-8B-Instruct using linear probes and the Logit Lens, and find a consistent four-stage, layer-wise ordering of probe-decodable signal types across depth: (1) early layers encode formula structure (operand/operator layout) while the gold next token is still far from top-1; (2) mid layers expose digit-wise sums and carry indicators; (3) deeper layers express result-level numerical abstractions that support near-perfect digit decoding from hidden states; and (4) near the output, representations align with final sequence generation, with the correct next token reliably ranked first. Across experiments, each signal family becomes linearly decodable with high accuracy (stage-wise peaks typically ≥95% on in-domain multi-digit addition, and up to 99%). Taken together, these observations—in our setting—are consistent with a hierarchical, computation-first account rather than rote pattern matching, and help explain why Logit Lens inspection is most informative mainly in later layers. Code and data are available at https://github.com/YaoToolChest/addition-in-four-movements.git.

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CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning
Jinyuan Feng | ChaoPeng Wei | Tenghai Qiu | Tianyi Hu | Zhiqiang Pu

In parameter-efficient fine-tuning, mixture-of-experts (MoE), which involves specializing functionalities into different experts and sparsely activating them appropriately, has been widely adopted as a promising approach to trade-off between model capacity and computation overhead. However, current MoE variants fall short on heterogeneous datasets, ignoring the fact that experts may learn similar knowledge, resulting in the underutilization of MoE’s capacity. In this paper, we propose Contrastive Representation for MoE (CoMoE), a novel method to promote modularization and specialization in MoE, where the experts are trained along with a contrastive objective by sampling from activated and inactivated experts in top-k routing. We demonstrate that such a contrastive objective recovers the mutual-information gap between inputs and the two types of experts. Experiments on several benchmarks and in multi-task settings demonstrate that CoMoE can consistently enhance MoE’s capacity and promote modularization among the experts.

Parameter-efficient fine-tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), offer an efficient way to adapt large language models with reduced computational costs. However, their performance is limited by the small number of trainable parameters. Recent work combines LoRA with the Mixture-of-Experts (MoE), i.e., LoRA-MoE, to enhance capacity, but two limitations remain in hindering the full exploitation of its potential: 1) the influence of downstream tasks when assigning expert numbers, and 2) the uniform rank assignment across all LoRA experts, which restricts representational diversity.To mitigate these gaps, we propose GuiLoMo, a fine-grained layer-wise expert numbers and ranks allocation strategy with GuidedSelection Vectors (GSVs). GSVs are learned via a prior bilevel optimization process to capture both model- and task-specific needs, and are then used to allocate optimal expert numbers and ranks.Experiments on three backbone models across diverse benchmarks show that GuiLoMo consistently achieves superior or comparable performance to all baselines. Further analysis offers key insights into how expert numbers and ranks vary across layers and tasks, highlighting the benefits of adaptive expert configuration. Our code is available at https://anonymous.4open.science/r/GuiLoMo-034.

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Rotate, Clip, and Partition: Towards W2A4KV4 Quantization by Integrating Rotation and Learnable Non-uniform Quantizer
Euntae Choi | Sumin Song | Woosang Lim | Sungjoo Yoo

We propose Rotate, Clip, and Partition (RCP), a Quantization-Aware Training (QAT) approach that first realizes extreme compression of LLMs with W2A4KV4 (2-bit weight, 4-bit activation, and 4-bit KV-cache) configuration. RCP integrates recent rotation techniques with a novel non-uniform weight quantizer design by theoretically and empirically analyzing the impact of rotation on the non-uniformity of weight distribution. Our weight quantizer, Learnable Direct Partitioning (LDP), introduces learnable parameters to directly learn non-uniform intervals jointly with LLM weights. We also present a GPU kernel supporting GEMV on non-uniform W2A4 as proof of concept. Experiments show that RCP can compress LLaMA-2-7B to W2A4KV4 with a loss of only 2.84 WikiText2 PPL and 5.29 times reduced memory footprint. Furthermore, RCP can quantize challenging mobile-targeted LLaMA-3.2 models and domain-specific WizardCoder-7B and MetaMath-7B with no critical problems such as convergence failure and repetition. Code is available at https://github.com/songsm921/RCP.

Fine-tuning large language models (LLMs) for recommendation in a generative manner has delivered promising results, but encounters significant inference overhead due to autoregressive decoding in the language space. This work explores bypassing language-space decoding by directly matching candidate items with the LLM’s internal thought representations in the latent space, eliminating the time-consuming autoregressive process to reduce computational costs. Towards this, we introduce Light Latent-space Decoding (L2D), an effective and efficient latent-space decoding method. L2D represents user-preferred items by using the hidden states of test sequences reflecting the LLM’s internal thought, and obtains candidate item representations from the hidden states of training sequences labeled with the corresponding candidate items. It then matches the two types of representations to decode items, achieving latent-space decoding. In this way, it enables efficient decoding without altering the LLM’s generative tuning paradigm, thereby preserving performance. Extensive empirical results demonstrate that L2D is more than 10x faster than language-space decoding while maintaining or enhancing performance.

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Forget for Get: A Lightweight Two-phase Gradient Method for Knowledge Editing in Large Language Models
Yanhong Li | Min Yang | Xiping Hu | Chengming Li

Recent studies have highlighted the remarkable knowledge retention capabilities of Large Language Models (LLMs) like GPT-4, while simultaneously revealing critical limitations in maintaining knowledge currency and accuracy. Existing knowledge editing methodologies, designed to update specific factual information without compromising general model performance, often encounter two fundamental challenges: parameter conflict during knowledge overwriting and excessive computational overhead. In this paper, we introduce ForGet (Forget for Get), a novel approach grounded in the principle of “forgetting before learning”. By pinpointing the location within the LLM that corresponds to the target knowledge, we first erase the outdated knowledge and then insert the new knowledge at this precise spot. ForGet is the first work to leverage a two-phase gradient-based process for knowledge editing, offering a lightweight solution that also delivers superior results. Experimental findings show that our method achieves more effective knowledge editing at a lower cost compared to previous techniques across various base models.

Retrieval-augmented generation (RAG) enables large language models (LLMs) to address queries beyond their internal knowledge by integrating domain knowledge in specialized corpus, which necessitates the generation of benchmarks on specific corpus to evaluate RAG systems. However, existing automated generation methods exhibit Weak Applicability and Weak Scalability. Weak Applicability refers to the reliance on metadata from specific corpora for query generation, constraining applicability to other corpora. Weak Scalability is characterized by fixed query content after generation, unable to dynamically increase difficulty, limiting scalability of the query. To overcome these issues, we propose AutoEvolve, an applicable approach for dynamically evolving queries to construct scalable RAG benchmarks. Our approach is grounded in three key innovations: (i) a corpus-agnostic method for constructing the universal entity-document graph; (ii) a suite of evolution operations designed to dynamically update queries; and (iii) a difficulty-guided metric that directs query evolution process. Through experiments on three generated benchmarks, we demonstrate that AutoEvolve evolves queries that are significantly more challenging, paving the way for more applicable and scalable RAG evaluations.

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Temporal Alignment of Time Sensitive Facts with Activation Engineering
Sanjay Govindan | Maurice Pagnucco | Yang Song

Large Language Models (LLMs) are trained on diverse and often conflicting knowledge spanning multiple domains and time periods. Some of this knowledge is only valid within specific temporal contexts, such as answering the question, “Who is the President of the United States in 2022?” Ensuring LLMs generate time-appropriate responses is crucial for maintaining relevance and accuracy. In this work we explore activation engineering as a method for temporally aligning LLMs to improve factual recall without any training. Activation engineering has predominantly been used to steer subjective and qualitative outcomes such as toxicity or behavior. Our research is one of few that uncovers the bounds of activation engineering on objective outcomes. We explore an activation engineering technique to anchor LLaMA 2, LLaMA 3.1, Qwen 2 and Gemma 2 to specific points in time and examine the effects of varying injection layers and prompting strategies. Our experiments demonstrate up to a 44% and 16% improvement in relative and explicit prompting respectively, achieving comparable performance to the fine-tuning method proposed by Zhao et al. (2024). Notably, for LLaMA 2 and LLaMA 3.1 our approach achieves similar results to the fine-tuning baseline while being significantly more computationally efficient and requiring no pre-aligned datasets.

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ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models
Kyungmin Kim | Youngbin Choi | Hyounghun Kim | Dongwoo Kim | Sangdon Park

In this paper, we propose ChronoBias, a novel benchmark for evaluating time-conditional group bias in the time-sensitive knowledge of large language models (LLMs).Our benchmark is constructed via a template-based semi-automated generation method, balancing the quality-quantity trade-off in existing benchmark curation approaches.For knowledge that changes over time, time-conditional group bias exhibits varying patterns across time intervals, evident in both the best- and worst-performing groups and in the bias metric itself.In addition to parametric knowledge bias–which influences group bias across all time intervals–we identify time-sensitivity bias as an additional factor after a model’s knowledge cutoff, accounting for much of the variation in time-conditional group bias over time.Since both biases are irreducible, retrieval-augmented generation (RAG) can be a promising approach, as it can address post-cutoff knowledge and better leverage pretraining knowledge that is underrepresented in the model parameters.While RAG improves both overall performance and group bias, we observe that the disparate patterns of time-conditional group bias still persist.Therefore, through extensive experiments with various model configurations, we illustrate how accurate and fair RAG-based LLMs should behave and provide actionable guidelines toward constructing such ideal models.

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MC²: A Minimum-Coverage and Dataset-Agnostic Framework for Compositional Generalization of LLMs on Semantic Parsing
Ziyao Xu | Zhe Yang | Houfeng Wang

Compositional generalization is one of the important abilities that large language models (LLMs) need to have for semantic parsing. Previous research typically relies on dataset-specific designs or a large number of samples in demonstrations to improve the compositional generalization of LLMs on semantic parsing. We revisit this issue and find that when the number of samples in a demonstration is limited to a theoretical lower bound for achieving compositional generalization (minimum-coverage), current advanced LLMs cannot arbitrarily achieve good compositional generalization generically on different semantic parsing datasets without dataset-specific designs. To solve this problem, we propose Multi-level Component Composition (MC^2), a minimum-coverage and dataset-agnostic framework based on input primitives, which aims to generically help LLMs achieve compositional generalization by selecting and organizing samples from multiple compositional levels that satisfy the primitive coverage. Experiments and analysis show that MC^2 can effectively improve the compositional generalization of LLMs on different semantic parsing datasets in the minimum-coverage setting.

The performance of Large Language Models (LLMs) critically depends on designing effective instructions, which is particularly challenging for black-box LLMs with inaccessible internal states. To this end, we introduce Learning to Instruct, a novel paradigm that formulates instruction optimization as an LLM fine-tuning objective for a white-box “instruction engineer” LLM, leveraging its rich learning capacity and vast pre-trained knowledge to enable efficient and effective instruction optimization. Within this paradigm, we propose Automatic Instruction Optimizer (AIO), a novel framework that fine-tunes a white-box LLM into a capable instruction engineer. AIO learns to optimize task-aware, human-comprehensible instructions by incorporating task nuances and feedback from the task-solving black-box LLM. To overcome the challenges of inaccessible black-box gradients and high API costs, AIO introduces a novel zeroth-order (ZO) gradient approximation mechanism guided by Thompson Sampling (TS), which reuses informative black-box LLM feedback for improved query efficiency. Extensive experiments show that AIO generally outperforms strong baselines in both effectiveness and efficiency, establishing Learning to Instruct as a promising new direction for black-box LLM instruction optimization.

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Enriching Patent Claim Generation with European Patent Dataset
Lekang Jiang | Chengzu Li | Stefan Goetz

Drafting patent claims is time-intensive, costly, and requires professional skill. Therefore, researchers have investigated large language models (LLMs) to assist inventors in writing claims. However, existing work has largely relied on datasets from the United States Patent and Trademark Office (USPTO). To enlarge research scope regarding various jurisdictions, drafting conventions, and legal standards, we introduce EPD, a European patent dataset. EPD presents rich textual data and structured metadata to support multiple patent-related tasks, including claim generation. This dataset enriches the field in three critical aspects. (1) Jurisdictional diversity: Patents from different offices vary in legal and drafting conventions. EPD fills a critical gap by providing a benchmark of European patents to enable more comprehensive evaluation. (2) Quality improvement: EPD offers high-quality granted patents with finalized and legally approved texts, whereas others consist of patent applications that are unexamined or provisional. Experiments show that LLMs fine-tuned on EPD significantly outperform those trained on previous datasets and even GPT-4o in claim quality and cross-domain generalization. (3) Real-world simulation: We propose a difficult subset of EPD to better reflect real-world challenges. Results reveal that all tested LLMs perform substantially worse on challenging samples, which highlights the need for future research.

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StepKE: Stepwise Knowledge Editing for Multi-Hop Question Answering
Jaewook Lee | Dahyun Jung | Heuiseok Lim

Knowledge editing aims to update Large Language Models (LLMs) with new information without costly retraining. However, consistently reflecting these updates in complex multi-hop Question Answering (QA), which demands reasoning over interconnected facts, is challenging. Many existing methods overlook the interplay with pre-existing knowledge, leading to inconsistent edit propagation. To overcome this, we introduce StepKE (Stepwise Knowledge Editing for Multi-hop QA), a novel framework for robustly integrating edited and existing knowledge for coherent multi-hop reasoning. StepKE uniquely decomposes multi-hop questions into sequential single-hop sub-questions, retrieving relevant facts (both edited and pre-existing) from an external knowledge graph for each step. It employs context-aware prompting with prior reasoning history and fine-tuning for precise edit propagation. This systematic integration enables effective stepwise reasoning. Experiments show StepKE generates significantly more accurate and consistent responses than baselines, showcasing strong knowledge editing and integration in multi-hop QA.

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AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmark
Lan Li | Liri Fang | Bertram Ludäscher | Vetle I Torvik

Data cleaning is a time-consuming and error-prone manual process even with modern workflow tools like OpenRefine. Here, we present AutoDCWorkflow, an LLM-based pipeline for automatically generating data-cleaning workflows. The pipeline takes a raw table coupled with a data analysis purpose, and generates a sequence of OpenRefine operations designed to produce a minimal, clean table sufficient to address the purpose. Six operations address common data quality issues including format inconsistencies, type errors, and duplicates.To evaluate AutoDCWorkflow, we create a benchmark with metrics assessing answers, data, and workflow quality for 142 purposes using 96 tables across six topics. The evaluation covers three key dimensions: (1) **Purpose Answer**: can the cleaned table produce a correct answer? (2) **Column (Value)**: how closely does it match the ground truth table? (3) **Workflow (Operations)**: to what extent does the generated workflow resemble the human-curated ground truth? Experiments show that Llama 3.1, Mistral, and Gemma 2 significantly enhance data quality, outperforming the baseline across all metrics. Gemma 2-27B consistently generates high-quality tables and answers, while Gemma 2-9B excels in producing workflows that resemble human annotations.

Graphical user interface (GUI) agents powered by multimodal large language models (MLLMs) have shown greater promise for human-interaction. However, due to the high fine-tuning cost, users often rely on open-source GUI agents or APIs offered by AI providers, which introduces a critical but underexplored supply chain threat: backdoor attacks. In this work, we first unveil that MLLM-powered GUI agents naturally expose multiple interaction-level triggers, such as historical steps, environment states, and task progress. Based on this observation, we introduce AgentGhost, an effective and stealthy framework for red-teaming backdoor attacks. Specifically, we first construct composite triggers by combining goal and interaction levels, allowing GUI agents to unintentionally activate backdoors while ensuring task utility. Then, we formulate backdoor injection as a Min-Max optimization problem that uses supervised contrastive learning to maximize the feature difference across sample classes at the representation space, improving flexibility of the backdoor. Meanwhile, it adopts supervised fine-tuning to minimize the discrepancy between backdoor and clean behavior, enhancing effectiveness and utility. Extensive results show that AgentGhost is effective and generic, with attack accuracy that reaches 99.7% on three attack objectives, and shows stealthiness with only 1% utility degradation. Furthermore, we tailor a defense method against AgentGhost that reduces the attack accuracy to 22.1%.

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Scale Down to Speed Up: Dynamic Data Selection for Reinforcement Learning
Zhuoyue Chen | Jihai Zhang | Ben Liu | Fangquan Lin | Wotao Yin

Optimizing data utilization remains a central challenge in applying Reinforcement Learning (RL) to Large Language Models (LLMs), directly impacting sample efficiency, training stability, and final model performance.Current approaches often rely on massive static datasets, leading to computational inefficiency and redundant gradient updates.In this paper, we propose ScalingRL, a data-centric RL framework that dynamically selects the most informative training samples to optimize RL for mathematical reasoning.Specifically, ScalingRL introduces the Data Effectiveness Score (DES) that quantitatively ranks prompts according to three complementary factors: problem difficulty, Chain-of-Thought complexity, and reward adaptability.Then, ScalingRL employs an adaptive curriculum scheduler that progressively adjusts the overall scale and specific mix of training prompts—balancing exploration of new, challenging data with exploitation of previously learned concepts—thereby tailoring the data distribution to the model’s current learning trajectory and performance.Experimental results demonstrate that ScalingRL achieves comparable performance to full-data training methods while requiring only 1.5K samples instead of 220K, reducing training time from 13 days to just 4 hours on 8×A800 GPUs.

CoT distillation is critical for enhancing small language models’ (SLMs) reasoning by transferring multi-step reasoning capability from the larger teacher models. However, existing work underestimates the importance of rationale quality, focusing primarily on data quantity, which may result in transferring noisy or incorrect information to the student model. To address the above issues, we proposed Model-Oriented Rationale Selection Distillation (MoRSD), which can discern and select high quality rationales for distillation. We further propose a Rationale Difficulty (RD) metric to measure the ability of the student model to generate the correct answer under a given rationale. Compared to the baseline, we achieved 4.6% average accuracy improvement on seven datasets over three tasks, using fewer rationales by controlling their accuracy, diversity, and difficulty. Our results reveal that a small portion of the high quality rationales can enhance the reasoning ability of student models than the entire dataset. Our method promises to be a possible solution for efficient CoT distillation. Our code will be released in https://github.com/Leon221220/MoRSD.

We introduce GeoDANO, a geometric vision-language model (VLM) with a domain-agnostic vision encoder, for solving plane geometry problems. Although VLMs have been employed for solving geometry problems, their ability to recognize geometric features remains insufficiently analyzed. To address this gap, we propose a benchmark that evaluates the recognition of visual geometric features, including primitives such as dots and lines, and relations such as orthogonality. Our preliminary study shows that vision encoders often used in general-purpose VLMs, e.g., OpenCLIP, fail to detect these features and struggle to generalize across domains. To overcome the limitation, we develop GeoCLIP, a CLIP-based model trained on synthetic geometric diagram–caption pairs. Benchmark results show that GeoCLIP outperforms existing vision encoders in recognizing geometric features. We then propose our VLM, GeoDANO, which augments GeoCLIP with a domain adaptation strategy for unseen diagram styles. GeoDANO outperforms specialized methods for plane geometry problems and GPT-4o on MathVerse. The implementation is available at https://github.com/ml-postech/GeoDANO.

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Leveraging 3D Gaussian for Temporal Knowledge Graph Embedding
Jiang Li | Xiangdong Su | Guanglai Gao

Representation learning in knowledge graphs (KGs) has predominantly focused on static data, yet many real-world knowledge graphs are inherently dynamic. For instance, the fact (The CEO of Apple, holds position, Steve Jobs) was valid until 2011, after which it changed, emphasizing the need to incorporate temporal information into knowledge representation. In this paper, we propose 3DG-TE, a novel temporal KG embedding method inspired by 3D Gaussian Splatting, where entities, relations, and timestamps are modeled as 3D Gaussian distributions with learnable structured covariance. This approach optimizes the Gaussian distributions of entities, relations, and timestamps to improve the overall KG representation. To effectively capture temporal-relational interactions, we design structured covariances that form composite transformation operators: relations induce rotational transformations, while timestamps regulate adaptive scaling. We also design a compound scoring function that integrates mean positions and structured covariance, preserving geometric interpretability. Experimental results on three benchmark TKG datasets demonstrate that 3DG-TE outperforms state-of-the-art baselines in temporal link prediction tasks. Theoretical analysis further confirms our model’s ability to capture key relation patterns.

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LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences
Liangqi Yuan | Dong-Jun Han | Christopher Brinton | Sabine Brunswicker

The rise of large language models (LLMs) has made natural language-driven route planning an emerging research area that encompasses rich user objectives. Current research exhibits two distinct approaches: direct route planning using LLM-as-Agent and graph-based searching strategies. However, LLMs in the former approach struggle to handle extensive map data, while the latter shows limited capability in understanding natural language preferences. Additionally, a more critical challenge arises from the highly heterogeneous and unpredictable spatio-temporal distribution of users across the globe. In this paper, we introduce a novel LLM-Assisted route Planning (LLMAP) system that employs an LLM-as-Parser to comprehend natural language, identify tasks, and extract user preferences and recognize task dependencies, coupled with a Multi-Step Graph construction with iterative Search (MSGS) algorithm as the underlying solver for optimal route finding. Our multi-objective optimization approach adaptively tunes objective weights to maximize points of interest (POI) quality and task completion rate while minimizing route distance, subject to three key constraints: user time limits, POI opening hours, and task dependencies. We conduct extensive experiments using 1,000 routing prompts sampled with varying complexity across 14 countries and 27 cities worldwide. The results demonstrate that our approach achieves superior performance with guarantees across multiple constraints.

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ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction
Jeesu Jung | Chanjun Park | Sangkeun Jung

Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence(AI) annotators. However, such approaches rely on instance-wise supervision, incurring substantial annotation cost and limited interpretability. In this paper, we propose **ZEBRA**—a model behavior-wise zero-annotation framework that constructs preference data by leveraging model behavior knowledge derived from benchmark performances.ZEBRA binarizes response pairs by evaluating the quality and similarity of their origin models, entirely bypassing instance-level annotation. This allows scalable, controllable, and cost-effective alignment data generation. Empirical results show that ZEBRA achieves alignment performance comparable to instance-supervised methods, despite requiring no manual or model-based labeling.

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Token Knowledge: A New Perspective For Knowledge in Large Language Models
Jieyong Wang | Chunyao Song | Tingjian Ge

In the era of prosperity of large language models (LLMs), hallucination remains a serious issue hindering LLMs’ expansion and reliability. Predicting the presence (and absence) of certain knowledge in LLMs could aid the hallucination avoidance. However, the token-based generation mode of LLM is different from the knowledge storage structure in the form of triples, which makes it difficult to accurately evaluate the knowledge boundary of LLM. We approach this problem from a novel perspective and, for the first time, introduce the concept of token knowledge in large language models. Consequently, we propose a token knowledge dataset construction method and use the intermediate states during inference to train probes. This allows us to predict if a specific token will appear in the LLM’s generated sequence, without even generating a single token. Our approach unlocks the model’s latent potential, enhancing its accuracy in assessing token knowledge from about 60% to over 90%, with strong out-of-distribution generalization by training on just a few dozen prompts. Finally, we apply KEGT to enhance a state-of-the-art knowledge boundary detection method, achieving improved performance while reducing computational time by over 90%. Furthermore, KEGT enables prevention of hallucinations in certain cases by leveraging its guidance in the token-level knowledge semantic space. Our code is available at https://github.com/CC-2000/KEGT.

Event extraction (EE) is a fundamental task in natural language processing (NLP) that involves identifying and extracting event information from unstructured text. Effective EE in real-world scenarios requires two key steps: selecting appropriate schemas from hundreds of candidates and executing the extraction process.Existing research exhibits two critical gaps: (1) the rigid schema fixation in existing pipeline systems, and (2) the absence of benchmarks for evaluating joint schema matching and extraction.Although large language models (LLMs) offer potential solutions, their schema hallucination tendencies and context window limitations pose challenges for practical deployment. In response, we propose Adaptive Schema-aware Event Extraction (ASEE), a novel paradigm combining schema paraphrasing with schema retrieval-augmented generation. ASEE adeptly retrieves paraphrased schemas and accurately generates targeted structures.To facilitate rigorous evaluation, we construct the Multi-Dimensional Schema-aware Event Extraction (MD-SEE) benchmark, which systematically consolidates 12 datasets across diverse domains, complexity levels, and language settings.Extensive evaluations on MD-SEE show that our proposed ASEE demonstrates strong adaptability across various scenarios, significantly improving the accuracy of event extraction. Our codes and datasets are available at https://github.com/USTC-StarTeam/ASEE.git

We study Attributed Question Answering (abbr., AQA), a newly-released long-form answer generation task. The tailored and efficient training programmes haven’t yet been leveraged to strengthen AQA models. This hinders the simultaneous enhancement of their essential capabilities, including evidence identification, cross-source relation recognition and anti-distraction reasoning. To address the issue, we propose a tailored progressive curriculum learning approach, and use it to optimize both encoder-decoder and decoder-only AQA models. Experiments on the benchmark QuoteSum show that our approach yields substantial improvements and enables the AQA performance to reach 73.9% Sem-F1 score.

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REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support
Jianwen Luo | Yu Hong | Shuai Yang | Jianmin Yao

Event argument extraction aims to identify event arguments and classify their roles within events, whereas relation extraction classifies semantic relationships between entities. Existing methods typically design task-specific models for EAE, which restricts the integration of relation-level semantics. Consequently, they overlook the complementary cues from RE that are beneficial for argument role disambiguation. To overcome this limitation, we propose REAR, a Relation-aware EAE Reinforced optimization framework. REAR first conducts joint supervised optimization on reasoning-enhanced data, which serves as a warm-up to strengthen the Large Language Model (LLM)’s ability to perform EAE while incorporating auxiliary cues from RE. Subsequently, it applies reinforcement learning to explore diverse reasoning trajectories and derive near-optimal strategies for integrating relation-level signals into EAE. Experiments on the ACE-E, ACE-E⁺ and ERE benchmarks demonstrate that REAR consistently surpasses previous decoder-only LLM methods, achieving F1-score gains of at least 0.9%, 2.2% and 1.6%, respectively.

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COMI-LINGUA: Expert Annotated Large-Scale Dataset for Multitask NLP in Hindi-English Code-Mixing
Rajvee Sheth | Himanshu Beniwal | Mayank Singh

We introduce COMI-LINGUA, the largest manually annotated Hindi-English code-mixed dataset, comprising 125K+ high-quality instances across five core NLP tasks: Token-level Language Identification, Matrix Language Identification, Named Entity Recognition, Part-Of-Speech Tagging and Machine Translation. Each instance is annotated by three bilingual annotators, yielding over 376K expert annotations with strong inter-annotator agreement (Fleiss’ Kappa ≥ 0.81). The rigorously preprocessed and filtered dataset covers both Devanagari and Roman scripts and spans diverse domains, ensuring real-world linguistic coverage. Evaluation reveals that closed-weight LLMs significantly outperform traditional tools and open-weight models in zero-shot settings. Notably, one-shot prompting consistently boosts performance across tasks, especially in structure-sensitive predictions like POS and NER. Fine-tuning open-weight LLMs on COMI-LINGUA demonstrates substantial improvements, achieving up to 95.25 F1 in NER, 98.77 F1 in MLI, and competitive MT performance, setting new benchmarks for Hinglish code-mixed text. COMI-LINGUA is publicly available at this URL: https://huggingface.co/datasets/LingoIITGN/COMI-LINGUA.

Large language models (LLMs) commonly risk copyright infringement by reproducing protected content verbatim or with insufficient transformative modifications, posing significant ethical, legal, and practical concerns. Current inference-time safeguards predominantly rely on restrictive refusal-based filters, often compromising the practical utility of these models. To address this, we collaborated closely with intellectual property experts to develop LAW-LM (Legally Aware Language Model), a legally-grounded framework explicitly designed to align LLM outputs with fair-use doctrine. Central to our method is FairUseDB, a carefully constructed dataset containing 18,000 expert-validated examples covering nine realistic infringement scenarios. Leveraging this dataset, we apply Direct Preference Optimization (DPO) to fine-tune open-source LLMs, encouraging them to produce legally compliant and practically useful alternatives rather than resorting to blunt refusal. Recognizing the shortcomings of traditional evaluation metrics, we propose new measures: Weighted Penalty Utility and Compliance Aware Harmonic Mean (CAH) to balance infringement risk against response utility. Extensive quantitative experiments coupled with expert evaluations confirm that LAW-LM substantially reduces problematic outputs compared to state-of-the-art approaches, while preserving real-world usability.

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InteractSpeech: A Speech Dialogue Interaction Corpus for Spoken Dialogue Model
Yifu Chen | Shengpeng Ji | Ziqing Wang | Hanting Wang | Zhou Zhao

Spoken Dialogue Models (SDMs) have achieved significant progress in recent years, yet they continue to face challenges in handling nuanced interactional phenomena. A significant bottleneck hindering further advancement is the scarcity of publicly available, high-quality datasets meticulously designed to train and evaluate these fine-grained interactive capabilities. We introduce InteractSpeech, a 150-hour English speech interaction dialogue dataset designed to empower spoken dialogue models with nuanced real-time interaction capabilities, such as handling interruptions and backchannels. InteractSpeech was created by synthesizing interactive dialogues from text using advanced speech synthesis, and by filtering real-world spoken dialogues for interactive segments. The dataset features precise speaker timestamps and annotations for diverse dialogue interactions, underpinned by a formal framework for interaction dynamics. We demonstrate InteractSpeech’s utility by fine-tuning a LLaMA 3-8B model on its textual scenarios and, crucially, by training a speech understanding model that accurately classifies key interactional events directly from audio. This highlights the dataset’s value in developing models capable of more natural and responsive conversational turn-taking. Audio samples are available at https://interactspeech.github.io/.

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Enhancing SQL Table Acquisition with Reverse Engineering for Text-to-SQL
Shixin Liu | Haoyu Xu | Yu Hong

Text-to-SQL oriented table acquisition suffers from heterogeneous semantic gap. To address the issue, we propose a Reverse Engineering (RE) based optimization approach. Instead of forward table search using questions as queries, RE reversely generates potentially-matched question conditioned on table schemas, and promotes semantic consistency verification between homogeneous questions. We experiment on two benchmarks, including SpiderUnion and BirdUnion. The test results show that our approach yields substantial improvements compared to the Retrieval-Reranker (2R) baseline, and achieves competitive performance in both table acquisition and Text-to-SQL tasks.

Efficiently managing the KV cache in Large Language Models (LLMs) is a critical challenge for long-context processing tasks such as retrieval-augmented generation (RAG), long text summarization, and multi-document analysis. Extending the context length substantially increases the KV cache size, leading to excessive memory consumption. Existing KV cache compression methods enforce a fixed pattern, neglecting task-specific characteristics, which hampers the effective retention of essential information while discarding less important tokens. In this paper, we introduce a novel Task-Aware KV cache mechanism that dynamically adjusts the KV cache size across different layers based on the characteristics of the tasks. Our approach builds on the significant observation of distinct activation patterns across layers in various tasks, which highlights the need for adaptive strategies tailored to each task’s unique demands. Based on this insight, we propose DynamicKV, a method that dynamically optimizes token retention by adjusting the number of tokens retained at each layer, adapting to the specific task. DynamicKV establishes global and per-layer maximum KV cache budgets, temporarily retaining the maximum budget for the current layer, and periodically updating the KV cache sizes of all preceding layers during inference. Our method demonstrates exceptional performance on the LongBench dataset, retaining only 1.7% of the KV cache while preserving 90%, 87%, 78%, and 83% of the original accuracy for LlaMA-3-8B-Instruct, Mistral-7B-Instruct-v0.2, Qwen2-7B-Instruct, and InternLM-2.5-7B-Chat-1M, respectively. When the retained KV cache size is increased to 6.9%, the performance becomes nearly indistinguishable from that without any KV cache compression. Notably, even under extreme compression (0.9%), DynamicKV surpasses state-of-the-art (SOTA) methods by 11% in the Needle-in-a-Haystack test using Mistral-7B-Instruct-v0.2. The code is available at repository https://github.com/DreamMr/DynamicK.

Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, particularly when directly employing LLMs as an intervention doctor. Researchers have mainly focused on using prompt engineering for role play as an intervention doctor and integrating auxiliary elements such as visual stimuli to enhance the sensory experience of the intervention, while neglecting the challenge that LLMs’ inherent dialogue style and intervention strategies do not meet the requirements of clinical dialogue interventions. To fill the gap, we propose a comprehensive framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) which is commonly used by clinicians. Specifically, we collected clinical recordings of dialogue interventions for autistic children and constructed the topic dialogue dataset ASD-iLLM-8k. By incorporating the system prompt based on the ABA and ASD-iLLM-8k dataset, we fine-tuned LLMs to develop ASD-iLLM. We also proposed a role-play strategy in which LLMs act as autistic children to comprehensively evaluate the doctor model’s capabilities at the dialogue level. Extensive experiments indicate that ASD-iLLM outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors. Our dataset, model, and code are available on https://github.com/Shuzhong-Lai/ASD-iLLM.

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GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction
Jie Zhao | Wanting Ning | Yuxiao Fei | Yubo Feng | Lishuang Li

In Natural Language Processing(NLP), Event Temporal Relation Extraction (ETRE) is to recognize the temporal relations of two events. Prior studies have noted the importance of language models for ETRE. However, the restricted pre-trained knowledge of Small Language Models(SLMs) limits their capability to handle minority class relations in imbalanced classification datasets. For Large Language Models(LLMs), researchers adopt manually designed prompts or instructions, which may introduce extra noise, leading to interference with the model’s judgment of the long-distance dependencies between events. To address these issues, we propose GDLLM, a Global Distance-aware modeling approach based on LLMs. We first present a distance-aware graph structure utilizing Graph Attention Network(GAT) to assist the LLMs in capturing long-distance dependency features. Additionally, we design a temporal feature learning paradigm based on soft inference to augment the identification of relations with a short-distance proximity band, which supplements the probabilistic information generated by LLMs into the multi-head attention mechanism. Since the global feature can be captured effectively, our framework substantially enhances the performance of minority relation classes and improves the overall learning ability. Experiments on two publicly available datasets, TB-Dense and MATRES, demonstrate that our approach achieves state-of-the-art (SOTA) performance.

As large language models (LLMs) process increasing context windows, the memory usage of KV cache has become a critical bottleneck during inference. The mainstream KV compression methods, including KV pruning and KV quantization, primarily focus on either token or precision dimensions separately. However, these works have left the trade-off between these two orthogonal dimensions largely unexplored. In this paper, we leverage the Information Bottleneck principle to formulate KV cache compression within a unified theoretical framework. We demonstrate that a carefully managed token-precision trade-off can achieve an optimal point within the Information Bottleneck compared to standalone KV pruning or KV quantization. Experiments reveal that storing more tokens in the KV cache at lower precision—a strategy we term quantized pruning—can significantly enhance the long-context performance of LLMs. An in-depth analysis of this token-precision trade-off across key aspects shows that quantized pruning achieves substantial improvements in retrieval-related tasks and consistently performs well across varying input lengths. Furthermore, quantized pruning exhibits notable stability and effectiveness across different KV pruning methods, quantization strategies, and model scales. These findings offer valuable insights into optimizing KV cache compression through balanced token-precision trade-off strategies. Our code isavailable at https://github.com/zhzihao/QPruningKV.

Retrieval-Augmented Generation (RAG) has become essential for large-scale code generation, grounding predictions in external code corpora to improve factuality. However, a critical yet underexplored aspect of RAG pipelines is chunking—the process of dividing documents into retrievable units. Existing line-based chunking heuristics often break semantic structures, splitting functions or merging unrelated code, which can degrade generation quality. We propose chunking via Abstract Syntax Trees (cAST), a structure-aware method that recursively breaks large AST nodes into smaller chunks and merges sibling nodes while respecting size limits. This approach generates self-contained, semantically coherent units across programming languages and tasks, improving performance on diverse code generation tasks, e.g., boosting Recall@5 by 4.3 points on RepoEval retrieval and Pass@1 by 2.67 points on SWE-bench generation. Our work highlights the importance of structure-aware chunking for scaling retrieval-enhanced code intelligence.

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A Group Fairness Lens for Large Language Models
Guanqun Bi | Yuqiang Xie | Lei Shen | Yanan Cao

The rapid advancement of large language models has revolutionized various applications but also raised crucial concerns about their potential to perpetuate biases and unfairness when deployed in social media contexts. Evaluating LLMs’ potential biases and fairness has become crucial, as existing methods rely on limited prompts focusing on just a few groups, lacking a comprehensive categorical perspective. In this paper, we propose evaluating LLM biases from a group fairness lens using a novel hierarchical schema characterizing diverse social groups. Specifically, we construct a dataset, GFair, encapsulating target-attribute combinations across multiple dimensions. In addition, we introduce statement organization, a new open-ended text generation task, to uncover complex biases in LLMs. Extensive evaluations of popular LLMs reveal inherent safety concerns. To mitigate the biases of LLM from a group fairness perspective, we pioneer a novel chain-of-thought method GF-Think to mitigate biases of LLMs from a group fairness perspective. Experimental results demonstrate its efficacy in mitigating bias in LLMs to achieve fairness.

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VLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training
Zhanpeng Chen | Chengjin Xu | Yiyan Qi | Xuhui Jiang | Jian Guo

Vision-language Models (VLMs) have demonstrated remarkable capabilities in processing and generating content across multiple data modalities. However, a significant drawback of VLMs is their reliance on static training data, leading to outdated information and limited contextual awareness. This static nature hampers their ability to provide accurate and up-to-date responses, particularly in dynamic or rapidly evolving contexts. To address these limitations, we propose RagVL, a novel framework with knowledge-enhanced reranking and noise-injected training. We instruction-tune the VLM with a simple yet effective instruction template to induce its ranking ability and serve it as a reranker to precisely filter the top-k retrieved images. For generation, we inject visual noise during training at the data and token levels to enhance the generator’s robustness. Extensive experiments on four datasets verify the effectiveness of our method. Code and models are available at https://anonymous.4open.science/r/RagVL-F694.

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Rethinking DPO: The Role of Rejected Responses in Preference Misalignment
Jae Hyeon Cho | JunHyeok Oh | Myunsoo Kim | Byung-Jun Lee

Direct Preference Optimization (DPO) is a simple and efficient framework that has attracted substantial attention. However, it often struggles to meet its primary objectives—increasing the generation probability of chosen responses while reducing that of rejected responses—due to the dominant influence of rejected responses on the loss function. This imbalance leads to suboptimal performance in promoting preferred responses. In this work, we systematically analyze the limitations of DPO and existing algorithms designed to achieve the objectives stated above. To address these limitations, we propose Bounded-DPO (BDPO), a novel method that bounds the influence of rejected responses while maintaining the original optimization structure of DPO. Through theoretical analysis and empirical evaluations, we demonstrate that BDPO achieves a balanced optimization of the chosen and rejected responses, outperforming existing algorithms.

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Enhancing Recommendation Explanations through User-Centric Refinement
Jingsen Zhang | Zihang Tian | Xueyang Feng | Xu Chen | Chong Chen

Generating natural language explanations for recommendations has become increasingly important in recommender systems. Traditional approaches typically treat user reviews as ground truth for explanations and focus on improving review prediction accuracy by designing various model architectures. However, due to limitations in data scale and model capability, these explanations often fail to meet key user-centric aspects such as factuality, personalization, and sentiment coherence, significantly reducing their overall helpfulness to users.In this paper, we propose a novel paradigm that refines initial explanations generated by existing explainable recommender models during the inference stage to enhance their quality in multiple aspects. Specifically, we introduce a multi-agent collaborative refinement framework based on large language models. To ensure alignment between the refinement process and user demands, we employ a plan-then-refine pattern to perform targeted modifications. To enable continuous improvements, we design a hierarchical reflection mechanism that provides feedback to the refinement process from both strategic and content perspectives. Extensive experiments on three datasets demonstrate the effectiveness of our framework.

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Distributional Surgery for Language Model Activations
Bao Nguyen | Binh Nguyen | Duy Nguyen | Viet Anh Nguyen

Language models, while capable of generating remarkably coherent and seemingly accurate text, can occasionally produce undesirable content including harmful or toxic outputs. In this paper, we present a new two-stage approach to detect and mitigate undesirable content generations by rectifying activations. First, we train an ensemble of layerwise classifiers to detect undesirable content using activations by minimizing a smooth surrogate of the risk-aware score. Then, for detected undesirable contents, we propose layerwise distributional steering policies that transform the attention heads. These policies are computed through principled semidefinite programming aims to minimally perturb the attention distribution while probabilistically guaranteeing the effectiveness of the editions. Empirical evaluations across multiple language models and datasets show that our method outperforms baselines in reducing the generation of undesirable output.

The rapid advancements in Large Language Models (LLMs) and Large Visual-Language Models (LVLMs) have opened up new opportunities for integrating visual and linguistic modalities. Yet, challenges remain in aligning these modalities effectively, causing issues such as hallucinations, where generated outputs are not grounded in the visual input, and safety concerns in the application of LVLMs across various domains. Existing alignment methods, such as instruction tuning and preference tuning, often rely on external datasets, human annotations, or complex post-processing, which limit scalability and introduce additional costs. To address these challenges, we propose a novel approach that generates the debiased self-judgment score, a self-evaluation metric created internally by the model without relying on external resources. This enables the model to autonomously improve alignment. Our method enhances both decoding strategies and preference tuning processes, resulting in improved alignment, reduced hallucinations, and enhanced safety. Empirical results show that our approach significantly outperforms traditional methods, offering a more effective solution for aligning LVLMs.

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Low-Confidence Gold: Refining Low-Confidence Samples for Efficient Instruction Tuning
Hongyi Cai | Jie Li | Mohammad Mahdinur Rahman | Wenzhen Dong

The effectiveness of instruction fine-tuning for Large Language Models is fundamentally constrained by the quality and efficiency of training datasets. This work introduces Low-Confidence Gold (LCG), a novel filtering framework that employs centroid-based clustering and confidence-guided selection for identifying valuable instruction pairs. Through a semi-supervised approach using a lightweight classifier trained on representative samples, LCG curates high-quality subsets while preserving data diversity. Experimental evaluation demonstrates that models fine-tuned on LCG-filtered subsets of 6K samples achieve superior performance compared to existing methods, with substantial improvements on MT-bench and consistent gains across comprehensive evaluation metrics. The framework’s efficacy while maintaining model performance establishes a promising result for efficient instruction tuning.

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Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home?
Yujin Choi | Youngjoo Park | Junyoung Byun | Jaewook Lee | Jinseong Park

Retrieval-augmented generation (RAG) mitigates the hallucination problem in large language models (LLMs) and has proven effective for personalized usages. However, delivering private retrieved documents directly to LLMs introduces vulnerability to membership inference attacks (MIAs), which try to determine whether the target data point exists in the private external database or not. Based on the insight that MIA queries typically exhibit high similarity to only one target document, we introduce a novel similarity-based MIA detection framework designed for the RAG system. With the proposed method, we show that a simple detect-and-hide strategy can successfully obfuscate attackers, maintain data utility, and remain system-agnostic against MIA. We experimentally prove its detection and defense against various state-of-the-art MIA methods and its adaptability to existing RAG systems.

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Causal-LLM: A Unified One-Shot Framework for Prompt- and Data-Driven Causal Graph Discovery
Amartya Roy | N Devharish | Shreya Ganguly | Kripabandhu Ghosh

Current causal discovery methods using Large Language Models (LLMs) often rely on pairwise or iterative strategies, which fail to capture global dependencies, amplify local biases, and reduce overall accuracy. This work introduces a unified framework for one-step full causal graph discovery through: (1) Prompt-based discovery with in-context learning when node metadata is available, and (2) Causal_llm, a data-driven method for settings without metadata. Empirical results demonstrate that the prompt-based approach outperforms state-of-the-art models (GranDAG, GES, ICA-LiNGAM) by approximately 40% in edge accuracy on datasets like Asia and Sachs, while maintaining strong performance on more complex graphs (ALARM, HEPAR2). Causal_llm consistently excels across all benchmarks, achieving 50% faster inference than reinforcement learning-based methods and improving precision by 25% in fairness-sensitive domains such as legal decision-making. We also introduce two domain-specific DAGs—one for bias propagation and another for legal reasoning under the Bhartiya Nyaya Sanhita—demonstrating LLMs’ capability for systemic, real-world causal discovery.

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LRPLAN: A Multi-Agent Collaboration of Large Language and Reasoning Models for Planning with Implicit & Explicit Constraints
T Karthikeyan | Om Dehlan | Mausam | Manish Gupta

Our goal is to build language model based multi-agent systems for complex planning problems involving multiple explicit and implicit constraints, some of which may be commonsense. Our initial investigations reveal that large language models (LLMs) are often unable to maintain consistency across the planning process, whereas large reasoning models (LRMs) struggle with handling implicit commonsense constraints. In response, we introduce LRPlan, a novel domain-independent, language-based multi-agent architecture where LLM and LRM-based agents collaborate at training time to abstract important patterns, heuristics and insights about the domain. At test time, they collaborate in implementing these learned patterns and insights for a new planning instance. We perform experiments on two datasets, TravelPlanner and TimeArena-Static, and use two LLM-LRM combinations from GPT and DeepSeek families. We find that LRPlan outperforms various multi-agent and single-agent baselines obtaining notably higher accuracy as well as cost efficiency. We make the code publiclyavailable.

Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requires considerable human effort, significantly limiting its scalability. To mitigate this, recent studies have explored automated prompt optimization as a promising solution. Despite these efforts, existing methods still face critical challenges in robustness, efficiency, and generalization. To systematically address these challenges, we first conduct an empirical analysis to identify the limitations of current reflection-based prompt optimization paradigm. Building on these insights, we propose 7 innovative approaches inspired by traditional deep learning paradigms for prompt optimization (DLPO), seamlessly integrating these concepts into text-based gradient optimization. Through these advancements, we progressively tackle the aforementioned challenges and validate our methods through extensive experimentation. We hope our study not only provides valuable guidance for future research but also offers a comprehensive understanding of the challenges and potential solutions in prompt optimization.

Few-shot relation classification aims to recognize the relation between two mentioned entities, with the help of only a few support samples. However, a few samples tend to be limited for tackling unlimited queries. If a query cannot find references from the support samples, it is defined as none-of-the-above (NOTA). Previous works mainly focus on how to distinguish N+1 categories, including N known relations and one NOTA class, to accurately recognize relations. However, the robustness towards various NOTA rates, i.e. the proportion of NOTA among queries, is under investigation. In this paper, we target the robustness and propose a simple but effective framework. Specifically, we introduce relation descriptions as external knowledge to enhance the model’s comprehension of the relation semantics. Moreover, we further promote robustness by proposing a novel agreement loss. It is designed for seeking decision consistency between the instance-level decision, i.e. support samples, and relation-level decision, i.e. relation descriptions. Extensive experimental results demonstrate that the proposed framework outperforms strong baselines while being robust against various NOTA rates. The code is released on GitHub at https://github.com/Pisces-29/RoFRC.

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For a Fistful of Puns: Evaluating a Puns in Multiword Expressions Identification Algorithm Without Dedicated Dataset
Julien Bezançon | Gaël Lejeune

Machine Translation systems has always faced challenges such as multiword expressions (MWEs) and wordplays, which impact their performance, being idiosyncratic and pervasive across different languages. In this context, we seek to explore the nature of puns created from multiword expressions (PMWEs), characterized by the creation of a wordplay from a source MWE to recontextualize it or to give it a humorous touch. Little work has been done on PMWEs in NLP. To address this challenge, we introduce ASMR, an alignment-based PMWE identification and tagging algorithm. We offer an in-depth analysis of three different approaches to ASMR, each created to identify different types of PMWEs. In the absence of PMWE-related datasets and resources, we proceed to a snowclone detection task in English.We also perform a MWE identification task in 26 languages to evaluate ASMR performances across different languages. We show that ASMR exhibits state-of-the-art results for the snowclone detection task and produces interesting results with the MWE identification task. These results may indicate that ASMR is suitable for a PMWE identification task.

Large Vision-Language Models (LVLMs) have recently shown promising results on various multimodal tasks, even achieving human-comparable performance in certain cases. Nevertheless, LVLMs remain prone to hallucinations–they often rely heavily on a single modality or memorize training data without properly grounding their outputs. To address this, we propose a training-free, tri-layer contrastive decoding with watermarking, which proceeds in three steps: (1) select a mature layer and an amateur layer among the decoding layers, (2) identify a pivot layer using a watermark-related question to assess whether the layer is visually well-grounded, and (3) apply tri-layer contrastive decoding to generate the final output. Experiments on public benchmarks such as POPE, MME and AMBER demonstrate that our method achieves state-of-the-art performance in reducing hallucinations in LVLMs and generates more visually grounded responses.

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Are the Reasoning Models Good at Automated Essay Scoring?
Lui Yoshida

This study investigates the validity and reliability of reasoning models, specifically OpenAI’s o3-mini and o4-mini, in automated essay scoring (AES) tasks. We evaluated these models’ performance on the TOEFL11 dataset by measuring agreement with expert ratings (validity) and consistency in repeated evaluations (reliability). Our findings reveal two key results: (1) the validity of reasoning models o3-mini and o4-mini is significantly lower than that of a non-reasoning model GPT-4o mini, and (2) the reliability of reasoning models cannot be considered high, with Intraclass Correlation Coefficients (ICC) of approximately 0.7 compared to GPT-4o mini’s 0.95. These results demonstrate that reasoning models, despite their excellent performance on many benchmarks, do not necessarily perform well on specific tasks such as AES. Additionally, we found that few-shot prompting significantly improves performance for reasoning models, while Chain of Thought (CoT) has less impact.

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Rethinking LLM-Based Recommendations: A Personalized Query-Driven Parallel Integration
Donghee Han | Hwanjun Song | Mun Yong Yi

Recent studies have explored integrating large langucage models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from serialized architecture.To effectively address these issues, we propose a Query-to-Recommendation, a parallel recommendation framework that decouples LLMs from candidate pre-selection and instead enables direct retrieval over the entire item pool. Our framework connects LLMs and recommendation models in a parallel manner, allowing each component to independently utilize its strengths without interfering with the other. In this framework, LLMs are utilized to generate feature-enriched item descriptions and personalized user queries, allowing for capturing diverse preferences and enabling rich semantic matching in a zero-shot manner. To effectively combine the complementary strengths of LLM and collaborative signals, we introduce an adaptive reranking strategy. Extensive experiments demonstrate an improvement in performance up to 57%, while also improving the novelty and diversity of recommendations.

Subject-driven text-to-image (T2I) generation aims to produce images that align with a given textual description, while preserving the visual identity from a referenced subject image. Despite its broad downstream applicability—ranging from enhanced personalization in image generation to consistent character representation in video rendering—progress in this field is limited by the lack of reliable automatic evaluation. Existing methods either assess only one aspect of the task (i.e., textual alignment or subject preservation), misalign with human judgments, or rely on costly API-based evaluation. To address this gap, we introduce RefVNLI, a cost-effective metric that evaluates both textual alignment and subject preservation in a single run. Trained on a large-scale dataset derived from video-reasoning benchmarks and image perturbations, RefVNLI outperforms or statistically matches existing baselines across multiple benchmarks and subject categories (e.g., Animal, Object), achieving up to 6.4-point gains in textual alignment and 5.9-point gains in subject preservation.

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What data should I include in my POS tagging training set?
Zoey Liu | Masoud Jasbi | Christan Grant | Kenji Sagae | Emily Prud’hommeaux

Building an NLP training set for understudied languages, including Indigenous and endangered languages, often faces challenges due to varying degrees of resource limitations in the speaker communities. What are some reasonable approaches for training set construction in these cases? We address this question with POS tagging as the test case. Although many might consider POS tagging “a solved problem”, it remains a crucial task for descriptive linguistics and language documentation and requires laborious manual annotation. Drawing data from 12 language families, we compare in-context learning, active learning (AL), and random sampling. Our results suggest: (1) for communities whose language data can be ethically shared with an API, using only 1,000 randomly sampled tokens as prompt examples, the proprietary GPT-4.1-mini can deliver desirable performance (F1>0.83) on par with that from a training set of thousands of tokens in AL iterations; (2) in cases where communities prefer not to share data, 4,500-5,500 tokens selected from AL can yield reasonable results at a pace statistically significantly faster than random sampling, evidenced by growth curve modeling.

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AttnComp: Attention-Guided Adaptive Context Compression for Retrieval-Augmented Generation
Lvzhou Luo | Yixuan Cao | Ping Luo

Retrieval-augmented generation improves the factual accuracy of Large Language Models (LLMs) by incorporating external context, but often suffers from irrelevant retrieved content that hinders effectiveness. Context compression addresses this issue by filtering out irrelevant information from context before LLM generation. However, existing methods struggle to adaptively adjust compression rates for different context, maintain low latency and integrate information across multiple documents. To overcome these limitations, We introduce AttnComp, an adaptive, efficient and context-aware compression framework. By leveraging the attention mechanism of LLMs to identify relevant information, AttnComp employs a Top-P compression algorithm to retain the minimal set of documents whose cumulative attention weights exceeds a predefined threshold. In addition to compression, AttnComp estimates response confidence by assessing the overall relevance of the retrieved content, enabling users to gauge response reliability. Experiments demonstrate that AttnComp outperforms existing compression methods and uncompressed baselines, achieving higher accuracy with substantial compression rates and lower latency.

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SafeInt: Shielding Large Language Models from Jailbreak Attacks via Safety-Aware Representation Intervention
Jiaqi Wu | Chen Chen | Chunyan Hou | Xiaojie Yuan

With the widespread real-world deployment of large language models (LLMs), ensuring their behavior complies with safety standards has become crucial. Jailbreak attacks exploit vulnerabilities in LLMs to induce undesirable behavior, posing a significant threat to LLM safety. Previous defenses often fail to achieve both effectiveness and efficiency simultaneously. Defenses from a representation perspective offer new insights, but existing interventions cannot dynamically adjust representations based on the harmfulness of the queries. To address this limitation, we propose SafeIntervention (SafeInt), a novel defense method that shields LLMs from jailbreak attacks through safety-aware representation intervention. Built on our analysis of the representations of jailbreak samples, the core idea of SafeInt is to relocate jailbreak-related representations into the rejection region. This is achieved by intervening in the representation distributions of jailbreak samples to align them with those of unsafe samples. We conduct comprehensive experiments covering six jailbreak attacks, two jailbreak datasets, and two utility benchmarks. Experimental results demonstrate that SafeInt outperforms all baselines in defending LLMs against jailbreak attacks while largely maintaining utility. Additionally, we evaluate SafeInt against adaptive attacks and verify its effectiveness in mitigating real-time attacks.

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Staged Knowledge Distillation Through Least-to-Most Prompting: Optimizing Teacher Guidance via Difficulty-Aware Training
Mengxiang Zhang | Lingyuan Liu

Knowledge distillation (KD) enables the compression of large language models (LLMs) by transferring knowledge from a high-capacity teacher model to a resource-efficient student model, maintaining competitive performance for tasks such as instruction following. However, conventional white-box KD methods often suffer from training-inference mismatches and suboptimal performance due to the asymmetric nature of Kullback-Leibler divergence (KLD) and reliance on computationally expensive student-generated outputs. To address these challenges, we propose Least-to-Most Prompting Knowledge Distillation (L2M-KD), a novel white-box KD method grounded in curriculum learning (CL) and adaptive loss design. L2M-KD employs a two-pronged approach: (1) a CL strategy that ranks training samples by difficulty using Rouge-L scores, partitioning them into easy-to-hard subsets across multiple stages, and (2) an adaptive KD loss that transitions from KLD to skew KLD, dynamically adjusting teacher guidance to mitigate mode-averaging and over-smoothing. Extensive experiments on instruction-following tasks demonstrate that L2M-KD outperforms existing white-box KD methods, achieving superior student model performance with reduced computational overhead by leveraging ground-truth outputs exclusively. Our findings underscore the efficacy of difficulty-aware training and adaptive teacher guidance, offering a computationally efficient and robust approach to LLM compression.

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LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering
Patrick Sutanto | Joan Santoso | Esther Irawati Setiawan | Aji Prasetya Wibawa

Encoder models offer efficiency for specific tasks, but their performance depend on data availability. While Large Language Models (LLMs) excel at few-shot learning, their direct application in real-world scenarios is often hindered by their high computational cost. To address this challenge, we propose a simple yet effective approach that uses LLMs for data generation and scoring to improve encoder only model performance. We evaluate this framework on few-shot Multiple Choice Question Answering (MCQA), an important task where acquiring labeled data is costly. Our approach utilizes LLMs to create MCQA questions and choices (exploring both direct JSON and decomposed generation methods) and assigns probability scores to these choices. This generated data and the LLM scores are then used to fine-tune smaller and more efficient DeBERTa-v3-base using distillation loss. Extensive experiments on the MMLU benchmark demonstrate that our method improves accuracy from 28.9% to 39.3%, representing a gain of over 10% compared to a baseline finetuned directly on 5-shot examples. This shows the effectiveness of LLM-driven data generation and knowledge distillation for few-shot MCQA.

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Teaching LLMs to Plan, Not Just Solve: Plan Learning Boosts LLMs Generalization in Reasoning Tasks
Tianlong Wang | Junzhe Chen | Weibin Liao | Xueting Han | Jing Bai

Reinforcement learning (RL) on self-generated data has emerged as a promising paradigm for improving reasoning in large language models (LLMs). However, RL relies on accurate reward signals, which are scarce in many domains, making it critical to train models that can generalize to unseen problems. Existing methods often focus on task-specific or domain-specific reasoning, lacking consideration for generalization and may degrade performance on other tasks. To address this, we distinguish between abstract plans, representing high-level problem-solving strategies, and concrete solutions, proposing that learning plans develops transferable general reasoning capabilities and promotes better generalization. Building on this insight, we propose PlanLearn, a framework that combines plan-based search with Step-level Advantage Preference Optimization (Step-APO) to optimize plan learning. Experimental results show that PlanLearn, trained exclusively on GSM8K and MATH, not only significantly improves in-domain performance but also enhances out-of-domain benchmarks, such as HumanEval (+12.2%), GPQA (+8.6%), ARC-C (+4.0%), MMLU-STEM (+2.2%), and BBH (+1.8%). The code is available at https://github.com/tianlwang/PlanLearn.

Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. However, their deployment in resource-constrained environments and concerns over user data privacy pose significant challenges. In contrast, Small Language Models (SLMs) offer computational efficiency but often lag in performance. To address these issues, we propose FedCoT, a federated framework designed for the Chain-of-Thought (CoT) distillation of knowledge from LLMs to SLMs, while ensuring the preservation of clients’ data privacy. FedCoT ensures secure and efficient knowledge transfer from an LLM on a high-powered server to an SLM on a resource-constrained client, while adhering to privacy requirements. Leveraging perturbed prompts and rationales generated through the CoT approach, the framework enhances the performance of the client’s SLM without compromising user data privacy within a multi-task learning framework. We propose two privacy protection strategies: the Exponential Mechanism Strategy and the Adaptive Exponential Mechanism Strategy, which balance user prompt privacy and the usability of rationales. Empirical evaluation on various text generation tasks demonstrates the effectiveness of FedCoT in training task-specific SLMs with enhanced performance while prioritizing data privacy protection. Our code has been contributed to the FATE open-source project and is now publicly accessible at https://github.com/FederatedAI/FATE-LLM/tree/main/python/fate_llm/algo/fedcot

Chain-of-Thought (CoT) prompting enhances the math reasoning capability of large language models (LLMs) to a large margin. However, the mechanism underlying such improvements remains unexplored. In this paper, we present SalaMAnder (Shapley-based Mathematical Expression Attribution and Metric), a theoretically grounded methodology as well as a mathematically rigorous evaluation metric for quantifying component-level contributions in few-shot CoT reasoning. Concretely, we leverage the Shapley value for mathematical expression attribution and develop an efficient stratified sampling algorithm that significantly reduces the computational complexity. Besides, we develop the CoSP (Cardinality of Shapley Positives) metric through covariance analysis. Comprehensive validation across popular LLM models and diverse mathematical benchmarks demonstrates that the CoSP metric within our SalaMAnder framework exhibits a robust monotonic correlation with model performance, not only providing theoretical explanations for the empirical success of existing few-shot CoT but also establishing mathematically rigorous principles for prompt construction optimization. Furthermore, we verify the reliability of the explanation, based on which we unify the insights of previous work.

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Representing LLMs in Prompt Semantic Task Space
Idan Kashani | Avi Mendelson | Yaniv Nemcovsky

Large language models (LLMs) achieve impressive results over various tasks, and ever-expanding public repositories contain an abundance of pre-trained models. Therefore, identifying the best-performing LLM for a given task is a significant challenge. Previous works have suggested learning LLM representations to address this. However, these approaches present limited scalability and require costly retraining to encompass additional models and datasets. Moreover, the produced representation utilizes distinct spaces that cannot be easily interpreted. This work presents an efficient, training-free approach to representing LLMs as linear operators within the prompts’ semantic task space, thus providing a highly interpretable representation of the models’ application. Our method utilizes closed-form computation of geometrical properties and ensures exceptional scalability and real-time adaptability to dynamically expanding repositories. We demonstrate our approach on success prediction and model selection tasks, achieving competitive or state-of-the-art results with notable performance in out-of-sample scenarios.

Large language models (LLMs) exhibit human-like intelligence, enabling them to simulate human behavior and support various applications that require both humanized communication and extensive knowledge reserves. Efforts are made to personify LLMs with special training data or hand-crafted prompts, while correspondingly faced with challenges such as insufficient data usage or rigid behavior patterns. Consequently, personified LLMs fail to capture personified knowledge or express persistent opinion. To fully unlock the potential of LLM personification, we propose PersLLM, a framework for better data construction and model tuning. For insufficient data usage, we incorporate strategies such as Chain-of-Thought prompting and anti-induction, improving the quality of data construction and capturing the personality experiences, knowledge, and thoughts more comprehensively. For rigid behavior patterns, we design the tuning process and introduce automated DPO to enhance the specificity and dynamism of the models’ personalities, which leads to a more natural opinion communication. Both automated metrics and expert human evaluations demonstrate the effectiveness of our approach. Case studies in human-machine interactions and multi-agent systems further suggest potential application scenarios and future directions for LLM personification.

Prior research indicates that although large language models (LLMs) can precisely articulate the theoretical probability distributions associated with optimal strategic choices, their actual decision-making systematically diverges from these prescriptions—a phenomenon we define as the cognition–behaviour gap in LLMs. For example, in a Rock–Paper–Scissors (RPS) game, LLMs correctly identify the strategy of Nash equilibrium as selecting each action (Rock, Paper, Scissors) with equal probability ¹⁄₃, but their observed choices systematically deviate from this uniform distribution. Through a comprehensive evaluation of 20 state-of-the-art LLMs, we identify two critical insights: (1) we demonstrate that intrinsic biases inherited from pre-training corpora alone are insufficient to explain the observed deviations; (2) we introduce a semantic-free paradigm that strips away intrinsic biases to isolate pure positional bias-LLMs exhibit distinct position preferences—for example, o1 favours the first option, DeepSeek-V3 peaks the middle and DeepSeek-R1 shows a bimodal bias toward first and last positions. Our findings advocate innovation to bridge the gap between strategic reasoning and decision-making in LLMs.

Large Vision–Language Models (LVLMs) have garnered substantial interest owing to their impressive ability to interpret visual inputs and converse with users.Nevertheless, LVLMs still suffer from object hallucination – generating descriptions for objects that are absent from the image, which undermines reliability and hinders real-world deployment. We propose DAPE-BR, a positional-alignment scheme that (i) preserves the pretrained weight order while globally—- visual–text distances, (ii) embeds an isotropic fused patch-distance metric, and (iii) applies a patch-distance causal mask to enforce spatial causality. Extensive experiments on POPE, MMStar and SQA show that DAPE-BR consistently reduces hallucinations and boosts.

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From Confidence to Collapse in LLM Factual Robustness
Alina Fastowski | Bardh Prenkaj | Gjergji Kasneci

Ensuring the robustness of factual knowledge in LLMs is critical for reliable applications in tasks such as question answering and reasoning. However, existing evaluation methods predominantly focus on performance-based metrics, often investigating from the perspective of prompt perturbations, which captures only the externally triggered side of knowledge robustness. To bridge this gap, we introduce a principled approach to measure factual robustness from the perspective of the generation process by analyzing token distribution entropy in combination with temperature scaling sensitivity. These two factors build the Factual Robustness Score (FRS), a novel metric which quantifies the stability of a fact against perturbations in decoding conditions, given its initial uncertainty. To validate our approach, we conduct extensive experiments on 5 LLMs across 3 closed-book QA datasets (SQuAD, TriviaQA, and HotpotQA). We show that factual robustness varies significantly – smaller models report an FRS of 0.76, larger ones 0.93 – with accuracy degrading by ~60% under increased uncertainty. These insights demonstrate how entropy and temperature scaling impact factual accuracy, and lay a foundation for developing more robust knowledge retention and retrieval in future models. We release our code at https://github.com/afastowski/frs.

News writing empowered by large language models (LLMs) has emerged as a prevalent trend due to their efficiency and scalability. This paradigm necessitates dynamic information acquisition, knowledge structuring, and precise viewpoint articulation. However, current approaches often rely on superficially retrieved information and oversimplified knowledge enumeration, resulting in shallow, repetitive, and unordered outputs. Additionally, the lack of controllability over narrative viewpoints fails to align with user-defined preferences. To address these limitations, we propose an LLM-based multi-agent controllable news writing framework termed CtrlNews. The framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles. Besides, we elaborate a fine-grained viewpoint control mechanism to precisely regulate bias, emotion, and exaggeration attributes. When composing long-form news articles, the controlled viewpoints are extended via emotion-preserving composition and self-reflection refinement to ensure the consistency of viewpoint control and prevent the dilution of the control effect. Experiments on quality and control effect evaluation, news dissemination effect assessment, and human evaluation demonstrate significant improvements across multiple metrics compared to existing methods.

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Joint Enhancement of Relational Reasoning for Long-Context LLMs
Zhirui Chen | Wei Shen | Jiashui Huang | Ling Shao

Despite significant progress, large language models (LLMs) still struggle with long contexts due to memory limitations and their inability to tackle complex and long-context tasks. Additionally, LLMs often suffer from a lack of transparency and are prone to producing hallucinations. To address these challenges, we propose JERR, a novel framework designed to enhance long-context comprehension via graph-based reasoning in LLMs. JERR integrates three key components: synopsis extraction, graph construction, and relational reasoning. First, synopsis is extracted by chunking text strategically, allowing the model to summarize and understand information more efficiently. Second, we build a directed acyclic graph (DAG) to resolve redundancy, ensuring logical consistency and clarity. Finally, we incorporate Monte Carlo Tree Search (MCTS) to help the model navigate complex reasoning paths, ensuring more accurate and interpretable outputs. This framework provides a novel solution that enables LLMs to handle extended contexts and complex reasoning tasks with improved reliability and transparency. Experimental results show that JERR consistently outperforms all baselines on the ROUGE and F1 metrics, achieving the highest scores on the LLM-Rater evaluation.

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Training Medical QA Models Based on Mixed Rewards from Multiple-Choice and Open-Ended Questions
Yue Qiu | Yujan Ting | Pei Dong | Terrence Chen | Weijing Huang

Reinforcement learning (RL) for large language models (LLMs) typically requires clear reward signals, which are often unavailable for open-ended (OE) questions where answer evaluation is ambiguous without scalable expert labeling. We investigate whether LLMs benefit from training on mixed data with varying reward clarity. Our approach combines Multiple-choice questions (MCQs), which offer clear binary rewards, with OE questions, for which we use simpler, potentially noisy rewards such as Jaccard similarity or LLM-based evaluators. We hypothesize that MCQs can stabilize training when mixed with OE questions. Our experiments show this mixed-data approach consistently improves medical question-answering performance across model scales.

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Rethink Rumor Detection in the Era of LLMs: A Review
Chang Yang | Peng Zhang | Jing Zhang | Hui Gao | Changhao Song

The rise of large language models (LLMs) has fundamentally reshaped the technological paradigm of rumor detection, offering transformative opportunities to construct adaptive detection systems while simultaneously ushering in new threats, such as “logically perfect rumors”. This paper aims to unify existing methods in the field of rumor detection and reveal the logical mechanisms behind them. From the perspective of complex systems, we innovatively propose a Cognition-Interaction-Behavior (CIB) tri-level framework for rumor detection based on collective intelligence and explore the synergistic relationship between LLMs and collective intelligence in rumor governance. We identify promising future research directions, including advancing agent-based modeling to capture complex rumor dynamics, addressing emerging challenges unique to the LLM era, and interdisciplinary perspectives. We hope this work lays a theoretical foundation for next-generation rumor detection paradigms and offers valuable insights for advancing the field.

Prior benchmarks for evaluating the domain-specific knowledge of large language models (LLMs) lack the scalability to handle complex academic tasks. To address this, we introduce ScholarBench, a benchmark centered on deep expert knowledge and complex academic problem-solving, which evaluates the academic reasoning ability of LLMs and is constructed through a three-step process. ScholarBench targets more specialized and logically complex contexts derived from academic literature, encompassing five distinct problem types. Unlike prior benchmarks, ScholarBench evaluates the abstraction, comprehension, and reasoning capabilities of LLMs across eight distinct research domains. To ensure high-quality evaluation data, we define category-specific example attributes and design questions that are aligned with the characteristic research methodologies and discourse structures of each domain. Additionally, this benchmark operates as an English-Korean bilingual dataset, facilitating simultaneous evaluation for linguistic capabilities of LLMs in both languages. The benchmark comprises 5,031 examples in Korean and 5,309 in English, with even state-of-the-art models like o3-mini achieving an average evaluation score of only 0.543, demonstrating the challenging nature of this benchmark.

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MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation
Jungyeon Lee | Lee Kangmin | Taeuk Kim

Knowledge conflict often arises in retrieval-augmented generation (RAG) systems, where retrieved documents may be inconsistent with one another or contradict the model’s parametric knowledge.Existing benchmarks for investigating the phenomenon have notable limitations, including a narrow focus on the question answering setup, heavy reliance on entity substitution techniques, and a restricted range of conflict types. To address these issues, we propose a knowledge graph (KG)-based framework that generates varied and subtle conflicts between two similar yet distinct contexts, while ensuring interpretability through the explicit relational structure of KGs.Experimental results on our benchmark, MAGIC, provide intriguing insights into the inner workings of LLMs regarding knowledge conflict: both open-source and proprietary models struggle with conflict detection—especially when multi-hop reasoning is required—and often fail to pinpoint the exact source of contradictions.Finally, we present in-depth analyses that serve as a foundation for improving LLMs in integrating diverse, sometimes even conflicting, information.

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Align Attention Heads Before Merging Them: An Effective Way for Converting MHA to GQA
Qingyun Jin | Xiaohui Song | Feng Zhou | Zengchang Qin

Large language models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, as the model size and the input sequence’s length increase, the linearly increasing key-value (KV) cache significantly degrades inference throughput. Therefore, grouped-query attention (GQA), as an alternative to multi-head attention (MHA), has been widely introduced into LLMs. In this work, we propose a cost-effective method for converting MHA into GQA with any compression ratio of KV heads. The key point of our method lies in the application of Procrustes analysis to the attention heads, which enhances the similarity among attention heads while preserving computational invariance, thereby improving the model’s post-training performance. Subsequently, we employ L₀ regularization to prune redundant parameters. The model after pruning can be adapted to the standard GQA framework. Experimental results show that our strategy can compress up to 87.5% KV heads of LLaMA2-7B model and 75% KV heads of Sheared-LLaMA-1.3B with acceptable performance degradation. Our code is released at https://github.com/fpcsong/mha2gqa.

In the current landscape of large language models (LLMs), many evaluation metrics have been developed and used as rewards during training to improve specific metrics. However, balancing these metrics and dynamically adjusting reward weights remains challenging, as current approaches often fail to enhance weaker metrics. To address this, we empirically propose a Dynamic Reward Balancing Optimization framework DRBO to mitigate the “short-board effect” by measuring performance, adjusting reward weights to prioritize weaker metrics, and optimizing the model via reinforcement learning. We apply DRBO to both single-task and multi-type task scenarios, validating its effectiveness in generation with citations and online shopping conversation tasks. The results demonstrate improved overall performance and balanced optimization across multiple metrics, effectively overcoming the diversity and complexity inherent in LLMs. Our codes are available at https://github.com/NuoJohnChen/DRBO.

As Large language models (LLMs) are increasingly deployed in diverse applications, faithfully integrating evolving factual knowledge into these models remains a critical challenge. Continued pre-training on paraphrased data has shown empirical promise for enhancing knowledge acquisition. However, this approach is often costly and unreliable, as it relies on external models or manual effort for rewriting, and may inadvertently alter the factual content. In this work, we hypothesize and empirically show that an LLM’s ability to continually predict the same factual knowledge tokens given diverse paraphrased contexts is positively correlated with its capacity to extract that knowledge via question-answering. Based on this view and aiming to improve generalization to diverse paraphrased contexts, we introduce two strategies to enhance LLMs’ ability to predict the same knowledge tokens given varied contexts, thereby enhancing knowledge acquisition. First, we propose formatting-based data augmentation, which diversifies documents conveying the same knowledge by altering document formats rather than their content, thereby preserving factual integrity. Second, we adopt sharpness-aware minimization as the optimizer to better improve generalization. Extensive experiments demonstrate our methods’ effectiveness in both continued pre-training and instruction tuning, and further gains can be achieved by combining with paraphrased data. Code and data are available at https://github.com/dvlab-research/llm-knowledge-generalization.

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FastCuRL: Curriculum Reinforcement Learning with Stage-wise Context Scaling for Efficient Training R1-like Reasoning Models
Mingyang Song | Mao Zheng | Zheng Li | Wenjie Yang | Xuan Luo

Improving training efficiency continues to be one of the primary challenges in large-scale Reinforcement Learning (RL). In this paper, we investigate how context length and the complexity of training data influence the RL scaling training process of R1-distilled reasoning models, e.g., DeepSeek-R1-Distill-Qwen-1.5B.Our experimental results reveal that: text-green(1) simply controlling the context length and selecting the training data based on the input prompt length can effectively improve the training efficiency of RL scaling, achieving better performance with more concise CoT; text-blue(2) properly scaling the context length helps mitigate entropy collapse; text-redand (3) carefully choosing the context length facilitates achieving efficient LLM training and reasoning. Inspired by these insights, we propose FastCuRL, a curriculum RL framework with stage-wise context scaling to achieve efficient LLM training and reasoning. Extensive experimental results demonstrate that FastCuRL-1.5B-V3 significantly outperforms state-of-the-art reasoning models on five competition-level benchmarks and achieves 49.6% accuracy on AIME 2024. Furthermore, FastCuRL-1.5B-Preview surpasses DeepScaleR-1.5B-Preview on five benchmarks while only using a single node with 8 GPUs and a total of 50% of training steps.

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TR-MTEB: A Comprehensive Benchmark and Embedding Model Suite for Turkish Sentence Representations
Mehmet Selman Baysan | Tunga Gungor

We introduce TR-MTEB, the first large-scale, task-diverse benchmark designed to evaluate sentence embedding models for Turkish. Covering six core tasks as classification, clustering, pair classification, retrieval, bitext mining, and semantic textual similarity, TR-MTEB incorporates 26 high-quality datasets, including native and translated resources. To complement this benchmark, we construct a corpus of 34.2 million weakly supervised Turkish sentence pairs and train two Turkish-specific embedding models using contrastive pretraining and supervised fine-tuning. Evaluation results show that our models, despite being trained on limited resources, achieve competitive performance across most tasks and significantly improve upon baseline monolingual models. All datasets, models, and evaluation pipelines are publicly released to facilitate further research in Turkish natural language processing and low-resource benchmarking.

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ImpRAG: Retrieval-Augmented Generation with Implicit Queries
Wenzheng Zhang | Xi Victoria Lin | Karl Stratos | Wen-tau Yih | Mingda Chen

Retrieval-Augmented Generation (RAG) systems traditionally treat retrieval and generation as separate processes, requiring explicit textual queries to connect them. This separation can limit the ability of models to generalize across diverse tasks. In this work, we propose a query-free RAG system, named ImpRAG, which integrates retrieval and generation into a unified model. ImpRAG allows models to implicitly express their information needs, eliminating the need for human-specified queries. By dividing pretrained decoder-only language models into specialized layer groups, ImpRAG optimizes retrieval and generation tasks simultaneously. Our approach employs a two-stage inference process, using the same model parameters and forward pass for both retrieval and generation, thereby minimizing the disparity between retrievers and language models. Experiments on 8 knowledge-intensive tasks demonstrate that ImpRAG achieves 3.6-11.5 improvements in exact match scores on unseen tasks with diverse formats, highlighting its effectiveness in enabling models to articulate their own information needs and generalize across tasks. Our analysis underscores the importance of balancing retrieval and generation parameters and leveraging generation perplexities as retrieval training objectives for enhanced performance.

Preference optimization methods like DPO have achieved remarkable performance in LLM alignment. However, the evaluation for these methods relies on a single response and overlooks other potential outputs, which could also be generated in real-world applications within this hypothetical space. To address this issue, this paper presents a Hypothesis-based PrEference-aware AnaLysis Framework (HEAL), a novel evaluation paradigm that formulates preference alignment as a re-ranking process within hypothesis spaces. The framework incorporates two complementary metrics: ranking accuracy for evaluating ordinal consistency and preference strength correlation for assessing continuous alignment. To facilitate this framework, we develop UniHypoBench, a unified hypothesis benchmark constructed from diverse instruction-response pairs. Through extensive experiments based on HEAL, with a particular focus on the intrinsic mechanisms of preference learning, we demonstrate that current preference learning methods can effectively capture preferences provided by proxy models while simultaneously suppressing negative samples. These findings contribute to preference learning research through two significant avenues. Theoretically, we introduce hypothesis space analysis as an innovative paradigm for understanding preference alignment. Practically, HEAL offers researchers robust diagnostic tools for refining preference optimization methods, while our empirical results identify promising directions for developing more advanced alignment algorithms capable of comprehensive preference capture.

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A Survey of Multilingual Reasoning in Language Models
Akash Ghosh | Debayan Datta | Sriparna Saha | Chirag Agarwal

While reasoning and multilingual capabilities in Language Models (LMs) have achieved remarkable progress in recent years, their integration into a unified paradigm—multilingual reasoning—is at a nascent stage. Multilingual reasoning requires language models to handle logical reasoning across languages while addressing misalignment, biases, and challenges in low-resource settings. This survey provides the first in-depth review of multilingual reasoning in LMs. In this survey, we provide a systematic overview of existing methods that leverage LMs for multilingual reasoning, specifically outlining the challenges, motivations, and foundational aspects of applying language models to reason across diverse languages. We provide an overview of the standard data resources used for training multilingual reasoning in LMs and the evaluation benchmarks employed to assess their multilingual capabilities. Next, we analyze various state-of-the-art methods and their performance on these benchmarks. Finally, we explore future research opportunities to improve multilingual reasoning in LMs, focusing on enhancing their ability to handle diverse languages and complex reasoning tasks.

Most of the existing work focuses on enabling LLMs to leverage legal rules (, law articles) to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. To better evaluate the LLMs’ capabilities on the task, in this work, we propose a new challenge task: Legal Paragraph Prediction (LPP), which aims to predict the legal paragraph given criminal facts. Moreover, to enhance the legal reasoning ability of LLMs, we propose a novel framework CLEAR, enabling LLMs to analyze legal cases with the guidance of legal rule insights. The CLEAR contains four key components, where the Legal Rules Retriever aims to retrieve legal rule knowledge, and the Rule Insights Generator is used to generate legal insights guiding the LLM’s reasoning, then the Case Analyzer analyze the case with the guidance of legal rule insights given criminal facts. Finally, the Legal Reasoner synthesizes the criminal facts, legal rule insights, and analysis results to derive the final decision. By conducting extensive experiments on a real-world dataset, experimental results validate the effectiveness of our proposed model. Our codes and dataset are available at https://anonymous.4open.science/r/CLEAR-3048.

The widespread use of cloud-based Large Language Models (LLMs) has heightened concerns over user privacy, as sensitive information may be inadvertently exposed during interactions with these services. To protect privacy before sending sensitive data to those models, we suggest sanitizing sensitive text using two common strategies used by humans: i) deleting sensitive expressions, and ii) obscuring sensitive details by abstracting them. To explore the issues and develop a tool for text rewriting, we curate the first corpus, coined , through both crowdsourcing and the use of large language models (LLMs). Compared to the prior works based on differential privacy, which lead to a sharp drop in information utility and unnatural texts, the human-inspired approaches result in more natural rewrites and offer an improved balance between privacy protection and data utility, as demonstrated by our extensive experiments.

Research ideation is crucial for scientific progress, but the exponential increase in scientific literature makes it challenging to stay updated and identify impactful directions. Recent developments in large language models(LLMs) offer a promising avenue to automate this process. However, existing methods for idea generation either trivially prompt LLMs or expose LLMs to extensive literature without indicating useful information. Inspired by human research processes, we propose a Chain-of-Ideas (CoI) agent, an LLM-based agent that organizes relevant literature in a chain structure to effectively mirror the progressive development in a research domain. This organization helps LLMs better grasp current advancements, thereby improving ideation capabilities. Further, we present Idea Arena, a protocol for evaluating idea-generation methods from different perspectives, which aligns closely with the preferences of human researchers. Experiments show that CoI agent consistently outperforms existing methods and matches human quality in idea generation. Moreover, CoI agent is budget-friendly, requiring only $0.50 to generate a candidate idea and its experimental design.

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Unveiling Multimodal Processing: Exploring Activation Patterns in Multimodal LLMs for Interpretability and Efficiency
Chuan Wu | Meng Su | Youxuan Fang | Shaolin Zhu

Recent Multimodal Large Language Models (MLLMs) have achieved remarkable advancements, yet their internal mechanisms for concurrently processing diverse modalities like text, image, and audio remain largely opaque. In this paper, we propose a methodology to convert dense MLLMs into fine-grained Mixture-of-Experts (MoE) architectures. This allows us to visually investigate their multimodal activation patterns through expert activation frequency heatmaps. Conducting comprehensive experiments on representative MLLMs, we analyze the similarities and differences in internal neuron activations when handling distinct modalities. Specifically, we examine the distribution of high-frequency activated experts, the distinct roles of high-frequency (e.g., fundamental logic) and low-frequency (e.g., domain-specific concepts) multimodal shared experts, and the prevalence and localization of modality-specific experts. Furthermore, we explore leveraging these discovered activation discrepancies to guide sparse activation and model pruning. Experimental results demonstrate that our approach substantially outperforms random expert pruning and can achieve comparable or even superior performance to the original unpruned models while utilizing significantly fewer active parameters. Our work not only sheds light on the multimodal processing mechanisms within MLLMs but also provides a practical pathway toward developing more interpretable and efficient multimodal systems.

Well-designed prompts are crucial for enhancing Large language models’ (LLMs) reasoning capabilities while aligning their outputs with task requirements across diverse domains. However, manually designed prompts require expertise and iterative experimentation. While existing prompt optimization methods aim to automate this process, they rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain. To address this, we propose Self-Supervised Prompt Optimization (SPO), a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without requiring external reference. Motivated by the observations that prompt quality manifests directly in LLM outputs and LLMs can effectively assess adherence to task requirements, we derive evaluation and optimization signals purely from output comparisons. Specifically, SPO selects superior prompts through pairwise output comparisons evaluated by an LLM evaluator, followed by an LLM optimizer that aligns outputs with task requirements. Extensive experiments demonstrate that SPO outperforms state-of-the-art prompt optimization methods, achieving comparable or superior results with significantly lower costs (e.g., 1.1% to 5.6% of existing methods) and fewer samples (e.g., three samples).

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Polish-English medical knowledge transfer: A new benchmark and results
Łukasz Grzybowski | Jakub Pokrywka | Michał Ciesiółka | Jeremi Ignacy Kaczmarek | Marek Kubis

Large Language Models (LLMs) have demonstrated significant potential in specialized tasks, including medical problem-solving. However, most studies predominantly focus on English-language contexts. This study introduces a novel benchmark dataset based on Polish medical licensing and specialization exams (LEK, LDEK, PES). The dataset, sourced from publicly available materials provided by the Medical Examination Center and the Chief Medical Chamber, includes Polish medical exam questions, along with a subset of parallel Polish-English corpora professionally translated for foreign candidates. By structuring a benchmark from these exam questions, we evaluate state-of-the-art LLMs, spanning general-purpose, domain-specific, and Polish-specific models, and compare their performance with that of human medical students and doctors. Our analysis shows that while models like GPT-4o achieve near-human performance, challenges persist in cross-lingual translation and domain-specific understanding. These findings highlight disparities in model performance across languages and medical specialties, emphasizing the limitations and ethical considerations of deploying LLMs in clinical practice.

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Hard Negatives, Hard Lessons: Revisiting Training Data Quality for Robust Information Retrieval with LLMs
Nandan Thakur | Crystina Zhang | Xueguang Ma | Jimmy Lin

Training robust retrieval and reranker models typically relies on large-scale retrieval datasets; for example, the BGE collection contains 1.6 million query-passage pairs sourced from various data sources.However, we find that certain datasets can negatively impact model effectiveness \textemdashpruning 8 out of 15 datasets from the BGE collection, reduces the training set size by 2.35×, surprisingly increases nDCG@10 on BEIR by 1.0 point.This motivates a deeper examination of training data quality, with a particular focus on “false negatives”, where relevant passages are incorrectly labeled as irrelevant.We utilize LLMs as a simple, cost-effective approach to *identify* and *relabel* false negatives in training datasets.Experimental results show that relabeling false negatives as true positives improves both E5 (base) and Qwen2.5-7B retrieval models by 0.7-1.4 points on BEIR and by 1.7-1.8 points at nDCG@10 on zero-shot AIR-Bench evaluation.Similar gains are observed for rerankers fine-tuned on the relabeled data, such as Qwen2.5-3B on BEIR.The reliability of LLMs to identify false negatives is supported by human annotation results. Our training dataset and code are publicly available.

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EventRelBench: A Comprehensive Benchmark for Evaluating Event Relation Understanding in Large Language Models
Jie Gong | Biaoshuai Zheng | Qiwang Hu

Understanding event relationships is critical for tasks such as narrative comprehension, information extraction, and reasoning in natural language processing. Despite the remarkable advancements of large language models (LLMs) across diverse NLP tasks, current studies have not systematically evaluated their ability to capture the complex of event relations. To this end, we aim to assess LLMs on event relationship extraction (ERE) by designing the benchmark EventRelBench. EventRelBench comprises 35K diverse event relation questions covering four key categories—coreference, temporal, causal, and supersub relations. These questions are provided at two levels of granularity: document-level and sentence-level. Extensive experiments on different sizes and types of LLMs show that existing LLMs still fall short in accurately extracting and understanding event relationships. To address this gap, we introduce EventRelInst, a 48K instruction fine‐tuning dataset in the event relation extraction domain. Experimental results not only highlight the shortcomings of current general-purpose LLMs in extracting event relationships but also demonstrate the effectiveness of EventRelInst. Both EventRelBench and EventRelBench will be publicly available.

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S2LPP: Small-to-Large Prompt Prediction across LLMs
Liang Cheng | Tianyi Li | Zhaowei Wang | Mark Steedman

The performance of pre-trained Large Language Models (LLMs) is often sensitive to nuances in prompt templates, requiring careful prompt engineering, adding costs in terms of computing and human effort. In this study, we present experiments encompassing multiple LLMs variants of varying sizes aimed at probing their preference with different prompts. Through experiments on Question Answering, we show prompt preference consistency across LLMs of different sizes. We also show that this consistency extends to other tasks, such as Natural Language Inference. Utilizing this consistency, we propose a method to use a smaller model to select effective prompt templates for a larger model. We show that our method substantially reduces the cost of prompt engineering while consistently matching performance with optimal prompts among candidates. More importantly, our experiment shows the efficacy of our strategy across fourteen LLMs and its applicability to a broad range of NLP tasks, highlighting its robustness.

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DroidCall: A Dataset for LLM-powered Android Intent Invocation
Weikai Xie | Li Zhang | Shihe Wang | Rongjie Yi | Mengwei Xu

The growing capabilities of large language models in natural language understanding significantly strengthen existing agentic systems. To power performant on-device mobile agents for better data privacy, we introduce DroidCall, the first training and testing dataset for accurate Android Intent invocation. With a highly flexible and reusable data generation pipeline, we constructed 10k samples in DroidCall. Given a task instruction in natural language, small language models such as Qwen2.5-3B and Gemma2-2B fine-tuned with DroidCall can approach or even surpass the capabilities of GPT-4o for accurate Android intent invocation. We also provide an end-to-end Android app equipped with these fine-tuned models to demonstrate the Android intent invocation process. The code and dataset are available at https://github.com/UbiquitousLearning/DroidCall

Training tool-augmented LLMs has emerged as a promising approach to enhancing language models’ capabilities for complex tasks. The current supervised fine-tuning paradigm relies on constructing extensive domain-specific datasets to train models. However, this approach often struggles to generalize effectively to unfamiliar or intricate tool-use scenarios. Recently, reinforcement learning (RL) paradigm can endow LLMs with superior reasoning and generalization abilities. In this work, we address a key question: Can the pure RL be used to effectively elicit a model’s intrinsic reasoning capabilities and enhance the tool-agnostic generalization? We propose a dynamic generalization-guided reward design for rule-based RL, which progressively shifts rewards from exploratory to exploitative tool-use patterns. Based on this design, we introduce the Tool-Zero series models. These models are trained to enable LLMs to autonomously utilize general tools by directly scaling up RL from Zero models (i.e., base models without post-training). Experimental results demonstrate that our models achieve over 7% performance improvement compared to both SFT and RL-with-SFT models under the same experimental settings. These gains are consistently replicated across cross-dataset and intra-dataset evaluations, validating the effectiveness and robustness of our methods.

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INREACT: An Inspire-Then-Reinforce Training Framework For Multimodal GUI Agent
Yuanlei Wang | Liuzhou Zhang | Haohao Luo | Ying Shen

Graphical User Interface (GUI) interaction, which aims to develop an intelligent GUI agent that executes user instructions to perform tasks such as installing applications by controlling digital devices, has gained significant attention due to its practical value. Although current advanced multimodal large language models (LLMs) provide GUI agents with robust perception and reasoning capabilities, they often struggle with the precise localization of small elements. To tackle this problem, we propose InReAct, a multimodal GUI agent framework that unifies observing, thinking, and acting for precise and interpretable decision-making. It is trained via a two-stage process: curriculum learning to progressively build perception, grounding, and reasoning abilities, followed by reinforcement learning to refine pixel-level grounding with an outcome-based reward. We introduce a rule-based reward function that jointly optimizes action-type selection and pixel-level localization accuracy. Experimental results on multiple datasets demonstrate the superiority of InReAct in both grounding and navigation tasks.

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Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models
Juraj Vladika | Mahdi Dhaini | Florian Matthes

The growing capabilities of Large Language Models (LLMs) can enhance healthcare by assisting medical researchers, physicians, and improving access to health services for patients. LLMs encode extensive knowledge within their parameters, including medical knowledge derived from many sources. However, the knowledge in LLMs can become outdated over time, posing challenges in keeping up with evolving medical recommendations and research. This can lead to LLMs providing outdated health advice or failures in medical reasoning tasks. To address this gap, our study introduces two novel biomedical question-answering (QA) datasets derived from medical systematic literature reviews: MedRevQA, a general dataset of 16,501 biomedical QA pairs, and MedChangeQA, a subset of 512 QA pairs whose verdict changed though time. By evaluating the performance of eight popular LLMs, we find that all models exhibit memorization of outdated knowledge to some extent. We provide deeper insights and analysis, paving the way for future research on this challenging aspect of LLMs.

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Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search
Shuo Huang | Xingliang Yuan | Gholamreza Haffari | Lizhen Qu

The increasing adoption of large language models (LLMs) in cloud-based services has raised significant privacy concerns, as user inputs may inadvertently expose sensitive information. Existing text anonymization and de-identification techniques, such as rule-based redaction and scrubbing, often struggle to balance privacy preservation with text naturalness and utility. In this work, we propose a zero-shot, tree-search-based iterative sentence rewriting algorithm that systematically obfuscates or deletes private information while preserving coherence, relevance, and naturalness. Our method incrementally rewrites privacy-sensitive segments through a structured search guided by a reward model, enabling dynamic exploration of the rewriting space. Experiments on privacy-sensitive datasets show that our approach significantly outperforms existing baselines, achieving a superior balance between privacy protection and utility preservation.

Korean legal knowledge is subject to frequent temporal updates driven by societal needs and government policies. Even minor modifications to legal provisions can have significant consequences, yet continuously retraining large language models (LLMs) to incorporate such updates is resource-intensive and impractical. To address this, we propose KoLEG, an on-the-fly Korean Legal knowledge editing framework enhanced with continuous retrieval. KoLEG employs an Editing-Aware Learning Strategy and a LawEdit Retriever, which together adaptively integrate subtle linguistic nuances and continuous legislative amendments. To support this task, we construct the Korean Legislative Amendment Dataset, explicitly designed for continuous legal knowledge updates with attention to both temporal dynamics and linguistic subtleties. KoLEG outperforms existing locate-then-edit and retrieval-based editing methods, demonstrating superior effectiveness in legal knowledge editing while preserving linguistic capabilities. Furthermore, KoLEG maintains robust performance in sequential editing, improves performance on precedent application tasks, and is qualitatively validated by legal experts.

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HARE: an entity and relation centric evaluation framework for histopathology reports
Yunsoo Kim | Michal Wen Sheue Ong | Alex Shavick | Honghan Wu | Adam P. Levine

Medical domain automated text generation is an active area of research and development; however, evaluating the clinical quality of generated reports remains a challenge, especially in instances where domain-specific metrics are lacking, e.g. histopathology. We propose HARE (Histopathology Automated Report Evaluation), a novel entity and relation centric framework, composed of a benchmark dataset, a named entity recognition (NER) model, a relation extraction (RE) model, and a novel metric, which prioritizes clinically relevant content by aligning critical histopathology entities and relations between reference and generated reports. To develop the HARE benchmark, we annotated 813 de-identified clinical diagnostic histopathology reports and 652 histopathology reports from The Cancer Genome Atlas (TCGA) with domain-specific entities and relations. We fine-tuned GatorTronS, a domain-adapted language model to develop HARE-NER and HARE-RE which achieved the highest overall F1-score (0.915) among the tested models. The proposed HARE metric outperformed traditional metrics including ROUGE and Meteor, as well as radiology metrics such as RadGraph-XL, with the highest correlation and the best regression to expert evaluations (higher than the second best method, GREEN, a large language model based radiology report evaluator, by Pearson r = 0.168, Spearman 𝜌 = 0.161, Kendall 𝜏 = 0.123, R² = 0.176, RMSE = 0.018). We release HARE, datasets, and the models at https://github.com/knowlab/HARE to foster advancements in histopathology report generation, providing a robust framework for improving the quality of reports.

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VeriFastScore: Speeding up long-form factuality evaluation
Rishanth Rajendhran | Amir Zadeh | Matthew Sarte | Chuan Li | Mohit Iyyer

Metrics like FactScore and VeriScore that evaluate long-form factuality operate by decomposing an input response into atomic claims and then individually verifying each claim. While effective and interpretable, these methods incur numerous LLM calls and can take upwards of 100s to evaluate a single response, limiting their practicality in large-scale evaluation and training scenarios. To address this, we propose VeriFastScore, which leverages synthetic data to fine-tune Llama3.1 8B for simultaneously extracting and verifying all verifiable claims within a given text based on evidence from Google Search. We show that this task cannot be solved via few-shot prompting with closed LLMs due to its complexity: the model receives ∼4K tokens of evidence on average and needs to concurrently decompose claims, judge their verifiability, and verify them against noisy evidence. However, our fine-tuned VeriFastScore model demonstrates strong correlation with the original VeriScore pipeline at both the example level (r=0.80) and system level (r=0.94) while achieving an overall speedup of 6.6× (9.9 × excluding evidence retrieval) over VeriScore. To facilitate future factuality research, we publicly release our VeriFastScore model and synthetic datasets.

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B-REASO: A Multi-Level Multi-Faceted Bengali Evaluation Suite for Foundation Models
Md Tanzib Hosain | Md Kishor Morol

The fast growth of large language models (LLMs) necessitates the urgent need for new NLP benchmarks. We provide B-REASO, the first inclusive Bengali assessment suite created to evaluate advanced foundation model knowledge and reasoning skills in a Bengali language setup. The B-REASO includes multiple-choice questions with four different degrees of difficulty: professional, college, high school, and middle school. The questions cover 50 different fields, from science and engineering to the humanities. Alongside B-REASO, there is B-REASO HEAVY, a subset of extremely difficult B-REASO topics that need for sophisticated reasoning skills to answer. We do a thorough assessment of the most sophisticated LLMs on B-REASO, encompassing models with an English focus. Findings show that only Claude-3.5-Sonnet was able to get an average accuracy of more than 65%, indicating that contemporary LLMs still have a long way to go. We hope that B-REASO will support the creation and expansion of foundation models for Bengali users by assisting in the analysis of significant advantages and disadvantages of these models. We open-source our code and data at https://github.com/kraritt/b-reaso.

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Extracting Conceptual Spaces from LLMs Using Prototype Embeddings
Nitesh Kumar | Usashi Chatterjee | Steven Schockaert

Conceptual spaces represent entities and concepts using cognitively meaningful dimensions, typically referring to perceptual features. Such representations are widely used in cognitive science and have the potential to serve as a cornerstone for explainable AI. Unfortunately, they have proven notoriously difficult to learn, although recent LLMs appear to capture the required perceptual features to a remarkable extent. Nonetheless, practical methods for extracting the corresponding conceptual spaces are currently still lacking. While various methods exist for extracting embeddings from LLMs, extracting conceptual spaces also requires us to encode the underlying features. In this paper, we propose a strategy in which features (e.g. sweetness) are encoded by embedding the description of a corresponding prototype (e.g. a very sweet food). To improve this strategy, we fine-tune the LLM to align the prototype embeddings with the corresponding conceptual space dimensions. Our empirical analysis finds this approach to be highly effective.

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FC-Attack: Jailbreaking Multimodal Large Language Models via Auto-Generated Flowcharts
Ziyi Zhang | Zhen Sun | Zongmin Zhang | Jihui Guo | Xinlei He

Multimodal Large Language Models (MLLMs) have become powerful and widely adopted in some practical applications.However, recent research has revealed their vulnerability to multimodal jailbreak attacks, whereby the model can be induced to generate harmful content, leading to safety risks. Although most MLLMs have undergone safety alignment, recent research shows that the visual modality is still vulnerable to jailbreak attacks.In our work, we discover that by using flowcharts with partially harmful information, MLLMs can be induced to provide additional harmful details. Based on this, we propose a jailbreak attack method based on auto-generated flowcharts, FC-Attack.Specifically, FC-Attack first fine-tunes a pre-trained LLM to create a step-description generator based on benign datasets.The generator is then used to produce step descriptions corresponding to a harmful query, which are transformed into flowcharts in 3 different shapes (vertical, horizontal, and S-shaped) as visual prompts.These flowcharts are then combined with a benign textual prompt to execute the jailbreak attack on MLLMs.Our evaluations on Advbench show that FC-Attack attains an attack success rate of up to 96% via images and up to 78% via videos across multiple MLLMs.Additionally, we investigate factors affecting the attack performance, including the number of steps and the font styles in the flowcharts. We also find that FC-Attack can improve the jailbreak performance from 4% to 28% in Claude-3.5 by changing the font style.To mitigate the attack, we explore several defenses and find that AdaShield can largely reduce the jailbreak performance but with the cost of utility drop.

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Multilingual Data Filtering using Synthetic Data from Large Language Models
Jonas Waldendorf | Barry Haddow | Alexandra Birch | Mateusz Klimaszewski

Filtering data, particularly data scraped from the internet, has long been recognised as a means to improve model performance. Recent studies have shown that effective filters can be created by utilising Large Language Models (LLMs) to synthetically label data, which is then used to train smaller neural models for filtering purposes. However, this approach has been tested mainly in English. Our paper extends this approach to languages beyond English, including languages not officially supported by the LLM. We validate our results on the downstream task of NMT and demonstrate that our approach is effective at both filtering parallel text for translation quality and filtering for domain specificity. For training the filtering model, we experiment with two different objectives for finetuning pre-trained transformers, as well as an efficient approach based on *n*-gram language models.

Despite the state-of-the-art performance of Large Language Models (LLMs), these models often suffer from hallucinations, which can undermine their performance in critical applications. In this work, we propose SAFE, a novel framework for detecting and mitigating hallucinations by leveraging Sparse Autoencoders (SAEs). While hallucination detection techniques and SAEs have been explored independently, their synergistic application in a comprehensive system, particularly for hallucination-aware query enrichment, has not been fully investigated. To validate the effectiveness of SAFE, we evaluate it on two models with available SAEs across four diverse cross-domain datasets designed to assess hallucination problems. Empirical results demonstrate that SAFE consistently improves query generation accuracy and mitigates hallucinations across all datasets, achieving accuracy improvements of up to 29.45%.

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Soteria: Language-Specific Functional Parameter Steering for Multilingual Safety Alignment
Somnath Banerjee | Sayan Layek | Pratyush Chatterjee | Animesh Mukherjee | Rima Hazra

Ensuring consistent safety across multiple languages remains a significant challenge for large language models (LLMs). We introduce Soteria, a lightweight yet powerful strategy that locates and minimally adjusts the “functional heads” most responsible for harmful content generation in each language. By altering only a fraction of parameters, Soteria drastically reduces policy violations without sacrificing overall model performance, even in low-resource settings. To rigorously evaluate our approach, we also present XThreatBench, a specialized multilingual dataset capturing fine-grained harmful behaviors drawn from real policy guidelines. Experiments with leading open-source LLMs (e.g., Llama, Qwen, Mistral) show that Soteria consistently improves safety metrics across high-, mid-, and low-resource languages. These findings highlight a promising path toward scalable, linguistically attuned, and ethically aligned LLMs worldwide.

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LLMs as a synthesis between symbolic and distributed approaches to language
Gemma Boleda

Since the middle of the 20th century, a fierce battle is being fought between symbolic and distributed approaches to language and cognition. The success of deep learning models, and LLMs in particular, has been alternatively taken as showing that the distributed camp has won, or dismissed as an irrelevant engineering development. In this position paper, I argue that deep learning models for language actually represent a synthesis between the two traditions. This is because 1) deep learning architectures allow for both distributed/continuous/fuzzy and symbolic/discrete/categorical-like representations and processing; 2) models trained on language make use of this flexibility. In particular, I review recent research in interpretability that showcases how a substantial part of morphosyntactic knowledge is encoded in a near-discrete fashion in LLMs. This line of research suggests that different behaviors arise in an emergent fashion, and models flexibly alternate between the two modes (and everything in between) as needed. This is possibly one of the main reasons for their wild success; and it makes them particularly interesting for the study of language. Is it time for peace?

Mental health issues are worsening in today’s competitive society, such as depression and anxiety. Traditional healings like counseling and chatbots fail to engage effectively, they often provide generic responses lacking emotional depth. Although large language models (LLMs) have the potential to create more human-like interactions, they still struggle to capture subtle emotions. This requires LLMs to be equipped with human-like adaptability and warmth. To fill this gap, we propose the MIND (Multi-agent INner Dialogue), a novel paradigm that provides more immersive psychological healing environments. Considering the strong generative and role-playing ability of LLM agents, we predefine an interactive healing framework and assign LLM agents different roles within the framework to engage in interactive inner dialogues with users, thereby providing an immersive healing experience. We conduct extensive human experiments in various real-world healing dimensions, and find that MIND provides a more user-friendly experience than traditional paradigms. This demonstrates that MIND effectively leverages the significant potential of LLMs in psychological healing.

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A Monte-Carlo Sampling Framework For Reliable Evaluation of Large Language Models Using Behavioral Analysis
Davood Wadi | Marc Fredette

Scientific evaluation of Large Language Models is an important topic that quantifies any degree of progress we make with new models. Even though current LLMs show high level of accuracy on benchmark datasets, the single-sample approach to evaluating them is not sufficient as it ignores high entropy of LLM responses. We introduce a Monte-Carlo evaluation framework for evaluating LLMs that follows behavioral science methodologies and provides statistical guarantees for estimates of performance. We test our framework on multiple LLMs to see if they are susceptible to cognitive biases. We find significant effect of prompts that induce cognitive biases in LLMs, raising questions about their reliability in social sciences and business. We also see higher susceptibility of newer and larger LLMs to cognitive biases, which shows a development towards more human-like and less rational LLM responses. We conclude by calling for the use of Monte-Carlo sampling as opposed to pass@1 for the broader LLM evaluations.

Rapid integration of large language models (LLMs) into societal applications has intensified concerns about their alignment with universal ethical principles, as their internal value representations remain opaque despite behavioral alignment advancements. Current approaches struggle to systematically interpret how values are encoded in neural architectures, limited by datasets that prioritize superficial judgments over mechanistic analysis. We introduce ValueLocate, a mechanistic interpretability framework grounded in the Schwartz Values Survey, to address this gap. Our method first constructs ValueInsight, a dataset that operationalizes four dimensions of universal value through behavioral contexts in the real world. Leveraging this dataset, we develop a neuron identification method that calculates activation differences between opposing value aspects, enabling precise localization of value-critical neurons without relying on computationally intensive attribution methods. Our proposed validation method demonstrates that targeted manipulation of these neurons effectively alters model value orientations, establishing causal relationships between neurons and value representations. This work advances the foundation for value alignment by bridging psychological value frameworks with neuron analysis in LLMs.

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Likelihood Variance as Text Importance for Resampling Texts to Map Language Models
Momose Oyama | Ryo Kishino | Hiroaki Yamagiwa | Hidetoshi Shimodaira

We address the computational cost of constructing a model map, which embeds diverse language models into a common space for comparison via KL divergence. The map relies on log-likelihoods over a large text set, making the cost proportional to the number of texts. To reduce this cost, we propose a resampling method that selects important texts with weights proportional to the variance of log-likelihoods across models for each text. Our method significantly reduces the number of required texts while preserving the accuracy of KL divergence estimates. Experiments show that it achieves comparable performance to uniform sampling with about half as many texts, and also facilitates efficient incorporation of new models into an existing map. These results enable scalable and efficient construction of language model maps.

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Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection
Hoang Phan | Victor Li | Qi Lei

Large language models (LLMs) have revolutionized natural language processing with their ability to generate coherent and contextually relevant text. However, their deployment raises significant concerns about the potential for generating harmful or inappropriate content. In this paper, we introduce Progressive Self-Reflection, a novel inference-time technique that empowers LLMs to self-monitor and correct their outputs dynamically. Experimental results demonstrate that applying our proposed method to Llama-3.1-8B-Instruct reduces the attack success rate from 77.47% to 5.86%, to Llama-3.1-8B base from 89.70% to 5.56%, and to Qwen2.5-7B-Instruct from 44.44% to 3.84%, without additional training. Furthermore, our method maintains their original performance across diverse tasks, including summarization, general knowledge, reasoning, and mathematics. Our approach acts as a test-time scaling method, where additional self-reflection rounds enhance safety at the cost of inference overhead. To balance safety with computational efficiency, we introduce a lightweight self-reflection predictor that estimates the optimal number of reflection rounds based on input complexity. This adaptive mechanism prevents unnecessary self-assessment on benign inputs while ensuring thorough evaluation when encountering potentially harmful content. Our findings suggest that Progressive Self-Reflection serves as a scalable test-time approach, enhancing LLM safety by dynamically allocating computational resources in proportion to the input’s risk profile.

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Efficient Integration of External Knowledge to LLM-based World Models via Retrieval-Augmented Generation and Reinforcement Learning
Chang Yang | Xinrun Wang | Qinggang Zhang | Qi Jiang | Xiao Huang

World models achieve remarkable success in predicting future states and planning in complex environments and Large Language Models (LLMs) serve as promising foundation to build general world models. However, their performances are usually constrained by the limited external knowledge to specific environments. Existing research attempts to enhance LLM-based world models through prompting or fine-tuning approaches, which are either requiring human knowledge or computationally extensive. Therefore, we introduce Retrieval-Augmented World Models (RAWM), a novel framework that leverages retrieval-augmented generation to efficiently integrate the external knowledge to LLM-based world models. Our main contributions are threefold: (i) We introduce a memory system and design an embedding model to retrieve relevant experiences as the in-context examples to improve the world model’s predictive accuracy. (ii) We develop a reinforcement learning (RL) training pipeline that fine-tunes a small MLP head on the pre-trained embedding model using Proximal Policy Optimization (PPO), further enhancing prediction performance. (iii) We conduct extensive experiments across three diverse environments, i.e., Game24, BlocksWorld, and BabyAI, demonstrating that RAWM consistently outperforms baseline models and exhibits strong generalizability. By leveraging the retrieval-augmented generation and the efficient RL training pipeline, RAWM dynamically utilizes relevant historical experiences and equips LLMs with environment-specific external knowledge without retraining, enabling more accurate and generalizable predictions.

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Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes
Tyler Loakman | William Thorne | Chenghua Lin

Humour, as a complex language form, is derived from myriad aspects of life. Whilst existing work on computational humour has focussed almost exclusively on short pun-based jokes, we investigate whether the ability of Large Language Models (LLMs) to explain humour depends on the particular form. We compare models’ joke explanation abilities from simple puns to complex topical humour that requires esoteric knowledge of real-world entities and events. To this end, we curate a dataset of 600 jokes across 4 joke types and manually write high-quality explanations. These jokes include heterographic and homographic puns, contemporary internet humour, and topical jokes. Using this dataset, we compare the zero-shot abilities of a range of LLMs to accurately and comprehensively explain jokes of different types, identifying key research gaps in the task of humour explanation. We find that none of the tested models (including reasoning models) are capable of reliably generating adequate explanations of all joke types, further highlighting the narrow focus of most existing works on overly simple joke forms.

As large language models (LLMs) become integral to social and interactive applications, the ability to model, control, and evaluate their personality traits has become a critical area of research. This survey provides a comprehensive and structured overview of the LLM-driven personality scenario. We introduce a functional taxonomy that organizes the field by how personality is modeled (from rule-based methods to model-centric and system-level LLM techniques), across which modalities it is expressed (extending beyond text to vision, speech, and immersive virtual reality), and how it is validated (covering both qualitative and quantitative evaluation paradigms). By contextualizing current advances and systematically analyzing the limitations of existing methods including subjectivity, context dependence, limited multimodal integration, and the lack of standardized evaluation protocols, we identify key research gaps. This survey serves as a guide for future inquiry, paving the way for the development LLMs with more consistent consistent, expressive, and trustworthy personality traits.

The rapid development of advanced large language models (LLMs) has made AI-generated text indistinguishable from human-written text. Previous work on detecting AI-generated text has made effective progress, but has not involved modern Chinese poetry. Due to the distinctive characteristics of modern Chinese poetry, it is difficult to identify whether a poem originated from humans or AI. The proliferation of AI-generated modern Chinese poetry has significantly disrupted the poetry ecosystem. Based on the urgency of identifying AI-generated poetry in the real Chinese world, this paper proposes a novel benchmark for detecting LLMs-generated modern Chinese poetry. We first construct a high-quality dataset, which includes both 800 poems written by six professional poets and 41,600 poems generated by four mainstream LLMs. Subsequently, we conduct systematic performance assessments of six detectors on this dataset. Experimental results demonstrate that current detectors cannot be used as reliable tools to detect modern Chinese poems generated by LLMs. The most difficult poetic features to detect are intrinsic qualities, especially style. The detection results verify the effectiveness and necessity of our proposed benchmark. Our work lays a foundation for future detection of AI-generated poetry.

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Leveraging the Cross-Domain & Cross-Linguistic Corpus for Low Resource NMT: A Case Study On Bhili-Hindi-English Parallel Corpus
Pooja Singh | Shashwat Bhardwaj | Vaibhav Sharma | Sandeep Kumar

The linguistic diversity of India poses significant machine translation challenges, especially for underrepresented tribal languages like Bhili, which lack high-quality linguistic resources. This paper addresses the gap by introducing Bhili-Hindi-English Parallel Corpus (BHEPC), the first and largest parallel corpus worldwide comprising 110,000 meticulously curated sentences across Bhili, Hindi, and English. The corpus was created with the assistance of expert human translators. BHEPC spans critical domains such as education, administration, and news, establishing a valuable benchmark for research in low resource machine translation. To establish a comprehensive Bhili Machine Translation benchmark, we evaluated a wide range of proprietary and open-source Multilingual Large Language Models (MLLMs) on bidirectional translation tasks between English/Hindi and Bhili. Comprehensive evaluation demonstrates that the fine-tuned NLLB-200 distilled 600M variant model outperforms others, highlighting the potential of multilingual models in low resource scenarios. Furthermore, we investigated the generative translation capabilities of multilingual LLMs on BHEPC using in-context learning, assessing performance under cross-domain generalization and quantifying distributional divergence. This work bridges a critical resource gap and promotes inclusive natural language processing technologies for low-resource and marginalized languages globally.

While Large Language Models (LLMs) have demonstrated impressive performance across natural language generation tasks, their ability to generate truly creative content—characterized by novelty, diversity, surprise, and quality—remains limited. Existing methods for enhancing LLM creativity often focus narrowly on diversity or specific tasks, failing to address creativity’s multifaceted nature in a generalizable way. In this work, we propose Creative Preference Optimization (CrPO), a novel alignment method that injects signals from multiple creativity dimensions into the preference optimization objective in a modular fashion. We train and evaluate creativity-augmented versions of several models using CrPO and MuCE, a new large-scale human preference dataset spanning over 200,000 human-generated responses and ratings from more than 30 psychological creativity assessments. Our models outperform strong baselines, including GPT-4o, on both automated and human evaluations, producing more novel, diverse, and surprising generations while maintaining high output quality. Additional evaluations on NoveltyBench further confirm the generalizability of our approach. Together, our results demonstrate that directly optimizing for creativity within preference frameworks is a promising direction for advancing the creative capabilities of LLMs without compromising output quality.

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Assistant-Guided Mitigation of Teacher Preference Bias in LLM-as-a-Judge
Zhuo Liu | Moxin Li | Xun Deng | Qifan Wang | Fuli Feng

LLM-as-a-Judge employs large language models (LLMs), such as GPT-4, to evaluate the quality of LLM-generated responses, gaining popularity for its cost-effectiveness and strong alignment with human evaluations. However, training proxy judge models using evaluation data generated by powerful teacher models introduces a critical yet previously overlooked issue: teacher preference bias, where the proxy judge model learns a biased preference for responses from the teacher model. To tackle this problem, we propose a novel setting that incorporates an additional assistant model, which is not biased toward the teacher model’s responses, to complement the training data. Building on this setup, we introduce AGDe-Judge, a three-stage framework designed to debias from both the labels and feedbacks in the training data. Extensive experiments demonstrate that AGDe-Judge effectively reduces teacher preference bias while maintaining strong performance across six evaluation benchmarks. .

Retrieval-augmented generation (RAG) has proven effective in enhancing the knowledge coverage of large language models (LLMs) and mitigating hallucinations by incorporating external retrieved documents. However, documents deemed relevant by the retriever are not necessarily helpful for answer generation, and including misleading information can even degrade performance. Existing efforts to estimate document utility often rely on the downstream generation performance, which conflates the influence of external documents with the intrinsic knowledge of the LLM, thereby obscuring the actual contribution of the retrieved content. To address this, this paper proposes Uplit-RAG, a uplift-driven knowledge preference alignment framework for RAG. Specifically, we first propose an uplift-based definition of document utility that quantifies each document’s marginal benefit over the LLM’s internal knowledge. We then optimize the reranker with three alignment objectives to identify and prioritize documents based on their uplift. This enables dynamic selection of documents that address the LLM’s knowledge gaps, going beyond fixed top-k selection, while reducing reference redundancy and the computational overhead of the LLM’s input. Extensive experiments demonstrate the effectiveness of Uplift-RAG.

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Sugar-Coated Poison: Benign Generation Unlocks Jailbreaking
Yuhang Wu | Yu-Jie Xiong | Hao Zhang | Jia-Chen Zhang | Zheng Zhou

With the increasingly deep integration of large language models (LLMs) across diverse domains, the effectiveness of their safety mechanisms is encountering severe challenges. Currently, jailbreak attacks based on prompt engineering, which induce models to generate potentially harmful content, have become a major security threat. However, existing methods primarily rely on black-box manipulation of prompt templates, resulting in high costs and poor generalizability. To break through the bottleneck, this study reveals the potential impact of the generation of LLMs on safety for the first time that Defense Threshold Decay (DTD) phenomena: as benign content generation increases, the model’s attention to input instructions progressively diminishes. Building on this insight, we propose the Sugar-Coated Poison (SCP) attack paradigm, using a “semantic reversal” strategy, where benign inputs that are opposite in meaning to malicious intent are crafted to induce the model into a safety response mode. When the defense threshold decays, an adversarial reasoning mechanism easily bypasses safety mechanisms. Experiments show SCP outperforms existing baselines. For defense, we propose Part-of-Speech Defense (POSD), leveraging verb-noun dependencies for syntactic analysis to enhance robustness and security of LLMs. Our code is available at https://anonymous.4open.science/r/SCP-9092.

Large Vision-Language Models (LVLMs) have achieved significant progress in tasks like visual question answering and document understanding. However, their potential to comprehend embodied environments and navigate within them remains underexplored. In this work, we first study the challenge of open-vocabulary object navigation by introducing DivScene, a large-scale dataset with 4,614 houses across 81 scene types and 5,707 kinds of target objects. Our dataset provides a much greater diversity of target objects and scene types than existing datasets, enabling a comprehensive task evaluation. We evaluated various methods with LVLMs and LLMs on our dataset and found that current models still fall short of open-vocab object navigation ability. Then, we fine-tuned LVLMs to predict the next action with CoT explanations. We observe that LVLM’s navigation ability can be improved substantially with only BFS-generated shortest paths without any human supervision, surpassing GPT-4o by over 20% in success rates.

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Data-scarce Behavior Editing of Language Models
Joykirat Singh | Subhabrata Dutta | Tanmoy Chakraborty

Large Language Models trained on web-scale text acquire language generation abilities that can solve a wide range of tasks, particularly when task knowledge is refined into the generative prior using in-context examples. However, spurious features learned from noisy data hinder their generalizability. Supervised fine-tuning can enhance task specificity but may lead to data inefficiency. Prior studies indicate that (i) noisy neural circuitries coexist with generalizable ones within LLMs, and (ii) finetuning typically enhances (or suppresses) existing abilities without introducing newer ones. Building upon these, we propose TaRot, a novel method for task adaptation. TaRot intervenes in the neural circuitries using learnable rotation matrices that are optimized using Bayesian optimization, on labelled samples in the order of standard few-shot prompting examples. Experiments on multiple classification and generation tasks using LLMs of varying sizes reveal the efficacy of TaRot, improving upon both zero- as well as few-shot performance, with average improvements (across models and tasks) of 15.6% and 14%, respectively.

The Key-Value (KV) cache reading latency increases significantly with context lengths, hindering the efficiency of long-context LLM inference. To address this, previous works propose retaining a small fraction of KV cache based on token importance. For example, KV eviction uses static heuristics to retain tokens, while KV retrieval dynamically selects query-relevant tokens for more adaptive cache management. However, we observe that important tokens are often sparsely distributed across the long context. This sparsity makes existing page-level KV retrieval inaccurate, as each page may include irrelevant tokens and miss critical ones. In this work, we propose Fier, a **Fi**ne-Grained and **E**fficient KV cache **R**etrieval method. Fier uses 1-bit quantized keys to estimate the importance of each token, resulting in efficient and precise retrieval. Experiments show that Fier matches full KV performance using only 11% of the cache budget across various long-context tasks, reducing decoding latency by 1.2× to 1.5×.

Speaker verification (SV) models are increasingly integrated into security, personalization, and access control systems, yet their robustness to many real-world challenges remains inadequately benchmarked. Real-world systems can face diverse conditions, some naturally occurring, and others that may be purposely, or even maliciously created, which introduce mismatches between enrollment and test data, affecting their performance. Ideally, the effect of all of these on model performance must be benchmarked; however existing benchmarks fall short, generally evaluating only a subset of potential conditions, and missing others entirely. We introduce SVeritas, the Speaker Verification tasks benchmark suite, which evaluates the performance of speaker verification systems under an extensive variety of stressors, including “natural” variations such as duration, spontaneity and content of the recordings, background conditions such as noise, microphone distance, reverberation, and channel mismatches, recording condition influences such as audio bandwidth and the effect of various codecs, physical influences, such as the age and health conditions of the speaker, as well as the suspectibility of the models to spoofing and adversarial attacks. While several benchmarks do exist that each cover some of these issues, SVeritas is the first comprehensive evaluation that not only includes all of these, but also several other entirely new, but nonetheless important real-life conditions that have not previously been benchmarked. We use SVeritas to evaluate several state-of-the-art SV models and observe that while some architectures maintain stability under common distortions, they suffer substantial performance degradation in scenarios involving cross-language trials, age mismatches, and codec-induced compression. Extending our analysis across demographic subgroups, we further identify disparities in robustness across age groups, gender, and linguistic backgrounds. By standardizing evaluation under realistic and synthetic stress conditions, SVeritas enables precise diagnosis of model weaknesses and establishes a foundation for advancing equitable and reliable speaker verification systems.

Speaker verification is a typical zero-shot learning task, where inference of unseen classes is performed by comparing embeddings of test instances to known examples. The models performing inference must hence naturally generate embeddings that cluster same-class instances compactly, while maintaining separation across classes. In order to learn to do so, they are typically trained on a large number of classes (speakers), often using specialized losses. However real-world speaker datasets often lack the class diversity needed to effectively learn this in a generalizable manner. We introduce CAARMA, a class augmentation framework that addresses this problem by generating synthetic classes through data mixing in the embedding space, expanding the number of training classes. To ensure the authenticity of the synthetic classes we adopt a novel adversarial refinement mechanism that minimizes categorical distinctions between synthetic and real classes. We evaluate CAARMA on multiple speaker verification tasks, as well as other representative zero-shot comparison-based speech analysis tasks and obtain consistent improvements: our framework demonstrates a significant improvement of 8% over all baseline models. Code for CAARMA will be released.

Virtual Teaching Assistants (VTAs) can reduce the workload of teaching teams in Asynchronous Learning Environments (ALEs) where timely, personalized support is often limited. As VTA systems grow more capable, rigorous and pedagogically sound evaluation becomes essential. Existing assessments often rely on surface-level metrics and lack sufficient grounding in educational theory, making it difficult to meaningfully compare the pedagogical effectiveness of VTA systems. To bridge this gap, we propose a pedagogically-oriented evaluation framework that is rooted in learning sciences and tailored to asynchronous forum discussions, a common VTA deployment context in ALE. We construct classifiers using expert annotations of VTA responses on a diverse set of forum posts. We evaluate the effectiveness of our classifiers, identifying approaches that improve accuracy as well as challenges that hinder generalization. Our work establishes a foundation for theory-driven evaluation of VTA systems, paving the way for more pedagogically effective AI in education.

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Demystifying Multilingual Reasoning in Process Reward Modeling
Weixuan Wang | Minghao Wu | Barry Haddow | Alexandra Birch

Large language models (LLMs) are designed to perform a wide range of tasks. To improve their ability to solve complex problems requiring multi-step reasoning, recent research leverages process reward modeling to provide fine-grained feedback at each step of the reasoning process for reinforcement learning (RL), but it predominantly focuses on English. In this paper, we tackle the critical challenge of extending process reward models (PRMs) to multilingual settings. To achieve this, we train multilingual PRMs on a dataset spanning seven languages, which is translated from English. Through comprehensive evaluations on two widely used reasoning benchmarks across 11 languages, we demonstrate that multilingual PRMs not only improve average accuracy but also reduce early-stage reasoning errors. Furthermore, our results highlight the sensitivity of multilingual PRMs to both the number of training languages and the volume of English data, while also uncovering the benefits arising from more candidate responses and trainable parameters. This work opens promising avenues for robust multilingual applications in complex, multi-step reasoning tasks.

Large Language Models (LLMs) as agents require careful behavioral adaptation. While adept at reactive tasks (e.g., medical reasoning), LLMs often struggle with proactive engagement, like unprompted identification of critical missing information or risks. We introduce **BehaviorBench**, a comprehensive dataset to evaluate agent behaviors across a clinical assistance spectrum. To rigorously test the current models, we also introduce **BehaviorBench-Hard**, a challenging subset where the performance of state-of-the-art models drops significantly, revealing weaknesses. To address these challenges, we propose **BehaviorSFT**, a novel training strategy using behavioral tokens to explicitly condition LLMs for dynamic behavioral selection which boosts performance on both benchmarks. Crucially, a blind clinician evaluation confirmed that our trained agents exhibit more realistic clinical behavior, striking a superior balance between helpful proactivity and necessary restraint versus standard fine-tuning or explicitly instructed agents. Project Page: https://behavior-adaptation.github.io/

Figure captions are crucial for helping readers understand and remember a figure’s key message. Many models have been developed to generate these captions, helping authors compose better quality captions more easily. Yet, authors almost always need to revise generic AI-generated captions to match their writing style and the domain’s style, highlighting the need for personalization. Despite language models’ personalization (LaMP) advances, these technologies often focus on text-only settings and rarely address scenarios where both inputs and profiles are multimodal. This paper introduces LaMP-Cap, a dataset for personalized figure caption generation with multimodal figure profiles. For each target figure, LaMP-Cap provides not only the needed inputs, such as figure images, but also up to three other figures from the same document—each with its image, caption, and figure-mentioning paragraphs—as a profile to characterize the context. Experiments with four LLMs show that using profile information consistently helps generate captions closer to the original author-written ones. Ablation studies reveal that images in the profile are more helpful than figure-mentioning paragraphs, highlighting the advantage of using multimodal profiles over text-only ones.

Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for knowledge injection during large language model (LLM) inference in recent years. However, due to their limited ability to exploit fine-grained inter-document relationships, current RAG implementations face challenges in effectively addressing the retrieved noise and redundancy content, which may cause error in the generation results. To address these limitations, we propose an **E**fficient **D**ynamic **C**lustering-based document **C**ompression framework (**EDC²-RAG**) that utilizes latent inter-document relationships while simultaneously removing irrelevant information and redundant content. We validate our approach, built upon GPT-3.5-Turbo and GPT-4o-mini, on widely used knowledge-QA and Hallucination-Detection datasets. Experimental results show that our method achieves consistent performance improvements across various scenarios and experimental settings, demonstrating strong robustness and applicability. Our code and datasets are available at https://github.com/Tsinghua-dhy/EDC-2-RAG.

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HebID: Detecting Social Identities in Hebrew-language Political Text
Guy Mor-Lan | Naama Rivlin-Angert | Yael R. Kaplan | Tamir Sheafer | Shaul R. Shenhav

Political language is deeply intertwined with social identities. While social identities are often shaped by specific cultural contexts, existing NLP datasets are predominantly English-centric and focus on coarse-grained identity categories. We introduce HebID, the first multilabel Hebrew corpus for social identity detection. The corpus contains 5,536 sentences from Israeli politicians’ Facebook posts (Dec 2018-Apr 2021), with each sentence manually annotated for twelve nuanced social identities (e.g., Rightist, Ultra-Orthodox, Socially-oriented) selected based on their salience in national survey data. We benchmark multilabel and single-label encoders alongside 2B-9B-parameter decoder LLMs, finding that Hebrew-tuned LLMs provide the best results (macro-F₁ = 0.74). We apply our classifier to politicians’ Facebook posts and parliamentary speeches, evaluating differences in popularity, temporal trends, clustering patterns, and gender-related variations in identity expression. We utilize identity choices from a national public survey, comparing the identities portrayed in elite discourse with those prioritized by the public. HebID provides a comprehensive foundation for studying social identities in Hebrew and can serve as a model for similar research in other non-English political contexts

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Dub-S2ST: Textless Speech-to-Speech Translation for Seamless Dubbing
Jeongsoo Choi | Jaehun Kim | Joon Son Chung

This paper introduces a cross-lingual dubbing system that translates speech from one language to another while preserving key characteristics such as duration, speaker identity, and speaking speed. Despite the strong translation quality of existing speech translation approaches, they often overlook the transfer of speech patterns, leading to mismatches with source speech and limiting their suitability for dubbing applications. To address this, we propose a discrete diffusion-based speech-to-unit translation model with explicit duration control, enabling time-aligned translation. We then synthesize speech based on the translated units and source speaker’s identity using a conditional flow matching model. Additionally, we introduce a unit-based speed adaptation mechanism that guides the translation model to produce speech at a rate consistent with the source, without relying on any text. Extensive experiments demonstrate that our framework generates natural and fluent translations that align with the original speech’s duration and speaking pace, while achieving competitive translation performance.

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FinGrAct: A Framework for FINe-GRrained Evaluation of ACTionability in Explainable Automatic Fact-Checking
Islam Eldifrawi | Shengrui Wang | Amine Trabelsi

The field of explainable Automatic Fact-Checking (AFC) aims to enhance the transparency and trustworthiness of automated fact verification systems by providing clear and comprehensible explanations. However, the effectiveness of these explanations depends ontheir actionability—the extent to which an AFC explanation pinpoints the error, supplies the correct fact, and backs it with sources. Despiteactionability being critical for high-quality explanations, no prior research has proposed a method to evaluate it. This paper introducesFinGrAct, a fine-grained evaluation framework that can access the web and is designed to assess actionability in AFC explanations through well-defined criteria. We also introduce a novel dataset to evaluate actionability in AFC explanations. FinGrAct surpasses state-of-the-art (SOTA) evaluators, achieving the highest Pearson and Kendall correlation with human judgments while demonstrating the lowest egocentricbias, making it a more robust evaluation approach for actionability evaluation in AFC.

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What Has Been Lost with Synthetic Evaluation?
Alexander Gill | Abhilasha Ravichander | Ana Marasovic

Large language models (LLMs) are increasingly used for data generation. However, creating evaluation benchmarks raises the bar for this emerging paradigm. Benchmarks must target specific phenomena, penalize exploiting shortcuts, and be challenging. Through two case studies, we ask whether LLMs are ready to meet these demands—by generating reasoning-over-text benchmarks and comparing them to those that were created through careful crowdsourcing. Specifically, we evaluate both the *validity* and *difficulty* of LLM-generated versions of two high-quality reading comprehension datasets: CondaQA, which evaluates reasoning about negation, and DROP, which targets reasoning about quantities. We find that prompting LLMs can produce variants of these datasets that are often valid according to the annotation guidelines, at a fraction of the cost of the original crowdsourcing effort. However, we show that they are *less challenging for LLMs* than their human-authored counterparts. This finding sheds light on what may have been lost by generating evaluation data with LLMs, and calls for critically reassessing the immediate use of this increasingly prevalent approach to benchmark creation.

Large language models are prone to generating hallucination that deviates from factual information. Existing studies mainly focus on detecting the presence of hallucinations but lack a systematic classification approach, which hinders deeper exploration of their characteristics. To address this, we introduce the concept of belief state, which quantifies the model’s confidence in its own responses. We define the belief state of the model based on self-consistency, leveraging answer repetition rates to label confident and uncertain states. Based on this, we categorize factuality hallucination into two types: Overconfident Hallucination and Unaware Hallucination. Furthermore, we propose BAFH, a factuality hallucination type detection method. By training a classifier on model’s hidden states, we establish a link between hidden states and belief states, enabling efficient and automatic hallucination type detection. Experimental results demonstrate the effectiveness of BAFH and the differences between hallucination types.

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Proxy Barrier: A Hidden Repeater Layer Defense Against System Prompt Leakage and Jailbreaking
Pedro Schindler Freire Brasil Ribeiro | Iago Alves Brito | Rafael Teixeira Sousa | Fernanda Bufon Färber | Julia Soares Dollis | Arlindo Rodrigues Galvão Filho

Prompt injection and jailbreak attacks remain a critical vulnerability for deployed large language models (LLMs), allowing adversaries to bypass safety protocols and extract sensitive information. To address this, we present Proxy Barrier (ProB), a lightweight defense that interposes a proxy LLM between the user and the target model. The proxy LLM is tasked solely to repeat the user input, and any failure indicates the presence of an attempt to reveal or override system instructions, leading the malicious request to be detected and blocked before it reaches the target model. ProB therefore requires no access to model weights or prompts, and is deployable entirely at the API level. Experiments across multiple model families demonstrate that ProB achieves state-of-the-art resilience against prompt leakage and jailbreak attacks. Notably, our approach outperforms baselines and achieves up to 98.8% defense effectiveness, and shows robust protection across both open and closed-source LLMs when suitably paired with proxy models, while also keeping response quality intact.

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AraSafe: Benchmarking Safety in Arabic LLMs
Hamdy Mubarak | Abubakr Mohamed | Majd Hawasly

We introduce AraSafe, the first large-scale native Arabic safety benchmark for large language models (LLMs), addressing the pressing need for culturally and linguistically representative evaluation resources. The dataset comprises 12K naturally occurring, human-written Arabic prompts containing both harmful and non-harmful content across diverse domains, including linguistics, social studies, and science. Each prompt was independently annotated by two experts into one of nine fine-grained safety categories, including ‘Safe/Not Harmful’, ‘Illegal Activities’, ‘Violence or Harm’, ‘Privacy Violation’, and ‘Hate Speech’. Additionally, to support training classifiers for harmful content and due to the imbalanced representation of harmful content in the natural dataset, we create a synthetic dataset of additional 12K harmful prompts generated by GPT-4o via carefully designed prompt engineering techniques. We benchmark a number of Arabic-centric and multilingual models in the 7 to 13B parameter range, including Jais, AceGPT, Allam, Fanar, Llama-3, Gemma-2, and Qwen3, as well as BERT-based fine-tuned classifier models on detecting harmful prompts. GPT-4o was used as an upper-bound reference baseline. Our evaluation reveals critical safety blind spots in Arabic LLMs and underscores the necessity of localized, culturally grounded benchmarks for building responsible AI systems.

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Nested Named Entity Recognition as Single-Pass Sequence Labeling
Alberto Muñoz-Ortiz | David Vilares | Caio Corro | Carlos Gómez-Rodríguez

We cast nested named entity recognition (NNER) as a sequence labeling task by leveraging prior work that linearizes constituency structures, effectively reducing the complexity of this structured prediction problem to straightforward token classification. By combining these constituency linearizations with pretrained encoders, our method captures nested entities while performing exactly n tagging actions. Our approach achieves competitive performance compared to less efficient systems, and it can be trained using any off-the-shelf sequence labeling library.

Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowledge. Recent studies have identified specific attention heads within the Transformer architecture, known as retrieval heads, responsible for extracting relevant contextual information. We hypothesise that masking these retrieval heads can induce hallucinations and that contrasting the outputs of the base LLM and the masked LLM can reduce hallucinations. To this end, we propose Decoding by Contrasting Retrieval Heads (DeCoRe), a novel training-free decoding strategy that amplifies information found in the context and model parameters. DeCoRe mitigates potentially hallucinated responses by dynamically contrasting the outputs of the base LLM and the masked LLM, using conditional entropy as a guide. Our extensive experiments confirm that DeCoRe improves performance on tasks requiring high contextual faithfulness, such as summarisation (XSum by 18.6%), instruction following (MemoTrap by 10.9%), and open-book question answering (NQ-Open by 2.4% and NQ-Swap by 5.5%).

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Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors
Zhengxiang Wang | Nafis Irtiza Tripto | Solha Park | Zhenzhen Li | Jiawei Zhou

As large language models (LLMs) become increasingly integrated into personal writing tools, a critical question arises: can LLMs faithfully imitate an individual’s writing style from just a few examples? Personal style is often subtle and implicit, making it difficult to specify through prompts yet essential for user-aligned generation. This work presents a comprehensive evaluation of state-of-the-art LLMs’ ability to mimic personal writing styles via in-context learning from a small number of user-authored samples. We introduce an ensemble of complementary metrics—including authorship attribution, authorship verification, style matching, and AI detection—to robustly assess style imitation. Our evaluation spans over 40,000 generations per model across domains such as news, email, forums, and blogs, covering writing samples from more than 400 real-world authors. Results show that while LLMs can approximate user styles in structured formats like news and email, they struggle with nuanced, informal writing in blogs and forums. Further analysis on various prompting strategies such as number of demonstrations reveal key limitations in effective personalization. Our findings highlight a fundamental gap in personalized LLM adaptation and the need for improved techniques to support implicit, style-consistent generation. To aid future research and for reproducibility, we open-source our data and code.

Distractor generation is the task of automatically generating plausible yet incorrect options (i.e., distractors) for fill-in-the-blank and multiple-choice questions. In assessment, distractors must be contextually relevant to the given question and answer. Even though recent research works focus on fine-tuning pre-trained encoder-decoder models with data augmentation techniques to generate distractors, these models often fail to capture the full semantic representation of a given question-answer and related distractors. The augmentation methods often rely on expanding the quantity of proposed candidates (i.e., questions or distractors), which can introduce noise into the models without necessarily enhancing their understanding of the deeper semantic relationships between question-answer and related distractors. This paper introduces a novel distractor generation model based on contrastive learning to train the model to recognize essential semantic features necessary to generate in-context distractors. The extensive experiments on two public datasets indicate that contrastive learning introduces a strong baseline model to the distractor generation task. It significantly outperforms recent models, increasing the NDCG@3 score from 24.68 to 32.33 on the MCQ dataset and from 26.66 to 36.68 on the SciQ dataset.

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Conflicts in Texts: Data, Implications and Challenges
Siyi Liu | Dan Roth

As NLP models become increasingly integrated into real-world applications, it becomes clear that there is a need to address the fact that models often rely on and generate conflicting information. Conflicts could reflect the complexity of situations, changes that need to be explained and dealt with, difficulties in data annotation, and mistakes in generated outputs. In all cases, disregarding the conflicts in data could result in undesired behaviors of models and undermine NLP models’ reliability and trustworthiness. This survey categorizes these conflicts into three key areas: (1) natural texts on the web, where factual inconsistencies, subjective biases, and multiple perspectives introduce contradictions; (2) human-annotated data, where annotator disagreements, mistakes, and societal biases impact model training; and (3) model interactions, where hallucinations and knowledge conflicts emerge during deployment. While prior work has addressed some of these conflicts in isolation, we unify them under the broader concept of conflicting information, analyze their implications, and discuss mitigation strategies. We highlight key challenges and future directions for developing conflict-aware NLP systems that can reason over and reconcile conflicting information more effectively.

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Recognizing Limits: Investigating Infeasibility in Large Language Models
Wenbo Zhang | Zihang Xu | Hengrui Cai

Large language models (LLMs) have shown remarkable performance in various tasks but often fail to handle queries that exceed their knowledge and capabilities, leading to incorrect or fabricated responses. This paper addresses the need for LLMs to recognize and refuse infeasible tasks due to the requests surpassing their capabilities. We conceptualize four main categories of infeasible tasks for LLMs, which cover a broad spectrum of hallucination-related challenges identified in prior literature. We develop and benchmark a new dataset comprising diverse infeasible and feasible tasks to evaluate multiple LLMs’ abilities to decline infeasible tasks. Furthermore, we explore the potential of increasing LLMs’ refusal capabilities with fine-tuning. Experiments validate the effectiveness of our trained models, offering promising directions for refining the operational boundaries of LLMs in real applications.

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VQA-Augmented Machine Translation with Cross-Modal Contrastive Learning
Zhihui Zhang | Shiliang Sun | Jing Zhao | Tengfei Song | Hao Yang

Multimodal machine translation (MMT) aims to enhance translation quality by integrating visual information. However, existing methods often extract visual features using pre-trained models while learning text features from scratch, leading to representation imbalance. These methods are also prone to being misled by redundant visual information, which results in suboptimal performance. To address these challenges, we propose CAMT, a novel cross-modal VQA-augmented MMT method. CAMT aligns image-source text pairs and image-question text pairs through dual-text contrastive learning, thereby improving semantic consistency across modalities. Additionally, we design an effective strategy for generating question–answer pairs to enhance fine-grained alignment and filter out irrelevant visual noise, while also addressing the scarcity of VQA annotations. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness of the proposed CAMT framework, which consistently outperforms state-of-the-art MMT methods across multiple evaluation metrics.

Image Difference Captioning (IDC) methods have advanced in highlighting subtle differences between similar images, but their performance is often constrained by limited training data. Using Large Multimodal Models (LMMs) to describe changes in image pairs mitigates data limits but adds noise. These change descriptions are often coarse summaries, obscuring fine details and hindering noise detection. In this work, we improve IDC with a noise-robust approach at both data and model levels. We use LMMs with structured prompts to generate fine-grained change descriptions during data curation. We propose a Noise-Aware Modeling and Captioning (NAMC) model with three modules: Noise Identification and Masking (NIM) to reduce noisy correspondences, Masked Image Reconstruction (MIR) to correct over-masking errors, and Fine-grained Description Generation (FDG) to produce coherent change descriptions. Experiments on four IDC benchmarks show that NAMC, pre-trained on our large-scale data, outperforms streamlined architectures and achieves competitive performance with LLM-finetuned methods, offering better inference efficiency.

Recently, Large Language Models (LLMs) have shown strong potential in recommendation tasks due to their broad world knowledge and reasoning capabilities. However, applying them to serendipity-oriented recommendation remains challenging, mainly due to a domain gap of LLMs in modeling personalized user behavior and the scarcity of labeled serendipitous interactions. In this paper, we introduce **SOLAR** (**S**erendipity-**O**ptimized **L**anguage model **A**ligned for **R**ecommendation), a two-stage framework that addresses these challenges. To alleviate label scarcity, we adopt a weak supervision strategy: a sequential ID-based recommender generates candidate items, which are then reranked by an LLM acting as a preference judge to produce serendipity-aware pseudo-labels. To bridge the domain gap, we propose a domain-adaptive instruction tuning method (SUN) that aligns LLMs with recommendation tasks. Experiments on three real-world datasets show that **SOLAR** consistently improves both accuracy and serendipity over strong baselines, showing its effectiveness in enabling more diverse, user-centric recommendations. Code and dataset are released at [https://github.com/SOLAR2025ARR/SOLAR](https://github.com/SOLAR2025ARR/SOLAR).

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AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science
Qiuhai Zeng | Claire Jin | Xinyue Wang | Yuhan Zheng | Qunhua Li

Large language models (LLMs) are increasingly used to automate data analysis through executable code generation. Yet, data science tasks often admit multiple statistically valid solutions—for example, different modeling strategies—making it critical to understand the reasoning behind analyses, not just their outcomes. While manual review of LLM-generated code can help ensure statistical soundness, it is labor-intensive and requires expertise. A more scalable approach is to evaluate the underlying workflows—the logical plans guiding code generation. However, it remains unclear how to assess whether an LLM-generated workflow supports reproducible implementations.To address this, we present **AIRepr**, an **A**nalyst–**I**nspector framework for automatically evaluating and improving the **repr**oducibility of LLM-generated data analysis workflows. Our framework is grounded in statistical principles and supports scalable, automated assessment. We introduce two novel reproducibility-enhancing prompting strategies and benchmark them against standard prompting across 15 analyst–inspector LLM pairs and 1,032 tasks from three public benchmarks. Our findings show that workflows with higher reproducibility also yield more accurate analyses, and that reproducibility-enhancing prompts substantially improve both metrics. This work provides a foundation for transparent, reliable, and efficient human–AI collaboration in data science. Our code is publicly available: [https://github.com/Anonymous-2025-Repr/LLM-DS-Reproducibility](https://github.com/Anonymous-2025-Repr/LLM-DS-Reproducibility)

Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks but remain vulnerable to misinformation, particularly in multi-turn dialogues where misleading context accumulates. Existing benchmarks, such as TruthfulQA and FEVER, assess factual accuracy in isolated queries but fail to evaluate LLMs’ resilience to misinformation in interactive settings. To address this limitation, we introduce MisinfoBench, a multi-dimensional benchmark designed to assess LLMs’ ability to discern, resist, and reject misinformation. MisinfoBench defines three core dimensions—Discernment, Resistance, and Principled Refusal—across seven evaluation tasks, systematically testing misinformation identification, contextual resistance, and the rejection of coercive false premises. It includes a dataset of 4,962 multi-turn dialogues and 2,000 misinformation-based question-answer pairs, capturing diverse misinformation scenarios. We evaluate 16 LLMs, revealing substantial disparities in misinformation resilience: proprietary models outperform open-source counterparts, while multi-turn dialogues and cross-lingual settings exacerbate misinformation susceptibility. Our findings highlight persistent vulnerabilities in LLMs’ misinformation defenses, emphasizing the need for context-aware training, adversarial robustness, and principled reasoning. MisinfoBench establishes a rigorous standard for evaluating misinformation resilience, advancing the development of more trustworthy AI systems.

With the rapid advancement of large language models (LLMs), natural language processing (NLP) has achieved remarkable progress. Nonetheless, significant challenges remain in handling texts with ambiguity, polysemy, or uncertainty. We introduce the Fuzzy Reasoning Chain (FRC) framework, which integrates LLM semantic priors with continuous fuzzy membership degrees, creating an explicit interaction between probability-based reasoning and fuzzy membership reasoning. This transition allows ambiguous inputs to be gradually transformed into clear and interpretable decisions while capturing conflicting or uncertain signals that traditional probability-based methods cannot. We validate FRC on sentiment analysis tasks, where both theoretical analysis and empirical results show that it ensures stable reasoning and facilitates knowledge transfer across different model scales. These findings indicate that FRC provides a general mechanism for managing subtle and ambiguous expressions with improved interpretability and robustness.

With the rapid development of large language models (LLMs) in math reasoning, the accuracy of models on existing math benchmarks has gradually approached 90% or even higher. More challenging math benchmarks are hence urgently in need to satisfy the increasing evaluation demands. To bridge this gap, we propose HighMATH. Problems in HighMATH are collected according to 3 criteria: problem complexity, knowledge domain diversity and fine-grained annotations. We collect 5,293 problems from Chinese senior high school mathematics exams published in 2024, covering 8 subjects and 7 levels of difficulty, with each problem involving an average of more than 2.4 knowledge points. We conduct a thorough evaluation of latest LLMs on the curated HighMATH, including o1-like models. Evaluation results demonstrate that the accuracy of advanced LLMs on HighMATH is significantly lower than that on previous math reasoning benchmarks. This gap even exceeds 30%. Our results also suggest that properly trained smaller LLMs may have great potential in math reasoning. Our data is available at https://github.com/tjunlp-lab/HighMATH.

Recently, advancements in AI counseling based on large language models have shown significant progress. However, existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and failing to capture the decision-making rationale behind each response. In this work, we propose CATCH, a novel data synthesis framework designed to address these challenges. Specifically, to improve therapy fidelity, we introduce the Progressive Dialogue Synthesis strategy, which extracts goals, resources, and solutions from a client’s self-report, organizes them into structured outlines, and then incrementally generates stage-aligned counseling dialogues. To capture decision-making rationale behind each response, we propose the Memory-Driven Dynamic Planning thinking pattern that integrates memory enhancement, global planning, and strategy reasoning; a collaborative multi-agent optimizer then leverages MDP to attach explicit chain-of-thought to each dialogue turn. Extensive experiments and human evaluations demonstrate that CATCH significantly enhances fidelity and logical coherence in AI counseling.

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MediVLM: A Vision Language Model for Radiology Report Generation from Medical Images
Debanjan Goswami | Ronast Subedi | Shayok Chakraborty

Generating radiology reports from medical images has garnered sufficient attention in the research community. While existing methods have demonstrated promise, they often tend to generate reports that are factually incomplete and inconsistent, fail to focus on informative regions within an image, and impose strong annotation assumptions, such as bounding box annotations, image level annotations (which can be challenging to obtain) for model training. In this paper, we propose MediVLM, a vision language model (VLM) for radiology report generation from medical images. The proposed model consists of a pre-trained object detector to extract the salient anatomical regions from the images, an image encoder, a text encoder, a module to align the visual and text representations, a cross attention layer to fuse the two representations and finally, a transformer based decoder to generate the final report. MediVLM can generate radiology reports even when no reports are available for training; this is an extremely useful feature, as curating such reports is a labor-intensive task. Further, it computes a severity score (depicting the seriousness of a patient’s medical condition) from the generated radiology reports, which can be used to prioritize patients who need immediate medical attention. Our extensive empirical analyses on three benchmark datasets corroborate the promise and potential of our method against competing baselines. Our code is open-sourcedin our project webpage at: https://sites.google.com/view/medivlm/home

With the commercialization of short video platforms (SVPs), the demand for compliance auditing of advertising content has grown rapidly. The rise of large vision-language models (VLMs) offers new opportunities for automating ad content moderation. However, short video advertising scenarios present unique challenges due to data drift (DD) and label drift (LD). DD refers to rapid shifts in data distribution caused by advertisers to evade platform review mechanisms. LD arises from the evolving and increasingly standardized review guidelines of SVPs, which effectively alter the classification boundaries over time. Despite the significance of these phenomena, there is currently a lack of benchmark tools designed to evaluate model performance under such conditions. To address this gap, we propose AdDriftBench (ADB). The ADB dataset consists of 3,480 short video ads, including 2,280 examples labeled under data drift scenarios, designed to evaluate the generalization capabilities of VLMs under rapidly shifting content distributions. An additional 1,200 examples represent label drift scenarios, aimed at assessing VLMs’ abilities in instruction following and fine-grained semantic understanding under varying auditing standards. Through extensive experiments on 16 open-source VLMs, we find that current models perform moderately in short video advertising contexts, particularly in handling fine-grained semantics and adapting to shifting instructions. Our dataset will be made publicly available.

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NIM: Neuro-symbolic Ideographic Metalanguage for Inclusive Communication
Prawaal Sharma | Poonam Goyal | Navneet Goyal | Vidisha Sharma

Digital communication has become the cornerstone of modern interaction, enabling rapid, accessible, and interactive exchanges. However, individuals with lower academic literacy often face significant barriers, exacerbating the “digital divide.” In this work, we introduce a novel, universal ideographic metalanguage designed as an innovative communication framework that transcends academic, linguistic, and cultural boundaries. Our approach leverages principles of Neuro-symbolic AI, combining neural-based large language models (LLMs) enriched with world knowledge and symbolic knowledge heuristics grounded in the linguistic theory of Natural Semantic Metalanguage (NSM). This enables the semantic decomposition of complex ideas into simpler, atomic concepts. Adopting a human-centric, collaborative methodology, we engaged over 200 semi-literate participants in defining the problem, selecting ideographs, and validating the system. With over 80% semantic comprehensibility, an accessible learning curve, and universal adaptability, our system effectively serves underprivileged populations with limited formal education.

Visual instruction tuning has become the predominant technology in eliciting the multimodal task-solving capabilities of large vision-language models (LVLMs). Despite the success, as visual instructions require images as the input, it would leave the gap in inheriting the task-solving capabilities from the backbone LLMs, and make it costly to collect a large-scale high-quality dataset. To address it, we propose ViFT, a visual instruction-free fine-tuning framework for LVLMs. In ViFT, we only require the text-only instructions and image caption data during training, to separately learn the task-solving and visual perception abilities. During inference, we extract and combine the representations of the text and image inputs, for fusing the two abilities to fulfill multimodal tasks. Experimental results demonstrate that ViFT can achieve state-of-the-art performance on several downstream benchmarks, with rather less training data. Our code and data will be publicly released.

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Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models
Jian Jornbowrl Wang | Xiaofei Xie | Qiang Hu | Shangqing Liu | Yi Li

Code Large Language Models (Code LLMs) have opened a new era in programming with their impressive capabilities. However, recent research has revealed critical limitations in their ability to reason about runtime behavior and understand the actual functionality of programs, which poses significant challenges for their post-training and practical deployment. Specifically, Code LLMs encounter two principal issues: (1) a lack of proficiency in reasoning about program execution behavior, as they struggle to interpret what programs actually do during runtime, and (2) inconsistent and fragmented representation of semantic information, such as execution traces, across existing methods, which hinders their ability to generalize and reason effectively. These challenges underscore the necessity for more systematic approaches to enhance the reasoning capabilities of Code LLMs. To address these issues, we introduce a generic framework to support integrating semantic information (e.g., execution trace) to code task-relevant prompts, and conduct a comprehensive study to explore the role of semantic information in enhancing the reasoning ability of Code LLMs accordingly. Specifically, we focus on investigating the usefulness of trace-based semantic information in boosting supervised fine-tuning(SFT) and post-phase inference of Code LLMs. The experimental results surprisingly disagree with previous works and demonstrate that semantic information has limited usefulness for SFT and test time scaling of Code LLM.

Generating long, informative, and factual outputs remains a major challenge for Large Language Models (LLMs). Existing benchmarks for long-form generation typically assess real-world queries with hard-to-verify metrics or use synthetic setups that ease evaluation but overlook real-world intricacies. In this paper, we introduce LongWeave, which balance real-world and verifiable assessment with Target-Anchored Evaluation (TAE). TAE constructs tasks by first defining verifiable targets within real-world scenarios, then systematically generating corresponding queries, textual materials, and anchors based on these targets. This ensures that tasks are both realistic and objectively assessable, enabling rigorous assessment of model capabilities in meeting complex real-world constraints. LongWeave supports customizable input/output lengths (up to 64K/8K tokens) across seven distinct tasks. Evaluation on 23 LLMs show that even state-of-the-art models encounter significant challenges in long-form generation as real-world complexity and output length increase. Dataset will be publicly available.

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XL-Suite: Cross-Lingual Synthetic Training and Evaluation Data for Open-Ended Generation
Vivek Iyer | Pinzhen Chen | Ricardo Rei | Alexandra Birch

Cross-lingual open-ended generation – responding in a language different from that of the query – is an important yet understudied problem. This work proposes XL-Instruct, a novel technique for generating high-quality synthetic data, and introduces XL-AlpacaEval, a new benchmark for evaluating cross-lingual generation capabilities of large language models (LLMs). Our experiments show that fine-tuning with just 8K instructions generated using XL-Instruct significantly improves model performance, increasing the win rate against GPT-4o-mini from 7.4% to 21.5% and improving on several fine-grained quality metrics. Moreover, base LLMs fine-tuned on XL-Instruct exhibit strong zero-shot improvements to same-language question answering, as shown on our machine-translated m-AlpacaEval. These consistent gains highlight the promising role of XL-Instruct in the post-training of multilingual LLMs. Finally, we publicly release XL-Suite, a collection of training and evaluation data to facilitate research in cross-lingual open-ended generation.

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Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling
Seyyed Saeid Cheshmi | Azal Ahmad Khan | Xinran Wang | Zirui Liu | Ali Anwar

Large Language Models (LLMs) are increasingly relied upon for solving complex reasoning tasks in domains such as mathematics, logic, and multi-step question answering. A growing line of work seeks to improve reasoning quality by scaling inference time compute particularly through Process Reward Models (PRMs), used to reward the reasoning at intermediate steps. While effective, these methods introduce substantial computational overhead, especially when generating large numbers of solutions in parallel. In this paper, we investigate whether PRMs can be used mid-generation to provide early signals that enable the rejection of suboptimal candidates before full generation of step is complete. We introduce the hypothesis that PRMs are also Partial Reward Models, meaning that the scores they assign to partially completed reasoning step are predictive of final output quality. This allows for principled early rejection based on intermediate token-level signals. We support this hypothesis both theoretically, by proving that the risk of discarding optimal beams decreases exponentially with generation length and empirically, by demonstrating a strong correlation between partial and final rewards across multiple reward models. On math reasoning benchmarks, our method achieves up to 1.4 × – 9 × reduction in inference FLOPs without degrading final performance. These results suggest that early rejection is a powerful mechanism for improving the compute-efficiency of reasoning in LLMs.

Cultural competence, defined as the ability to understand and adapt to multicultural contexts, is increasingly vital for large language models (LLMs) in global environments. While several cultural benchmarks exist to assess LLMs’ cultural competence, current evaluations suffer from fragmented taxonomies, domain specificity, and heavy reliance on manual data annotation. To address these limitations, we introduce CultureSynth, a novel framework comprising (1) a comprehensive hierarchical multilingual cultural taxonomy covering 12 primary and 130 secondary topics, and (2) a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs. The CultureSynth-7 synthetic benchmark contains 19,360 entries and 4,149 manually verified entries across 7 languages. Evaluation of 14 prevalent LLMs of different sizes reveals clear performance stratification led by ChatGPT-4o-Latest and Qwen2.5-72B-Instruct. The results demonstrate that a 3B-parameter threshold is necessary for achieving basic cultural competence, models display varying architectural biases in knowledge processing, and significant geographic disparities exist across models. We believe that CultureSynth offers a scalable framework for developing culturally aware AI systems while reducing reliance on manual annotation.

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DesignCLIP: Multimodal Learning with CLIP for Design Patent Understanding
Zhu Wang | Homaira Huda Shomee | Sathya N. Ravi | Sourav Medya

In the field of design patent analysis, traditional tasks such as patent classification and patent image retrieval heavily depend on the image data. However, patent images—typically consisting of sketches with abstract and structural elements of an invention—often fall short in conveying comprehensive visual context and semantic information. This inadequacy can lead to ambiguities in evaluation during prior art searches. Recent advancements in vision-language models, such as CLIP, offer promising opportunities for more reliable and accurate AI-driven patent analysis. In this work, we leverage CLIP models to develop a unified framework DesignCLIP for design patent applications with a large-scale dataset of U.S. design patents. To address the unique characteristics of patent data, DesignCLIP incorporates class-aware classification and contrastive learning, utilizing generated detailed captions for patent images and multi-views image learning. We validate the effectiveness of DesignCLIP across various downstream tasks, including patent classification and patent retrieval. Additionally, we explore multimodal patent retrieval, which provides the potential to enhance creativity and innovation in design by offering more diverse sources of inspiration. Our experiments show that DesignCLIP consistently outperforms baseline and SOTA models in the patent domain on all tasks. Our findings underscore the promise of multimodal approaches in advancing patent analysis. The codebase is available here: https://github.com/AI4Patents/DesignCLIP.

Retrieval-Augmented Generation (RAG) integrates external knowledge with Large Language Models (LLMs) to enhance factual correctness and mitigate hallucination. However, dense retrievers often become the bottleneck of RAG systems due to their limited parameters compared to LLMs and their inability to perform step-by-step reasoning. While prompt-based iterative RAG attempts to address these limitations, it is constrained by human-designed workflows.To address these limitations, we propose R3-RAG, which uses Reinforcement learning to make the LLM learn how to Reason and Retrieve step by step, thus retrieving comprehensive external knowledge and leading to correct answers. R3-RAG is divided into two stages. We first use cold start to make the model learn the manner of iteratively interleaving reasoning and retrieval. Then we use reinforcement learning to further harness its ability to better explore the external retrieval environment.Specifically, we propose two rewards for R3-RAG: 1) answer correctness for outcome reward, which judges whether the trajectory leads to a correct answer; 2) relevance-based document verification for process reward, encouraging the model to retrieve documents that are relevant to the user question, through which we can let the model learn how to iteratively reason and retrieve relevant documents to get the correct answer.Experimental results show that R3-RAG significantly outperforms baselines and can transfer well to different retrievers.

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‘Hello, World!’: Making GNNs Talk with LLMs
Sunwoo Kim | Soo Yong Lee | Jaemin Yoo | Kijung Shin

While graph neural networks (GNNs) have shown remarkable performance across diverse graph-related tasks, their high-dimensional hidden representations render them black boxes. In this work, we propose Graph Lingual Network (GLN), a GNN built on large language models (LLMs), with hidden representations in the form of human-readable text. Through careful prompt design, GLN incorporates not only the message passing module of GNNs but also advanced GNN techniques, including graph attention and initial residual connection. The comprehensibility of GLN’s hidden representations enables an intuitive analysis of how node representations change (1) across layers and (2) under advanced GNN techniques, shedding light on the inner workings of GNNs. Furthermore, we demonstrate that GLN achieves strong zero-shot performance on node classification and link prediction, outperforming existing LLM-based baseline methods.

The rapid advancement of multimodal large language models (MLLMs) has significantly enhanced performance across benchmarks. However, data contamination — partial/entire benchmark data is included in the model’s training set — poses critical challenges for fair evaluation. Existing detection methods for unimodal large language models (LLMs) are inadequate for MLLMs due to multimodal data complexity and multi-phase training. We systematically analyze multimodal data contamination using our analytical framework, MM-DETECT, which defines two contamination categories — unimodal and cross-modal — and effectively quantifies contamination severity across multiple-choice and caption-based Visual Question Answering tasks. Evaluations on twelve MLLMs and five benchmarks reveal significant contamination, particularly in proprietary models and older benchmarks. Crucially, contamination sometimes originates during unimodal pre-training rather than solely from multimodal fine-tuning. Our insights refine contamination understanding, guiding evaluation practices and improving multimodal model reliability.

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NLKI: A Lightweight Natural Language Knowledge Integration Framework for Improving Small VLMs in Commonsense VQA Tasks
Aritra Dutta | Swapnanil Mukherjee | Deepanway Ghosal | Somak Aditya

Commonsense visual–question answering often hinges on knowledge that is missing from the image or the question. Small vision-language models (sVLMs) such as ViLT, VisualBERT, and FLAVA therefore lag behind their larger generative counterparts. To study the effect of careful commonsense knowledge integration on sVLMs, we present an end-to-end framework (NLKI) that (i) retrieves natural language facts, (ii) prompts an LLM to craft natural language explanations, and (iii) feeds both signals to sVLMs across two commonsense VQA datasets (CRIC, AOKVQA) and a visual-entailment dataset (e-SNLI-VE). Facts retrieved using a fine-tuned ColBERTv2 and an object information-enriched prompt yield explanations that largely cut down hallucinations while lifting the end-to-end answer accuracy by up to 7% (across three datasets), making FLAVA and other models in NLKI match or exceed medium-sized VLMs such as Qwen-2 VL-2B and SmolVLM-2.5B. As these benchmarks contain 10–25% label noise, additional finetuning using noise-robust losses (such as symmetric cross-entropy and generalised cross-entropy) adds another 2.5% in CRIC and 5.5% in AOKVQA. Our findings expose when LLM-based commonsense knowledge beats retrieval from commonsense knowledge bases, how noise-aware training stabilises small models in the context of external knowledge augmentation, and why parameter-efficient commonsense reasoning is now within reach for 250M models.

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Text or Pixels? Evaluating Efficiency and Understanding of LLMs with Visual Text Inputs
Yanhong Li | Zixuan Lan | Jiawei Zhou

Large language models (LLMs) and their multimodal variants can now process visual inputs, including images of text. This raises an intriguing question: Can we compress textual inputs by feeding them as images to reduce token usage while preserving performance?In this paper, we show that *visual text representations* are a practical and surprisingly effective form of input compression for decoder LLMs. We exploit this idea by rendering long text inputs as a single image and providing it directly to the model. This approach dramatically reduces the number of decoder tokens required, offering a new form of input compression. Through experiments on two distinct benchmarks — RULER (long-context retrieval) and CNN/DailyMail (document summarization) — we demonstrate that this text-as-image method yields substantial token savings *without degrading task performance*.

Recent trends in LLMs development clearly show growing interest in the use and application of sovereign LLMs. The global debate over sovereign LLMs highlights the need for governments to develop their LLMs, tailored to their unique socio-cultural and historical contexts. However, there remains a shortage of frameworks and datasets to verify two critical questions: (1) how well these models align with users’ socio-cultural backgrounds, and (2) whether they maintain safety and technical robustness without exposing users to potential harms and risks. To address this gap, we construct a new dataset and introduce an analytic framework for extracting and evaluating the socio-cultural elements of sovereign LLMs, alongside assessments of their technical robustness. Our experimental results demonstrate that while sovereign LLMs play a meaningful role in supporting low-resource languages, they do not always meet the popular claim that these models serve their target users well. We also show that pursuing this untested claim may lead to underestimating critical quality attributes such as safety. Our study suggests that advancing sovereign LLMs requires a more extensive evaluation that incorporates a broader range of well-grounded and practical criteria.

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Sample Efficient Alignment Learning With Episodic Control
Van Dai Do | Quan Hung Tran | Ahmed Kirmani | Lu Zhang | Hung Le

Aligning large language models (LLMs) with specific task objectives is challenging, especially when access to feedback signals for guiding the model is limited. While existing parametric methods perform reasonably, they rely heavily on large datasets and frequent feedback, making them impractical in scenarios with limited human feedback. We introduce Alignment Learning with Episodic Control (ALEC), a non-parametric framework that aligns LLM outputs during inference without fine-tuning. ALEC employs a key-value memory to store the associations between generated text and its corresponding values. It leverages a novel confidence-based writing scheme to update these stored values, maximizing the use of available data. During inference, ALEC utilizes a nearest-neighbor mechanism to estimate the values of generated texts, enabling the selection of the optimal text for decoding. Our method outperforms state-of-the-art baselines on harmless, helpful, and summarization tasks, demonstrating improved alignment with minimal interactions with the true reward model.

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Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts
ChaeHun Park | Hojun Cho | Jaegul Choo

Automatic speech recognition systems often fail on specialized vocabulary in tasks such as weather forecasting. To address this, we introduce an evaluation dataset of Korean weather queries. The dataset was recorded by diverse native speakers following pronunciation guidelines from domain experts and underwent rigorous verification. Benchmarking both open-source models and a commercial API reveals high error rates on meteorological terms. We also explore a lightweight text-to-speech-based data augmentation strategy, yielding substantial error reduction for domain-specific vocabulary and notable improvement in overall recognition accuracy. Our dataset is available at https://huggingface.co/datasets/ddehun/korean-weather-asr.

Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks, yet they remain fundamentally limited in their understanding of 3D spatial structures.We propose Geometric Distillation, a lightweight, annotation-free fine-tuning framework that injects human-inspired geometric cues into pretrained VLMs without modifying their architecture.By distilling (1) sparse correspondences, (2) relative depth relations, and (3) dense cost volumes from off-the-shelf 3D foundation models (e.g., MASt3R, VGGT), our method shapes representations to be geometry-aware while remaining compatible with natural image–text inputs.Through extensive evaluations on 3D vision-language reasoning and 3D perception benchmarks, our method consistently outperforms prior approaches, achieving improved 3D spatial reasoning with significantly lower computational cost.Our work demonstrates a scalable and efficient path to bridge 2D-trained VLMs with 3D understanding, opening up wider use in spatially grounded multimodal tasks.

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CAPE: Context-Aware Personality Evaluation Framework for Large Language Models
Jivnesh Sandhan | Fei Cheng | Tushar Sandhan | Yugo Murawaki

Psychometric tests, traditionally used to assess humans, are now being applied to Large Language Models (LLMs) to evaluate their behavioral traits. However, existing studies follow a context-free approach, answering each question in isolation to avoid contextual influence. We term this the Disney World test, an artificial setting that ignores real-world applications, where conversational history shapes responses. To bridge this gap, we propose the first Context-Aware Personality Evaluation (CAPE) framework for LLMs, incorporating prior conversational interactions. To thoroughly analyze the influence of context, we introduce novel metrics to quantify the consistency of LLM responses, a fundamental trait in human behavior. Our exhaustive experiments on 7 LLMs reveal that conversational history enhances response consistency via in-context learning but also induces personality shifts, with GPT-3.5-Turbo and GPT-4-Turbo exhibiting extreme deviations. While GPT models are robust to question ordering, Gemini-1.5-Flash and Llama-8B display significant sensitivity. Moreover, GPT models response stem from their intrinsic personality traits as well as prior interactions, whereas Gemini-1.5-Flash and Llama-8B heavily depend on prior interactions. Finally, applying our framework to Role Playing Agents (RPAs) shows context-dependent personality shifts improve response consistency and better align with human judgments.

Vision-Language Models (VLMs) show promise for autonomous driving, yet their struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. To overcome this, we introduce AgentThink, a pioneering unified framework that, for the first time, integrates Chain-of-Thought (CoT) reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. AgentThink’s core innovations include: (i) Structured Data Generation, by establishing an autonomous driving tool library to automatically construct structured, self-verified reasoning data explicitly incorporating tool usage for diverse driving scenarios; (ii) A Two-stage Training Pipeline, employing Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO) to equip VLMs with the capability for autonomous tool invocation; and (iii) Agent-style Tool-Usage Evaluation, introducing a novel multi-tool assessment protocol to rigorously evaluate the model’s tool invocation and utilization. Experiments on the DriveLMM-o1 benchmark demonstrate AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54%, while markedly improving reasoning quality and consistency. Furthermore, ablation studies and robust zero-shot/few-shot generalization experiments across various benchmarks underscore its powerful capabilities. These findings highlight a promising trajectory for developing trustworthy and tool-aware autonomous driving models.

Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces external knowledge but suffers from hallucinations and latency due to noisy retrievals. Continued pretraining internalizes domain knowledge but is costly and lacks cross-domain flexibility. We attribute this challenge to the long-tail distribution of domain knowledge, which leaves partial yet useful internal knowledge underutilized. We further argue that knowledge acquisition should be progressive, mirroring human learning: first understanding concepts, then applying them to complex reasoning. To address this, we propose Selct2Know (S2K), a cost-effective framework that internalizes domain knowledge through an internal-external knowledge self-selection strategy and selective supervised fine-tuning. We also introduce a structured reasoning data generation pipeline and integrate GRPO to enhance reasoning ability. Experiments on medical, legal, and financial QA benchmarks show that S2K consistently outperforms existing methods and matches domain-pretrained LLMs with significantly lower cost.

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GenPTQ: Green Post-Training Quantization for Large-Scale ASR Models with Mixed-Precision Bit Allocation
Beom Jin Kang | Hyun Kim

Large-scale models have achieved state-of-the-art performance in automatic speech recognition (ASR), but their high memory and computation demands pose significant challenges for deployment. To address these challenges, weight-only quantization is widely adopted in large-scale models, where weights dominate memory usage, as it enables efficient compression with minimal accuracy degradation compared to activation quantization. Accordingly, most prior quantization studies for ASR models have focused on weights and employed quantization-aware training (QAT) to restore accuracy. However, QAT incurs substantial additional training costs, posing clear limitations for practical application to large-scale models. Moreover, despite the varying quantization sensitivity across layers, mixed-precision quantization (MPQ) remains underexplored in ASR. In this paper, we propose GenPTQ, a mixed-precision post-training quantization method that optimizes the trade-off among accuracy, model size, and optimization cost by leveraging gradient-based sensitivity measurement and transforming the search space into a continuous domain for efficient numerical optimization. Applied to Whisper and Conformer models across multiple speech datasets, GenPTQ achieves up to 89.1% model size reduction (2.5-bit average precision) with only a 0.8% increase in WER, and completes optimization in just 15 seconds. These results demonstrate its effectiveness for low-resource ASR deployment.

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“Where Does This Strange Smell Come from?”: Enabling Conversational Interfaces for Artificial Olfaction
Xueyi Zhou | Qi Lu | Dong-Kyu Chae

Existing Artificial Olfaction (AO) primarily serves two tasks: Odor Classification (OC) and Odor Source Localization (OSL). Both tasks w.r.t. indoor event detection scenarios are studied either using a single electronic nose (e-nose) mounted on the ceiling or mobile robot(s) equipped with e-noses. However, they are not compatible with smart home scenarios due to diverse obstacles (e.g., chairs and tables) and the need for natural interaction. In this paper, we explore the feasibility and usability of a Conversational Interfaces for Artificial Olfaction (CIAO) system using Large Language Models (LLMs) in Smart Home. We made the first olfaction-oriented corpus for LLM evaluation, as well as an olfaction dataset via a self-developed olfactory sensory network. We train the dedicated models for OSL and OC using the dataset and integrate them into a tool within the MCP (Model Context Protocol) server. Five commercial LLMs are used as MCP clients for experiments and validation. Our experimental results indicate that our CIAO system is technically feasible and applicable. Besides, we observe that ChatGPT-4o relatively outperforms in terms of both answer quality and overall LLM usability in pervasive IoT scenarios. Qwen-Plus, in contrast, appears to be a promising solution for robot-compatible applications. To our knowledge, this work is the first effort to bring forward conversational interfaces for AO, enabling multi-turn conversations with contexts beyond one-off question answering. Our codes and partial corpus are available at https://github.com/HokyeeJau/CIAO.

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LightRAG: Simple and Fast Retrieval-Augmented Generation
Zirui Guo | Lianghao Xia | Yanhua Yu | Tu Ao | Chao Huang

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness, which can lead to fragmented answers that fail to capture complex interdependencies. To address these challenges, we propose LightRAG, a novel framework that incorporates graph structures into text indexing and retrieval processes. This innovative approach employs a dual-level retrieval system that enhances comprehensive information retrieval from both low- and high-level knowledge discovery. Additionally, the integration of graph structures with vector representations facilitates efficient retrieval of related entities and their relationships, significantly improving response times while maintaining contextual relevance. This capability is further enhanced by an incremental update algorithm that ensures the timely integration of new data, allowing the system to remain effective and responsive in rapidly changing data environments. Extensive experimental validation demonstrates considerable improvements in retrieval accuracy and efficiency compared to existing approaches. We have made our LightRAG framework open source and anonymously available at the link: https://anonymous.4open.science/r/LightRAG-2BEE.

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Beyond Distribution: Investigating Language Models’ Understanding of Sino-Korean Morphemes
Taehee Jeon

We investigate whether Transformer-based language models, trained solely on Hangul text, can learn the compositional morphology of Sino-Korean (SK) morphemes, which are fundamental to Korean vocabulary. Using BERT_BASE and fastText, we conduct controlled experiments with target words and their “real” vs. “fake” neighbors—pairs that share a Hangul syllable representing the same SK morpheme vs. those that share only the Hangul syllable. Our results show that while both models—especially BERT—distinguish real and fake pairs to some extent, their performance is primarily driven by the frequency of each experimental word rather than a true understanding of SK morphemes. These findings highlight the limits of distributional learning for morpheme-level understanding and emphasize the need for explicit morphological modeling or Hanja-aware strategies to improve semantic representation in Korean language models. Our dataset and analysis code are available at: https://github.com/taeheejeon22/ko-skmorph-lm.

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Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning
Qi Yang | Jingjie Zeng | Liang Yang | Kai Ma | Hongfei Lin

Sarcasm detection is a crucial yet challenging task in natural language processing. Existing methods primarily rely on supervised learning or prompt engineering, which often struggle to capture the complex reasoning process required for effective sarcasm detection. This paper proposes a novel approach that decomposes sarcasm detection into three fundamental dimensions: language, context, and emotion, meticulously modeling the sarcasm reasoning process. To enhance the quality of reasoning, we employ reinforcement learning algorithms and design customized reward models for each dimension. We utilize five widely used sarcasm detection datasets and annotate the sarcasm reasoning process from these three dimensions to improve the performance of the reward models. Experiments demonstrate that our method outperforms state-of-the-art baseline methods in most cases. Additionally, we observe the central role of emotional contrast in sarcasm detection. Our research provides empirical insights into the mechanism of sarcasm, emphasizing that emotional contrast is at its core, supported by linguistic and contextual cues.

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but pose risks of inadvertently exposing copyrighted or proprietary data, especially when such data is used for training but not intended for distribution. Traditional methods address these leaks only after content is generated, which can lead to the exposure of sensitive information. This study introduces a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks. By using a curated dataset of copyrighted materials, we trained a neural network classifier to identify risks, allowing for early intervention by stopping the generation process or altering outputs to prevent disclosure. Integrated with a Retrieval-Augmented Generation (RAG) system, this framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. Our results show that analyzing internal states effectively mitigates the risk of copyrighted data leakage, offering a scalable solution that fits smoothly into AI workflows, ensuring compliance with copyright regulations while maintaining high-quality text generation. Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but pose risks of inadvertently exposing copyrighted or proprietary data, especially when such data is used for training but not intended for distribution. Traditional methods address these leaks only after content is generated, which can lead to the exposure of sensitive information. This study introduces a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks. By using a curated dataset of copyrighted materials, we trained a neural network classifier to identify risks, allowing for early intervention by stopping the generation process or altering outputs to prevent disclosure. Integrated with a Retrieval-Augmented Generation (RAG) system, this framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. Our results show that analyzing internal states effectively mitigates the risk of copyrighted data leakage, offering a scalable solution that fits smoothly into AI workflows, ensuring compliance with copyright regulations while maintaining high-quality text generation. Our code can be found here: (https://anonymous.4open.science/r/Internal-states-leakage-9D6E).

Large vision-language models (LVLMs) have achieved remarkable performance on multimodal tasks. However, they still suffer from hallucinations, generating text inconsistent with visual input, posing significant risks in real-world applications. Existing approaches to address this issue focus on incorporating external knowledge bases, alignment training, or decoding strategies, all of which require substantial computational cost and time. Recent works try to explore more efficient alternatives by adjusting LVLMs’ internal representations. Although promising, these methods may cause hallucinations to be insufficiently suppressed or lead to excessive interventions that negatively affect normal semantics. In this work, we leverage sparse autoencoders (SAEs) to identify semantic directions closely associated with faithfulness or hallucination, extracting more precise and disentangled hallucination-related representations. Our analysis demonstrates that interventions along the identified faithful direction can mitigate hallucinations, while those along the hallucinatory direction can exacerbate them. Building on these insights, we propose **S**teering LVLMs via **S**AE **L**atent Directions (SSL), a plug-and-play method based on SAE-derived latent directions to mitigate hallucinations in LVLMs. Extensive experiments demonstrate that SSL significantly outperforms existing decoding approaches in mitigating hallucinations, while maintaining transferability across different model architectures with negligible additional time overhead. The code is available at [https://github.com/huazhenglin2003/SSL](https://github.com/huazhenglin2003/SSL).

Chart understanding requires models to effectively analyze and reason about numerical data, textual elements, and complex visual components. Our observations reveal that the perception capabilities of existing large vision-language models (LVLMs) constitute a critical bottleneck in this process. In this study, we delve into this perception bottleneck by decomposing it into two components: the vision encoder bottleneck, where the visual representation may fail to encapsulate the correct information, and the extraction bottleneck, where the language model struggles to extract the necessary information from the provided visual representations. Through comprehensive experiments, we find that (1) the information embedded within visual representations is substantially richer than what is typically captured by linear extractors, such as the widely used retrieval accuracy metric; (2) While instruction tuning effectively enhances the extraction capability of LVLMs, the vision encoder remains a critical bottleneck, demanding focused attention and improvement. Therefore, we further enhance the visual encoder to mitigate the vision encoder bottleneck under a contrastive learning framework. Empirical results demonstrate that our approach significantly mitigates the perception bottleneck and improves the ability of LVLMs to comprehend charts.

Function calling enables large language models (LLMs) to interact with external systems by leveraging tools and APIs. When faced with multi-step tool usage, LLMs still struggle with tool selection, parameter generation, and tool-chain planning. Existing methods typically rely on manually designing task-specific demonstrations, or retrieving from a curated library. These approaches demand substantial expert effort and prompt engineering becomes increasingly complex and inefficient as tool diversity and task difficulty scale. To address these challenges, we propose a self-guided method, Stepwise ExperiencE Recall (SEER), which performs fine-grained, stepwise retrieval from a continually updated experience pool. Instead of relying on static or manually curated library, SEER incrementally augments the experience pool with past successful trajectories, enabling continuous expansion of the pool and improved model performance over time. Evaluated on the ToolQA benchmark, SEER achieves an average improvement of 6.1% on easy and 4.7% on hard questions. We further test SEER on 𝜏-bench, which includes two real-world domains. Powered by Qwen2.5-7B and Qwen2.5-72B models, SEER demonstrates substantial accuracy gains of 7.44% and 23.38%, respectively.

Generative Information Retrieval is an emerging retrieval paradigm that exhibits remarkable performance in monolingual scenarios. However, applying these methods to multilingual retrieval still encounters two primary challenges, cross-lingual identifier misalignment and identifier inflation. To address these limitations, we propose Multilingual Generative Retrieval via Cross-lingual Semantic Compression (MGR-CSC), a novel framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space, and we propose a dynamic multi-step constrained decoding strategy during retrieval. MGR-CSC improves cross-lingual alignment by assigning consistent identifiers and enhances decoding efficiency by reducing redundancy. Experiments demonstrate that MGR-CSC achieves outstanding retrieval accuracy, improving by 6.83% on mMarco100k and 4.77% on mNQ320k, while reducing document identifiers length by 74.51% and 78.2%, respectively. We publicly release our dataset and code at https://github.com/simengggg/MGR-CSC

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Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation
Hui Huang | Julien Velcin | Yacine Kessaci

Question-Answering (QA) systems are vital for rapidly accessing and comprehending information in academic literature.However, some academic questions require synthesizing information across multiple documents. While several prior resources consider multi-document QA, they often do not strictly enforce cross-document synthesis or exploit the explicit inter-paper structure that links sources.To address this, we introduce a pipeline methodology for constructing a Multi-Document Academic QA (MDA-QA) dataset. By both detecting communities based on citation networks and leveraging Large Language Models (LLMs), we were able to form thematically coherent communities and generate QA pairs related to multi-document content automatically.We further develop an automated filtering mechanism to ensure multi-document dependence.Our resulting dataset consists of 6,804 QA pairs and serves as a benchmark for evaluating multi-document retrieval and QA systems.Our experimental results highlight that standard lexical and embedding-based retrieval methods struggle to locate all relevant documents, indicating a persistent gap in multi-document reasoning. We release our dataset and source code for the community.

Large language models (LLMs) based Multilingual Knowledge Graph Completion (MKGC) aim to predict missing facts by leveraging LLMs’ multilingual understanding capabilities, improving the completeness of multilingual knowledge graphs (KGs).However, existing MKGC research underutilizes the multilingual capabilities of LLMs and ignores the shareability of cross-lingual knowledge.In this paper, we propose a novel MKGC framework that leverages multilingual shared knowledge to significantly enhance performance through two components: Knowledge-level Grouped Mixture of Experts (KL-GMoE) and Iterative Entity Reranking (IER).KL-GMoE efficiently models shared knowledge, while IER significantly enhances its utilization.To evaluate our framework, we constructed a mKG dataset containing 5 languages and conducted comprehensive comparative experiments with existing state-of-the-art (SOTA) MKGC method.The experimental results demonstrate that our framework achieves improvements of 5.47%, 3.27%, and 1.01% in the Hits@1, Hits@3, and Hits@10 metrics, respectively, compared with SOTA MKGC method.Further experimental analysis revealed the properties of knowledge sharing in settings of unseen and unbalanced languages.We have released the dataset and code for our work on https://github.com/gaoxiaofei07/KL-GMoE.

Transformer-based self-attention mechanism serves as the core of modern language models, yet it often suffers from *localization*, where attentions collapse onto a limited subset of tokens and fail to capture long-range dependencies. To address this issue, we propose **Self-Attention One-step Belief Propagation (SAOBP)**, a refinement framework that injects *multi-hop* relationships through a belief propagation process. To interpret and quantify these interactions, we introduce **Global Token Dependency (GTD)** that captures the relative contribution of multi-hop connections within the attention graph. Empirical results indicate that SAOBP helps prevent entropy collapse in deeper layers and adaptively maintains GTD at task-appropriate levels, thereby supporting improvements in model performance. Importantly, we observe competitive gains in small-scale models, highlighting its potential for improving inference quality in resource-constrained scenarios.

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Imagination and Contemplation: A Balanced Framework for Semantic-Augmented Multimodal Machine Translation
Zhuang Yu | Shiliang Sun | Jing Zhao | Tengfei Song | Hao Yang

Multimodal Machine Translation (MMT) enhances textual translation through auxiliary inputs such as images, which is particularly effective in resolving linguistic ambiguities. However, visual information often introduces redundancy or noise, potentially impairing translation quality. To address this challenge, we propose a balanced semantic-augmented framework that integrates “Imagination“ and “Contemplation“ in multimodal understanding. Specifically, we first generate synthetic images from the source text and align them with the authentic images via an optimal transport (OT) loss to enhance visual-semantic consistency. A CLIP-based similarity gating mechanism is introduced to adaptively fuse visual features from both authentic and synthetic images during visual representation learning. To strengthen semantic grounding, a neural machine translation (NMT) branch is incorporated as a regularization signal, and a Kullback-Leibler (KL) divergence is applied between MMT and NMT outputs to mitigate modality mismatch. Furthermore, an image-text contrastive (ITC) loss aligns the final translations with image representations, reinforcing multimodal coherence. Experiments on multiple translation datasets with a diverse set of language pairs demonstrate that our framework outperforms existing baselines, particularly in cases with visually ambiguous or weakly correlated content.

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NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use
Yuqing Zhang | Ecesu Ürker | Tessa Verhoef | Gemma Boleda | Arianna Bisazza

Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeling) cannot get at causal mechanisms, and experimental paradigms with humans are hard to apply to semantic change due to the extended diachronic processes involved. This work introduces NeLLCom-Lex, a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system (e.g. English) and then systematically manipulating their communicative needs. Using a well-established color naming task, we simulate the evolution of a lexical system within a single generation, and study which factors lead agents to: (i) develop human-like naming behavior and lexicons, and (ii) change their behavior and lexicons according to their communicative needs. Our experiments with different supervised and reinforcement learning pipelines show that neural agents trained to ‘speak’ an existing language can reproduce human-like patterns in color naming to a remarkable extent, supporting the further use of NeLLCom-Lex to elucidate the mechanisms of semantic change.

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RLMEval: Evaluating Research-Level Neural Theorem Proving
Auguste Poiroux | Antoine Bosselut | Viktor Kunčak

Despite impressive results on curated benchmarks, the practical impact of large language models (LLMs) on research-level neural theorem proving and proof autoformalization is still limited. We introduce RLMEval, an evaluation suite for these tasks, focusing on research-level mathematics from real-world Lean formalization projects. RLMEval targets the evaluation of neural theorem proving and proof autoformalization on challenging research-level theorems by leveraging real Lean Blueprint formalization projects. Our evaluation of state-of-the-art models on RLMEval, comprising 613 theorems from 6 Lean projects, reveals a significant gap: progress on existing benchmarks does not readily translate to these more realistic settings, with the best model achieving only a 10.3% pass rate. RLMEval provides a new, challenging benchmark designed to guide and accelerate progress in automated reasoning for formal mathematics.

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KaeDe: Progressive Generation of Logical Forms via Knowledge-Aware Question Decomposition for Improved KBQA
Ranran Bu | Jian Cao | Jianqi Gao | Shiyou Qian | Hongming Cai

Knowledge base question answering (KBQA) refers to the task of answering natural language questions using large-scale structured knowledge bases (KBs). Existing semantic parsing-based (SP-based) methods achieve superior performance by directly converting questions into structured logical form (LF) queries using fine-tuned large language models (LLMs). However, these methods face the key challenge of difficulty in directly generating LFs for complex graph structures, which often leads to non-executable LFs that negatively impact overall KBQA performance. To address this challenge, we propose KaeDe, a novel generate-then-retrieve method for KBQA. This approach integrates knowledge-aware question decomposition and subsequent progressive LF generation within the generation phase, followed by an unsupervised retrieval phase. Specifically, the original question is decomposed into simplified, topic entity-centric sub-questions and explanations within the KB context. Path-level LFs are derived from these intermediate expressions and then combined into a comprehensive graph-level LF. Finally, the LF is refined through unsupervised entity and relation retrieval. Experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance on WebQuestionSP (WebQSP) and ComplexWebQuestions (CWQ) benchmarks, particularly with fewer model parameters.

Recent failures such as Google Gemini generating people of color in Nazi-era uniforms illustrate how AI outputs can be factually plausible yet socially harmful. AI models are increasingly evaluated for “fairness,” yet existing benchmarks often conflate two fundamentally different dimensions: factual correctness and normative fairness. A model may generate responses that are factually accurate but socially unfair, or conversely, appear fair while distorting factual reality. We argue that identifying the boundary between fact and fair is essential for meaningful fairness evaluation. We introduce Fact-or-Fair, a benchmark with (i) objective queries aligned with descriptive, fact-based judgments, and (ii) subjective queries aligned with normative, fairness-based judgments. Our queries are constructed from 19 statistics and are grounded in cognitive psychology, drawing on representativeness bias, attribution bias, and ingroup–outgroup bias to explain why models often misalign fact and fairness. Experiments across ten frontier models reveal different levels of fact-fair trade-offs. By reframing fairness evaluation, we provide both a new theoretical lens and a practical benchmark to advance the responsible model assessments. Our test suite is publicly available at https://github.com/uclanlp/Fact-or-Fair.

Due to the widespread dissemination of rumors on social media platforms, detecting rumors has been a long-standing concern for various communities. However, existing rumor detection methods rarely consider the fairness issues inherent in the model, which can lead to biased predictions across different stakeholder groups (e.g., domains and originating platforms of the detected content), also undermining their detection effectiveness. In this work, we propose a two-step framework to address this issue. First, we perform unsupervised partitioning to dynamically identify potential unfair data patterns without requiring sensitive attribute annotations. Then, we apply invariant learning to these partitions to extract fair and informative feature representations that enhance rumor detection. Extensive experiments show that our method outperforms strong baselines regarding detection and fairness performance, and also demonstrate robust performance on out-of-distribution samples. Further empirical results indicate that our learned features remain informative and fair across stakeholder groups and can correct errors when applied to existing baselines.

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STEAM: A Semantic-Level Knowledge Editing Framework for Large Language Models
Geunyeong Jeong | Juoh Sun | Seonghee Lee | Harksoo Kim

Large Language Models store extensive factual knowledge acquired during large-scale pre-training. However, this knowledge is inherently static, reflecting only the state of the world at the time of training. Knowledge editing has emerged as a promising solution for updating outdated or incorrect facts without full retraining. However, most existing locate-and-edit methods primarily focus on token-level likelihood optimization without addressing semantic coherence. Our analysis reveals that such edited knowledge is often encoded as isolated residual streams in the model’s latent space, distinct from pre-existing knowledge and bypassing natural reasoning process. To address this, we propose STEAM, a semantic-level knowledge editing framework that enhances integration of updated knowledge into the model’s knowledge structure. STEAM first identifies target representations as semantic anchors for the updated factual association, then guides the internal representation of the edited fact towards these anchors through an alignment loss during optimization. Experimental results demonstrate that STEAM improves model’s ability to reason with edited knowledge and enhances semantic coherence, underscoring the importance of latent-space alignment for reliable and coherent knowledge editing. The code is available at https://github.com/GY-Jeong/STEAM.

Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successfully transferred to non-high-resource languages due to resource constraints, which struggles with multilingual reasoning tasks. To this end, we propose Structured-of-Thought (SoT), a training-free method that improves the performance on multilingual reasoning through a multi-step transformation: Language Thinking Transformation and Structured Knowledge Transformation. The SoT method converts language-specific semantic information into language-agnostic structured representations, enabling the models to understand the query in different languages more sophisticated. Besides, SoT effectively guides LLMs toward more concentrated reasoning to maintain consistent underlying reasoning pathways when handling cross-lingual variations in expression. Experimental results demonstrate that SoT outperforms several strong baselines on multiple multilingual reasoning benchmarks when adapting to various backbones of LLMs. It can also be integrated with other training-free strategies for further improvements. Our code is available at https://github.com/Cherry-qwq/SoT.

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How Reliable is Multilingual LLM-as-a-Judge?
Xiyan Fu | Wei Liu

LLM-as-a-Judge has emerged as a popular evaluation strategy, where advanced large language models assess generation results in alignment with human instructions. While these models serve as a promising alternative to human annotators, their reliability in multilingual evaluation remains uncertain. To bridge this gap, we conduct a comprehensive analysis of multilingual LLM-as-a-Judge. Specifically, we evaluate five models from different model families across five diverse tasks involving 25 languages. Our findings reveal that LLMs struggle to achieve consistent judgment results across languages, with an average Fleiss’ Kappa of approximately 0.3, and some models performing even worse. To investigate the cause of inconsistency, we analyze various influencing factors. We observe that consistency varies significantly across languages, with particularly poor performance in low-resource languages. Additionally, we find that neither training on multilingual data nor increasing model scale directly improves judgment consistency. These findings suggest that LLMs are not yet reliable for evaluating multilingual predictions. Our work provides valuable insights into the limitations of multilingual LLM-as-a-Judge, and sheds light on future research.

Large Language Models (LLMs) have demonstrated strong performance in open-ended generation tasks. However, they often struggle to adapt content to users with differing cognitive capacities, leading to a phenomenon we term cognitive misalignment. This issue arises in two forms: knowledge-level misalignment, where content is too complex or too simplistic relative to user understanding, and presentation style misalignment, where the structure or tone hinders effective comprehension. To address these challenges, we propose the Cognitive-Level Alignment Framework (CLAF), a general-purpose generation framework that aligns both knowledge complexity and presentation style with user cognition. CLAF integrates a capability-aware retrieval module based on a hierarchical knowledge graph and a style optimization module guided by Bloom’s taxonomy and preference learning. Additionally, a knowledge-controllable generation component ensures consistency and relevance throughout the output. To support training and evaluation, we construct Scale, a cognitively annotated dataset containing responses at multiple comprehension levels per query. Empirical results show that CLAF enhances the adaptability and informativeness of LLM outputs across a range of user profiles, offering a robust solution to cognitive-level alignment in real-world applications.

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Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs
Essa Jan | Moiz Ali | Muhammad Saram Hassan | Muhammad Fareed Zaffar | Yasir Zaki

As the knowledge of large language models (LLMs) becomes outdated over time, there is a growing need for efficient methods to update them, especially when injecting proprietary information. Our study reveals that comprehension-intensive fine-tuning tasks (e.g., question answering and blanks) achieve substantially higher knowledge retention rates (48%) compared to mapping-oriented tasks like translation (17%) or text-to-JSON conversion (20%), despite exposure to identical factual content. We demonstrate that this pattern persists across model architectures and follows scaling laws, with larger models showing improved retention across all task types. However, all models exhibit significant performance drops when applying injected knowledge in broader contexts, suggesting limited semantic integration. These findings show the importance of task selection in updating LLM knowledge, showing that effective knowledge injection relies not just on data exposure but on the depth of cognitive engagement during fine-tuning.

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INDOORWORLD : Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment
Dekun Wu | Frederik Brudy | Bang Liu | Yi Wang

Virtual environments are essential to AI agent research. Existing environments for LLM agent research typically focus on either physical task solving or social simulation, with the former oversimplifying agent individuality and social dynamics, and the latter lacking physical grounding of social behaviors. We introduce IndoorWorld, a heterogeneous multi-agent environment that tightly integrates physical and social dynamics. By introducing novel challenges for LLM-driven agents in orchestrating social dynamics to influence physical environments and anchoring social interactions within world states, IndoorWorld opens up possibilities of LLM-based building occupant simulation for architectural design. We demonstrate the potential with a series of experiments within an office setting to examine the impact of multi-agent collaboration, resource competition, and spatial layout on agent behavior.

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ARXSA: A General Negative Feedback Control Theory in Vision-Language Models
Zeyu Zhang | Tianqi Chen | Yuki Todo

The Transformer model has been increasingly applied across various domains, driven by the self-attention mechanism, which offers robust data processing capabilities and has substantially contributed to the advancement of the model. In the self-attention mechanism, three core matrices from the same data batch are computed together to determine correlations between input elements. Drawing inspiration from the efficiency and stability conferred by negative feedback structures in predictive control systems, the concept of vertical training was introduced to integrate data from multiple batches. Accordingly, this paper proposes an autoregressive with exogenous inputs (ARX) approach for the self-attention mechanism, transforming the Encoder block into a negative feedback predictive control system. A network architecture based on this method is also proposed, enabling the autoregressive with exogenous inputs for self-attention to transmit data from batches at previous time points. The effectiveness of the proposed approach is validated through comparative experimental evaluations.

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Breaking the Attention Trap in Code LLMs: A Rejection Sampling Approach to Enhance Code Execution Prediction
Xingcheng Ruan | Haoxiang Geng | Yunhui Xia | Bingran Zhao

Code-specific Large Language Models (Code LLMs) have greatly improved performance across code-related tasks, offering substantial benefits in practical applications. However, existing research reveals significant performance bottlenecks in Code Execution tasks, which requires models to predict the execution results of given code snippets. This study identifies that, the Attention Trap phenomenon in training data constitutes a key constraint on model performance. To address this phenomenon, we propose the Attention Cracking with Rejection Sampling (AC-RS) method. The method first applies structural optimization to training data to eliminate attention traps. Then, it conducts secondary training on the outputs generated by the fine-tuned model to mitigate potential negative impacts from manual data intervention. Experimental results show that AC-RS significantly enhances the accuracy of Code Execution while preserving models’ original capabilities. Notably, the optimized 7B model achieves Code Execution accuracy comparable to 32B model and GPT-4o.

The advancement of Large Language Models (LLMs) enables flexible and interpretable automatic evaluations. In the field of machine translation evaluation, utilizing LLMs with translation error annotations based on Multidimensional Quality Metrics (MQM) yields more human-aligned judgments. However, current LLM-based evaluation methods still face challenges in accurately identifying error spans and assessing their severity. In this paper, we propose HiMATE, a Hierarchical Multi-Agent Framework for Machine Translation Evaluation. We argue that existing approaches inadequately exploit the fine-grained structural and semantic information within the MQM hierarchy. To address this, we develop a hierarchical multi-agent system grounded in the MQM error typology, enabling granular evaluation of subtype errors. Two key strategies are incorporated to further mitigate systemic hallucinations within the framework: the utilization of the model’s self-reflective capability and the facilitation of agent discussion involving asymmetric information. Empirically, HiMATE outperforms competitive baselines across different datasets in conducting human-aligned evaluations. Further analyses underscore its significant advantage in error span detection and severity assessment, achieving an average F1-score improvement of 89% over the best-performing baseline. We make our code and data publicly available at https://github.com/nlp2ct-shijie/HiMATE.

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ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments
Gili Lior | Eliya Habba | Shahar Levy | Avi Caciularu | Gabriel Stanovsky

LLMs are highly sensitive to prompt phrasing, yet standard benchmarks typically report performance using a single prompt, raising concerns about the reliability of such evaluations. In this work, we argue for a stochastic method of moments evaluation over the space of meaning-preserving prompt perturbations. We introduce a formal definition of *reliable evaluation* that accounts for prompt sensitivity, and suggest ReliableEval - a method for estimating the number of prompt resamplings needed to obtain meaningful results. Using our framework, we stochastically evaluate five frontier LLMs and find that even top-performing models like GPT-4o and Claude-3.7-Sonnet exhibit substantial prompt sensitivity. Our approach is model-, task-, and metric-agnostic, offering a recipe for meaningful and robust LLM evaluation.

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From Characters to Tokens: Dynamic Grouping with Hierarchical BPE
Rares Dolga | Lucas Maystre | Tudor Berariu | David Barber

Subword tokenization methods like Byte Pair Encoding (BPE) are widely used in large language models due to their balance of vocabulary compactness and representational power. However, they suffer from inefficiencies in representing rare words and require large embedding matrices. Character-level models address these issues but introduce performance bottlenecks, particularly in Transformer-based architectures. Recent hierarchical models attempt to merge the benefits of both paradigms by grouping characters into patches, but existing patching strategies either rely on whitespace—limiting applicability to certain languages—or require auxiliary models that introduce new dependencies. In this paper, we propose a dynamic character grouping method that leverages the structure of existing BPE tokenization without requiring additional models. By appending explicit end-of-patch markers to BPE tokens and introducing a second-level BPE compression stage to control patch granularity, our method offers efficient, flexible, and language-agnostic representations. Empirical results demonstrate that our approach matches or exceeds the performance of dynamic entropy- and whitespace-based patching strategies, while maintaining a compact vocabulary.

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Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant
Lei Shen | Xiaoyu Shen

In recent years, multi-agent frameworks powered by large language models (LLMs) have advanced rapidly. Despite this progress, there is still a notable absence of benchmark datasets specifically tailored to evaluate their performance. To bridge this gap, we introduce Auto-SLURP, a benchmark dataset aimed at evaluating LLM-based multi-agent frameworks in the context of smart personal assistants. Auto-SLURP extends the original SLURP dataset—initially developed for natural language understanding tasks—by relabeling the data and integrating simulated servers and external services. This enhancement enables a comprehensive end-to-end evaluation pipeline, covering language understanding, task execution, and response generation. Our experiments demonstrate that Auto-SLURP presents a significant challenge for current state-of-the-art frameworks, highlighting that truly reliable and intelligent multi-agent personal assistants remain a work in progress.

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NER Retriever: Zero-Shot Named Entity Retrieval with Type-Aware Embeddings
Or Shachar | Uri Katz | Yoav Goldberg | Oren Glickman

We present NER Retriever, a zero-shot retrieval framework for ad-hoc Named Entity Recognition (NER), where a user-defined type description is used to retrieve documents mentioning entities of that type. Instead of relying on fixed schemas or fine-tuned models, our method builds on pretrained language models (LLMs) to embed both entity mentions and type descriptions into a shared semantic space. We show that internal representations—specifically, the value vectors from mid-layer transformer blocks—encode fine-grained type information more effectively than commonly used top-layer embeddings. To refine these representations, we train a lightweight contrastive projection network that aligns type-compatible entities while separating unrelated types. The resulting entity embeddings are compact, type-aware, and well-suited for nearest-neighbor search. Evaluated on three benchmarks, NER Retriever significantly outperforms both lexical (BM25) and dense (sentence-level) retrieval baselines, particularly in low-context settings. Our findings provide empirical support for representation selection within LLMs and demonstrate a practical solution for scalable, schema-free entity retrieval.

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MMATH: A Multilingual Benchmark for Mathematical Reasoning
Wenyang Luo | Xin Zhao | Jing Sha | Shijin Wang | Ji-Rong Wen

The advent of large reasoning models, such as OpenAI o1 and DeepSeek R1, has significantly advanced complex reasoning tasks. However, their capabilities in multilingual complex reasoning remain underexplored, with existing efforts largely focused on simpler tasks like MGSM. To address this gap, we introduce , a benchmark for multilingual complex reasoning spanning 374 high-quality math problems across 10 typologically diverse languages. Using , we observe that even advanced models like DeepSeek R1 exhibit substantial performance disparities across languages and suffer from a critical off-target issue—generating responses in unintended languages. To address this, we explore strategies including prompting and training, demonstrating that reasoning in English and answering in target languages can simultaneously enhance performance and preserve target-language consistency. Our findings offer new insights and practical strategies for advancing the multilingual reasoning capabilities of large language models. Our code and data could be found at https://github.com/RUCAIBox/MMATH.

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MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters
Rrubaa Panchendrarajan | Rubén Míguez Pérez | Arkaitz Zubiaga

In the context of fact-checking, claims are often repeated across various platforms and in different languages, which can benefit from a process that reduces this redundancy. While retrieving previously fact-checked claims has been investigated as a solution, the growing number of unverified claims and expanding size of fact-checked databases calls for alternative, more efficient solutions. A promising solution is to group claims that discuss the same underlying facts into clusters to improve claim retrieval and validation. However, research on claim clustering is hindered by the lack of suitable datasets. To bridge this gap, we introduce MultiClaimNet, a collection of three multilingual claim cluster datasets containing claims in 86 languages across diverse topics. Claim clusters are formed automatically from claim-matching pairs with limited manual intervention. We leverage two existing claim-matching datasets to form the smaller datasets within MultiClaimNet. To build the larger dataset, we propose and validate an approach involving retrieval of approximate nearest neighbors to form candidate claim pairs and an automated annotation of claim similarity using large language models. This larger dataset contains 85.3K fact-checked claims written in 78 languages. We further conduct extensive experiments using various clustering techniques and sentence embedding models to establish baseline performance. Our datasets and findings provide a strong foundation for scalable claim clustering, contributing to efficient fact-checking pipelines.

Large language models (LLMs) excel in natural language tasks, with Chain-of-Thought (CoT) prompting enhancing reasoning through step-by-step decomposition. However, CoT struggles in knowledge-intensive tasks with multiple entities and implicit multi-hop relations, failing to connect entities systematically in zero-shot settings. Existing knowledge graph methods, limited by static structures, lack adaptability in complex scenarios. We propose DS-MHP, a zero-shot framework to enhance LLM reasoning in multi-entity relation tasks. DS-MHP operates in three stages: 1) constructing query-specific subgraphs by extracting entities and relations; 2) generating and refining multi-hop paths using a hybrid strategy of Breadth-First Search, greedy expansion, and LLM supplementation; and 3) guiding LLMs with subgraphs and paths, aggregating answers via majority voting. Evaluated on 12 datasets spanning commonsense, logical, symbolic, and arithmetic reasoning, DS-MHP outperforms baselines and state-of-the-art methods in nearly all benchmarks. It achieves overall average accuracy increases of 3.9% on Mistral-7B and 3.6% on GPT-3.5 Turbo compared to SOTA, with significant gains in logical and symbolic reasoning. Additionally, DS-MHP reduces runtime and LLM calls compared to SOTA, enhancing computational efficiency. These improvements demonstrate DS-MHP’s superior reasoning accuracy, explainability, and efficiency in complex multi-entity tasks.

Children learn to speak with a low amount of data and can be taught new words on a few-shot basis, making them particularly data-efficient learners. The BabyLM challenge aims at exploring language model (LM) training in the low-data regime but uses metrics that concentrate on the head of the word distribution. Here, we introduce LongTail-Swap (LT-Swap), a benchmark that focuses on the tail of the distribution, i.e., measures the ability of LMs to learn new words with very little exposure, like infants do. LT-Swap is a pretraining corpus-specific test set of acceptable versus unacceptable sentence pairs that isolate semantic and syntactic usage of rare words. Models are evaluated in a zero-shot fashion by computing the average log probabilities over the two members of each pair.We built two such test sets associated with the 10M words and 100M words BabyLM training sets, respectively, and evaluated 16 models from the BabyLM leaderboard. Our results not only highlight the poor performance of language models on rare words but also reveal that performance differences across LM architectures are much more pronounced in the long tail than in the head. This offers new insights into which architectures are better at handling rare word generalization. We’ve also made the code publicly available on GitHub, enabling the generation of LT-Swap benchmarks based on any English text corpus.

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TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis
Xiang Li | Xianfu Cheng | Dezhuang Miao | Xiaoming Zhang | Zhoujun Li

Multimodal Sentiment Analysis (MSA) with missing modalities has attracted increasing attention recently. While current Transformer-based methods leverage dense text information to maintain model robustness, their quadratic complexity hinders efficient long-range modeling and multimodal fusion. To this end, we propose a novel and efficient Text-enhanced Fusion Mamba (TF-Mamba) framework for robust MSA with missing modalities. Specifically, a Text-aware Modality Enhancement (TME) module aligns and enriches non-text modalities, while reconstructing the missing text semantics. Moreover, we develop Text-based Context Mamba (TC-Mamba) to capture intra-modal contextual dependencies under text collaboration. Finally, Text-guided Query Mamba (TQ-Mamba) queries text-guided multimodal information and learns joint representations for sentiment prediction. Extensive experiments on three MSA datasets demonstrate the effectiveness and efficiency of the proposed method under missing modality scenarios. Code is available at https://github.com/codemous/TF-Mamba.

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Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs
Manon Reusens | Bart Baesens | David Jurgens

Personalized Large Language Models (LLMs) are increasingly used in diverse applications, where they are assigned a specific persona—such as a happy high school teacher—to guide their responses. While prior research has examined how well LLMs adhere to predefined personas in writing style, a comprehensive analysis of consistency across different personas and task types is lacking. In this paper, we introduce a new standardized framework to analyze consistency in persona-assigned LLMs. We define consistency as the extent to which a model maintains coherent responses when assigned the same persona across different tasks and runs. Our framework evaluates personas across four different categories (happiness, occupation, personality, and political stance) spanning multiple task dimensions (survey writing, essay generation, social media post generation, single turn, and multi-turn conversations). Our findings reveal that consistency is influenced by multiple factors, including the assigned persona, stereotypes, and model design choices. Consistency also varies across tasks, increasing with more structured tasks and additional context. All code is available on GitHub.

In this work, we introduce SPLICE, a human-curated benchmark derived from the COIN instructional video dataset, designed to probe event-based reasoning across multiple dimensions: temporal, causal, spatial, contextual, and general knowledge. SPLICE includes 3,381 human-filtered videos spanning 12 categories and 180 sub-categories, such as sports, engineering, and housework. These videos are segmented into a total of 11,423 event clips. We evaluate both human participants and state-of-the-art vision-language models (VLMs) on the task of rearranging these clips into coherent event sequences to assess visual reasoning capabilities. Results reveal a significant gap: VLMs struggle to match human performance. While human-annotated textual descriptions improve model accuracy, they do not affect human performance, suggesting that models rely more on language priors than on visual understanding. Even with annotations, VLMs fall short of human-level reasoning, underscoring persistent challenges in visual reasoning. A deeper analysis across sub-categories shows that VLMs perform relatively better on videos where temporal and causal reasoning are dominant, compared to those where contextual and spatial reasoning are dominant. They also perform better on everyday tasks than on specialized ones.

Large Language Models (LLMs) have been studied intensively in the context of education, yielding heterogeneous results. Nowadays, these models are also deployed in formal education institutes. While specialized models exist, using prompt-moderated LLMs is widespread. In this study, we therefore investigate the effectiveness of prompt-moderated LLMs for math tutoring at a tertiary-level. We conduct a three-phase study with students (N=49) first receiving a review of the topics, then solving exercises, and finally writing an exam. During the exercises, they are presented with different types of assistance. We analyze the effect of LLM usage on the students’ performance, their engagement with the LLM, and their conversation strategies. Our results show that the prompt-moderation had a negative influence when compared to an unmoderated LLM. However, when the assistance was removed again, both LLM groups performed better than the control group, contradicting concerns about shallow learning. We publish the annotated conversations as a dataset to foster future research.

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SkewRoute: Training-Free LLM Routing for Knowledge Graph Retrieval-Augmented Generation via Score Skewness of Retrieved Context
Hairu Wang | Yuan Feng | Yukun Cao | Xike Xie | S Kevin Zhou

Large language models excel at many tasks but often incur high inference costs during deployment. To mitigate hallucination, many systems use a knowledge graph to enhance retrieval-augmented generation (KG-RAG). However, the large amount of retrieved knowledge contexts increase these inference costs further. A promising solution to balance performance and cost is LLM routing, which directs simple queries to smaller LLMs and complex ones to larger LLMs. However, no dedicated routing methods currently exist for RAG, and existing training-based routers face challenges scaling to this domain due to the need for extensive training data. We observe that the score distributions produced by the retrieval scorer strongly correlate with query difficulty. Based on this, we propose an extremely simple yet effective routing framework, the first specifically designed for KG-RAG that efficiently balances performance and cost in a plug-and-play manner. It delivers over 3x higher routing effectiveness while reducing runtime to less than 0.001x compared to existing methods. Our code is available at https://github.com/hrwang00/SkewRoute.

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Acquiescence Bias in Large Language Models
Daniel Braun

Acquiescence bias, i.e. the tendency of humans to agree with statements in surveys, independent of their actual beliefs, is well researched and documented. Since Large Language Models (LLMs) have been shown to be very influenceable by relatively small changes in input and are trained on human-generated data, it is reasonable to assume that they could show a similar tendency. We present a study investigating the presence of acquiescence bias in LLMs across different models, tasks, and languages (English, German, and Polish). Our results indicate that, contrary to humans, LLMs display a bias towards answering no, regardless of whether it indicates agreement or disagreement.

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Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
Niv Eckhaus | Uri Berger | Gabriel Stanovsky

LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are asynchronous. For example, in group chats, online team meetings, or social games, there is no inherent notion of turns. In this work, we develop an adaptive asynchronous LLM agent consisting of two modules: a generator that decides what to say, and a scheduler that decides when to say it. To evaluate our agent, we collect a unique dataset of online Mafia games, where our agent plays with human participants. Overall, our agent performs on par with human players, both in game performance metrics and in its ability to blend in with the other human players. Our analysis shows that the agent’s behavior in deciding when to speak closely mirrors human patterns, although differences emerge in message content. We make all of our code and data publicly available. This work paves the way for integration of LLMs into realistic human group settings, from assistance in team discussions to educational and professional environments where complex social dynamics must be navigated.

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How Sampling Affects the Detectability of Machine-written texts: A Comprehensive Study
Matthieu Dubois | François Yvon | Pablo Piantanida

As texts generated by Large Language Models (LLMs) are ever more common and often indistinguishable from human-written content, research on automatic text detection has attracted growing attention. Many recent detectors report near-perfect accuracy, often boasting AUROC scores above 99%. However, these claims typically assume fixed generation settings, leaving open the question of how robust such systems are to changes in decoding strategies. In this work, we systematically examine how sampling-based decoding impacts detectability, with a focus on how subtle variations in a model’s (sub)word-level distribution affect detection performance. We find that even minor adjustments to decoding parameters - such as temperature, top-p, or nucleus sampling - can severely impair detector accuracy, with AUROC dropping from near-perfect levels to 1% in some settings. Our findings expose critical blind spots in current detection methods and emphasize the need for more comprehensive evaluation protocols. To facilitate future research, we release a large-scale dataset encompassing 37 decoding configurations, along with our code and evaluation framework https://github.com/BaggerOfWords/Sampling-and-Detection.

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An Improved, Strong Baseline for Pre-Trained Large Language Models as Task-Oriented Dialogue Systems
Sebastian Steindl | André Kestler | Ulrich Schäfer | Bernd Ludwig

Large Language Models (LLMs) have recently been studied within the context of Task-Oriented Dialogues (TOD). However, previous research is inconclusive on their effectiveness, with some studies claiming that LLMs are unable to perform the TOD task and others making sophisticated additions to their setup and coming to opposite conclusions. In this work, we take a detailed look at previous results that state LLMs perform insufficiently as a TOD system. As a result, we propose an updated, stronger baseline for multiple out-of-the-box LLM performances as TOD systems. We introduce a Self-Checking mechanism as a simple, yet effective, component to drastically improve their performance. Our results show that newer, pre-trained LLMs can, in fact, perform as TOD systems out-of-the-box, challenging the previous understanding. We show that LLMs can even perform competitively to fine-tuned models in certain metrics. Based on this, we propose directions for future research. Our code is published on Github.

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MATCH: Task-Driven Code Evaluation through Contrastive Learning
Marah Ghoummaid | Vladimir Tchuiev | Ofek Glick | Michal Moshkovitz | Dotan Di Castro

AI-based code generation is increasingly prevalent, with GitHub Copilot estimated to generate 46% of the code on GitHub. Accurately evaluating how well generated code aligns with developer intent remains a critical challenge. Traditional evaluation methods, such as unit tests, are often unscalable and costly. Syntactic similarity metrics (e.g., BLEU, ROUGE) fail to capture code functionality, and metrics like CodeBERTScore require reference code, which is not always available. To address the gap in reference-free evaluation, with few alternatives such as ICE-Score, this paper introduces MATCH, a novel reference-free metric. MATCH uses Contrastive Learning to generate meaningful embeddings for code and natural language task descriptions, enabling similarity scoring that reflects how well generated code implements the task. We show that MATCH achieves stronger correlations with functional correctness and human preference than existing metrics across multiple programming languages.

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Evaluating Large Language Models for Cross-Lingual Retrieval
Longfei Zuo | Pingjun Hong | Oliver Kraus | Barbara Plank | Robert Litschko

Multi-stage information retrieval (IR) has become a widely-adopted paradigm in search. While Large Language Models (LLMs) have been extensively evaluated as second-stage reranking models for monolingual IR, a systematic large-scale comparison is still lacking for cross-lingual IR (CLIR). Moreover, while prior work shows that LLM-based rerankers improve CLIR performance, their evaluation setup relies on machine translation (MT) for the first stage. This is not only prohibitively expensive but also prone to error propagation across stages. Our evaluation on passage-level and document-level CLIR reveals that this setup, which we term noisy monolingual IR, is favorable for LLMs. However, LLMs still fail to improve the first-stage ranking if instead produced by multilingual bi-encoders. We further show that pairwise rerankers based on instruction-tuned LLMs perform competitively with listwise rerankers. To the best of our knowledge, we are the first to study the interaction between retrievers and rerankers in two-stage CLIR with LLMs. Our findings reveal that, without MT, current state-of-the-art rerankers fall severely short when directly applied in CLIR.

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SGCD: Subtask-Guided Causal-Debiasing Framework for Robust Cross-Utterance Sentiment Quadruple Extraction in Dialogues
Xiang Li | Keyu Yao | Gang Shen

The rise of digital social media has generated a vast amount of conversational data on platforms like Twitter and Reddit, allowing users to express sentiments through multi-turn dialogues. Dialogue-level aspect-based sentiment quadruple analysis (DiaASQ) seeks to extract structured information in the form of quadruples from these dialogues. However, it encounters challenges related to cross-utterance elements and focus bias. To address these issues, we introduce the Subtask-Guided and Causal-Debiasing (SGCD) framework. This framework leverages subtask-specific features to guide the learning of token-level features, which are then adaptively combined at the utterance level to meet specific semantic requirements. The SGCD framework employs multi-granularity attention paths to enhance cross-utterance matching and dialogue structure modeling. It also incorporates structural causal graphs and inverse probability weighting to mitigate biases from speakers and thread structures. Experimental results demonstrate that SGCD outperforms state-of-the-art methods, improving semantic modeling and bias robustness. This approach provides an effective solution for structured sentiment analysis in complex dialogues.

In this paper, we introduce a comprehensive benchmark for Persian (Farsi) text embeddings, built upon the Massive Text Embedding Benchmark (MTEB). Our benchmark includes 63 datasets spanning seven different tasks: classification, clustering, pair classification, reranking, retrieval, summary retrieval, and semantic textual similarity. The datasets are a combination of existing, translated, and newly generated (synthetic) data, offering a diverse and robust evaluation framework for Persian language models. All newly translated and synthetic datasets were rigorously evaluated by both humans and automated systems to ensure high quality and reliability. Given the growing adoption of text embedding models in chatbots, evaluation datasets are becoming an essential component of chatbot development and Retrieval-Augmented Generation (RAG) systems. As a contribution, we include chatbot evaluation datasets in the MTEB benchmark for the first time. Additionally, we introduce the novel task of summary retrieval, which is not included in the standard MTEB tasks. Another key contribution of this work is the introduction of a substantial number of new Persian-language NLP datasets for both training and evaluation, many of which have no existing counterparts in Persian. We evaluate the performance of several Persian and multilingual embedding models across a wide range of tasks. This work presents an open-source benchmark with datasets, accompanying code, and a public leaderboard.

Limited low-resource language corpora in professional domains like medicine hinder cross-lingual domain adaptation of pre-trained large language models (PLMs). While abundant English medical corpora could complement this scarcity, the effective mixture of English and target language, including machine-translated content, remains underexplored. We examined how linguistic features (e.g., token sizes and language proportions) affect performance on a Japanese–English medical knowledge benchmark. Through continued pre-training of a bilingual PLM on multilingual corpora with varying proportions of English and Japanese texts (both original and machine-translated), we analyzed correlations between linguistic features and fine-grained task performance. Our findings suggest a practical approach to optimizing multilingual corpora for cross-lingual domain adaptation, which requires leveraging specialized knowledge from English corpora while ensuring sufficient coverage of language-specific expressions in a target language (Japanese). Such insights will contribute to the development of multilingual models that effectively leverage English-language resources in various professional domains with low-resource languages.

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Structure Trumps Size: Rethinking Data Quality for LLM Reasoning
Hu Xu | Zeyan Li | Rui Wang | Jianfeng Xu

As domain-specific datasets continue to expand, Large Language Models (LLMs) have achieved significant improvements across various fields through supervised fine-tuning (SFT). However, is more data always better for model fine-tuning? Through a series of controlled experiments, we discover that dataset structure—rather than mere size—plays a decisive role in enhancing LLM reasoning capabilities. While existing methods acknowledge that good data quality can make training more efficient, they primarily rely on simple heuristic strategies and lack systematic, quantitative frameworks for evaluating data quality. To address this gap, we introduce MCSQ—the first multi-dimensional quantitative framework for reasoning data management. MCSQ rigorously evaluates and optimizes datasets along six orthogonal dimensions. Through comprehensive controlled experiments, we find that selectively incorporating “distorted” (model-disagreed) or “mismatched” (low-relevance) samples—which are typically discarded in traditional approaches—can outperform conventional “clean” data on certain advanced reasoning benchmarks. Our findings challenge traditional assumptions about data “quality” in LLM fine-tuning and provide actionable, quantitative guidance for efficient, structure-aware dataset management. The datasets and codes are both available at https://github.com/xuhu0115/MCSQ.

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A Zero-Shot Neuro-Symbolic Approach for Complex Knowledge Graph Question Answering
Prerna Agarwal | Srikanta Bedathur

Existing low-resource Knowledge Graph Question Answering (KGQA) methods rely heavily on Large Language Models (LLMs) for semantic parsing of natural language question to its corresponding logical form (LF) such as SPARQL, S-Expression, etc. However, LLMs becomes bottleneck for practical applications due to: (i) its high computational resource requirements; (2) limited knowledge of LLM about different LFs; (3) unavailability of low-resource annotated data for new KGs and settings. This motivates us to design a KGQA framework that can operate in a zero-shot setting without the need for additional resources. In this paper, we propose (NS-KGQA): a zero-shot neuro-symbolic approach based on neural KG embeddings that have demonstrated their ability to effectively model KG structure without the need of additional data. We extract a link-prediction based symbolic question subgraph. We then propose a Symbolic Resolver that uses Dual KG Embeddings combined with a symbolic approach to resolve the symbolic question subgraph. Our extensive experiments on Complex KGQA benchmarks such as KQA Pro demonstrate the effectiveness of our approach. NS-KGQA outperforms all other LLM-based zero-shot baselines by 26% (avg).

In the realm of large vision-language models (LVLMs), adversarial jailbreak attacks serve as a red-teaming approach to identify safety vulnerabilities of these models and their associated defense mechanisms. However, we identify a critical limitation: not every adversarial optimization step leads to a positive outcome, and indiscriminately accepting optimization results at each step may reduce the overall attack success rate. To address this challenge, we introduce HKVE (Hierarchical Key-Value Equalization), an innovative jailbreaking framework that selectively accepts gradient optimization results based on the distribution of attention scores across different layers, ensuring that every optimization step positively contributes to the attack. Extensive experiments demonstrate HKVE’s significant effectiveness, achieving attack success rates of 75.08% on MiniGPT4, 85.84% on LLaVA and 81.00% on Qwen-VL, substantially outperforming existing methods by margins of 20.43%, 21.01% and 26.43% respectively. Furthermore, making every step effective not only leads to an increase in attack success rate but also allows for a reduction in the number of iterations, thereby lowering computational costs.

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MT-Mol: Multi Agent System with Tool-based Reasoning for Molecular Optimization
Hyomin Kim | Yunhui Jang | Sungsoo Ahn

Large language models (LLMs) have large potential for molecular optimization, as they can gather external chemistry tools and enable collaborative interactions to iteratively refine molecular candidates. However, this potential remains underexplored, particularly in the context of structured reasoning, interpretability, and comprehensive tool-grounded molecular optimization. To address this gap, we introduce MT-Mol, a multi-agent framework for molecular optimization that leverages tool-guided reasoning and role-specialized LLM agents. Our system incorporates comprehensive RDKit tools, categorized into five distinct domains: structural descriptors, electronic and topological features, fragment-based functional groups, molecular representations, and miscellaneous chemical properties. Each category is managed by an expert analyst agent, responsible for extracting task-relevant tools and enabling interpretable, chemically grounded feedback. MT-Mol produces molecules with tool-aligned and stepwise reasoning through the interaction between the analyst agents, a molecule-generating scientist, a reasoning-output verifier, and a reviewer agent. As a result, we show that our framework shows the state-of-the-art performance of the PMO-1K benchmark on 15 out of 23 tasks and outperforms LLM baselines on ChemCoTBench benchmark.

Recently, Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation, prompting the recommendation community to leverage these powerful models to address fundamental challenges in traditional recommender systems, including limited comprehension of complex user intents, insufficient interaction capabilities, and inadequate recommendation interpretability. This survey presents a comprehensive synthesis of this rapidly evolving field. We consolidate existing studies into three paradigms: (i) recommender-oriented methods, which directly enhance core recommendation mechanisms; (ii) interaction-oriented methods, which conduct multi-turn conversations to elicit preferences and deliver interpretable explanations; and (iii) simulation-oriented methods, that model user-item interactions through multi-agent frameworks. Then, we dissect a four-module agent architecture: profile, memory, planning, and action. Then we review representative designs, public datasets, and evaluation protocols. Finally, we give the open challenges that impede real-world deployment, including cost-efficient inference, robust evaluation, and security.

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Efficiently Selecting Response Generation Strategies for Synthetic Data Construction by Self-Aligned Perplexity
Xuan Ren | Qi Chen | Lingqiao Liu

Fine-tuning large language models (LLMs) typically relies on producing large sets of input-output pairs. Yet for a given question, there can be many valid outputs. In practice, these outputs are often derived by distilling knowledge from teacher models, and they can vary depending on the specific teacher model or prompting strategy employed.Recent findings show that how these training outputs are generated can significantly affect the performance of the fine-tuned model, raising an important question: how do we pick the best data generation method from among numerous possibilities? Rather than exhaustively training and evaluating on each candidate, this paper proposes a scalable approximate method that assesses a small subset of generated data to estimate its suitability for a specific target LLM. Our central idea is that effective outputs should be familiar to the target LLM. While previous work measures familiarity with perplexity, we find that perplexity might be suboptimal in characterizing “familiarity” through empirical analyses and practical observations. To address this, we introduce self-aligned perplexity, a novel metric capturing how closely candidate outputs adhere to the target LLM’s own style and reasoning patterns. In this way, we can identify the most effective generation strategy on a small sample, then apply it to produce the complete training set. We demonstrate that training on data generated by the chosen method yields significant improvements across diverse reasoning-focused benchmarks, particularly in cases where different candidate methods lead to highly divergent training outcomes.

The increasing demand for domain-specific evaluation of large language models (LLMs) has led to the development of numerous benchmarks. These efforts often adhere to the principle of data scaling, relying on large corpora or extensive question-answer (QA) sets to ensure broad coverage. However, the impact of corpus and QA set design on the precision and recall of domain-specific LLM performance remains poorly understood. In this paper, we argue that data scaling is not always the optimal principle for domain-specific benchmark construction. Instead, we introduce Comp-Comp, an iterative benchmarking framework grounded in the principle of comprehensiveness and compactness. Comprehensiveness ensures semantic recall by covering the full breadth of the domain, while compactness improves precision by reducing redundancy and noise. To demonstrate the effectiveness of our approach, we present a case study conducted at a well-renowned university, resulting in the creation of PolyBench, a large-scale, high-quality academic benchmark. Although this study focuses on academia, the Comp-Comp framework is domain-agnostic and readily adaptable to a wide range of specialized fields. The source code and datasets can be accessed at https://github.com/Anya-RB-Chen/COMP-COMP.

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FrameEOL: Semantic Frame Induction using Causal Language Models
Chihiro Yano | Kosuke Yamada | Hayato Tsukagoshi | Ryohei Sasano | Koichi Takeda

Semantic frame induction is the task of clustering frame-evoking words according to the semantic frames they evoke. In recent years, leveraging embeddings of frame-evoking words that are obtained using masked language models (MLMs) such as BERT has led to high-performance semantic frame induction. Although causal language models (CLMs) such as the GPT and Llama series succeed in a wide range of language comprehension tasks and can engage in dialogue as if they understood frames, they have not yet been applied to semantic frame induction. We propose a new method for semantic frame induction based on CLMs. Specifically, we introduce FrameEOL, a prompt-based method for obtaining Frame Embeddings that outputs One frame-name as a Label representing the given situation. To obtain embeddings more suitable for frame induction, we leverage in-context learning (ICL) and deep metric learning (DML). Frame induction is then performed by clustering the resulting embeddings. Experimental results on the English and Japanese FrameNet datasets demonstrate that the proposed methods outperform existing frame induction methods. In particular, for Japanese, which lacks extensive frame resources, the CLM-based method using only 5 ICL examples achieved comparable performance to the MLM-based method fine-tuned with DML.

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CaTER: A Framework for Context-aware Topology Entity Retrieval Contrastive Learning in End-to-End Task-Oriented Dialogue Systems
Di Wu Hebeu | Zhizhi Yu

Retrieving entity knowledge that aligns with user intent is essential for task-oriented dialogue (TOD) systems to support personalization and localization, especially under large-scale knowledge bases. However, generative models tend to suffer from implicit association preference, while retrieval-generation approaches face knowledge transfer discrepancies. To address these challenges, we propose CaTER, a Context-aware Topology Entity Retrieval Contrastive Learning Framework. CaTER introduces a cycle context-aware distilling attention mechanism, which employs context-independent sparse pooling to suppress noise from weakly relevant attributes. We further construct topologically hard negative samples by decoupling entity information from generated responses and design a topology entity retrieval contrastive loss to train the retriever by reverse distillation. Extensive experiments on three standard TOD benchmarks with both small and large-scale knowledge bases show that CaTER consistently outperforms strong baselines such as MAKER and MK-TOD, achieving state-of-the-art performance in TOD system.

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Attribution and Application of Multiple Neurons in Multimodal Large Language Models
Feiyu Wang | Ziran Zhao | Dong Yu | Pengyuan Liu

Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance across various tasks. However, the internal mechanisms by which they interpret and integrate cross-modal information remain insufficiently understood. In this paper, to address the limitations of prior studies that could only identify neurons corresponding to single-token and rely on the vocabulary of LLMs, we propose a novel method to identify multimodal neurons in Transformer-based MLLMs. Then we introduce fuzzy set theory to model the complex relationship between neurons and semantic concepts and to characterize how multiple neurons collaboratively contribute to semantic concepts. Through both theoretical analysis and empirical validation, we demonstrate the effectiveness of our method and present some meaningful findings. Furthermore, by modulating neuron activation values based on the constructed fuzzy sets, we enhance performance on the Visual Question Answering (VQA) task, showing the practical value of our approach in downstream applications in MLLMs.

Hallucination detection remains a fundamental challenge for the safe and reliable deployment of large language models (LLMs), especially in applications requiring factual accuracy. Existing hallucination benchmarks often operate at the sequence level and are limited to English, lacking the fine-grained, multilingual supervision needed for comprehensive evaluation. In this work, we introduce PsiloQA, a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languages. PsiloQA is constructed through an automated three-stage pipeline: generating question–answer pairs from Wikipedia using GPT-4o, eliciting potentially hallucinated answers from diverse LLMs in a no-context setting, and automatically annotating hallucinated spans using GPT-4o by comparing against golden answers and retrieved context. We evaluate a wide range of hallucination detection methods-including uncertainty quantification, LLM-based tagging, and fine-tuned encoder models-and show that encoder-based models achieve the strongest performance across languages. Furthermore, PsiloQA demonstrates effective cross-lingual generalization and supports robust knowledge transfer to other benchmarks, all while being significantly more cost-efficient than human-annotated datasets. Our dataset and results advance the development of scalable, fine-grained hallucination detection in multilingual settings.

Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, positioning them as promising tools for supporting human problem-solving. However, what happens when their performance is affected by *misinformation*, i.e., incorrect inputs introduced by users due to oversights or gaps in knowledge? Such misinformation is prevalent in real-world interactions with LLMs, yet how it propagates within LLMs’ reasoning process remains underexplored. Focusing on mathematical reasoning, we present a comprehensive analysis of how misinformation affects intermediate reasoning steps and final answers. We also examine how effectively LLMs can correct misinformation when explicitly instructed to do so. Even with explicit instructions, LLMs succeed less than half the time in rectifyingmisinformation, despite possessing correct internal knowledge, leading to significant accuracy drops (10.02% – 72.20%), and the degradation holds with thinking models (4.30% – 19.97%). Further analysis shows that applying factual corrections early in the reasoning process most effectively reduces misinformation propagation, and fine-tuning on synthesized data with early-stage corrections significantly improves reasoning factuality. Our work offers a practical approach to mitigating misinformation propagation.

Instruction tuning of large language models (LLMs) benefits more from a handful of high-quality examples than from hordes of low-quality ones. Existing selection methods typically rely on static, heuristic quality scores and are executed only once before training. Consequently, they neither adapt to the changing state of the model nor target downstream objectives, leaving substantial room for optimization. We propose RAISE (**R**einforced **A**daptive **I**nstruction **SE**lection), a *dynamic*, *task-driven* framework that integrates selection into every training step. At each step, RAISE estimates the expected contribution of each candidate instruction to task performance and admits only the most helpful. By modeling this process as sequential decision making, we optimize the selector with reinforcement learning, yielding an interpretable policy specialized for the target task. Extensive experiments show that RAISE reaches comparable or better results than full-data training while updating only 1% of the steps, demonstrating both high efficacy and significant computational savings.

Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data organization strategy has shown preliminary effectiveness in instruction tuning. However, current curriculum tuning methods suffer from the curriculum rigidity, since they rely solely on static heuristic difficulty metrics. These methods fail to adapt to the evolving capabilities of models during training, resulting in a fixed and potentially sub-optimal learning trajectory. To address the issue, **C**ompetence-**A**ware **M**ulti-**P**erspective c**U**rriculum in**S**truction tuning framework termed **CAMPUS** is proposed. CAMPUS offers several advantages: (1) Dynamic selection for sub-curriculum. (2) Competency-aware adjustment to the curriculum schedule. (3) Multiple difficulty-based scheduling. Extensive experiments prove the superior performance of CAMPUS, compared to other state-of-the-art baselines for efficient instruction tuning.

Current LLMs are trained to refuse potentially harmful input queries regardless of whether users actually had harmful intents, causing a tradeoff between safety and user experience. Through a study of 480 participants evaluating 3,840 query-response pairs, we examine how different refusal strategies affect user perceptions across varying motivations. Our findings reveal that response strategy largely shapes user experience, while actual user motivation has negligible impact. Partial compliance—providing general information without actionable details—emerges as the optimal strategy, reducing negative user perceptions by over 50% to flat-out refusals. Complementing this, we analyze response patterns of 9 state-of-the-art LLMs and evaluate how 6 reward models score different refusal strategies, demonstrating that models rarely deploy partial compliance naturally and reward models currently undervalue it. This work demonstrates that effective guardrails require focusing on crafting thoughtful refusals rather than detecting intent, offering a path toward AI safety mechanisms that ensure both safety and sustained user engagement.

Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research.

The primary goal of traditional federated learning is to protect data privacy by enabling distributed edge devices to collaboratively train a shared global model while keeping raw data decentralized at local clients. The rise of large language models (LLMs) has introduced new challenges in distributed systems, as their substantial computational requirements and the need for specialized expertise raise critical concerns about protecting intellectual property (IP). This highlights the need for a federated learning approach that can safeguard both sensitive data and proprietary models. To tackle this challenge, we propose FedQSN, a federated learning approach that leverages random masking to obscure a subnetwork of model parameters and applies quantization to the remaining parameters. Consequently, the server transmits only a privacy-preserving proxy of the global model to clients during each communication round, thus enhancing the model’s confidentiality. Experimental results across various models and tasks demonstrate that our approach not only maintains strong model performance in federated learning settings but also achieves enhanced protection of model parameters compared to baseline methods.

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SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning
Mingsheng Cai | Jiuming Jiang | Wenhao Huang | Che Liu | Rossella Arcucci

Cardiovascular diseases are a leading cause of death and disability worldwide. Electrocardiogram (ECG) is critical for diagnosing and monitoring cardiac health, but obtaining large-scale annotated ECG datasets is labor-intensive and time-consuming. Recent ECG Self-Supervised Learning (eSSL) methods mitigate this by learning features without extensive labels but fail to capture fine-grained clinical semantics and require extensive task-specific fine-tuning. To address these challenges, we propose SuPreME, a Supervised Pre-training framework for Multimodal ECG representation learning. SuPreME is pre-trained using structured diagnostic labels derived from ECG report entities through a one-time offline extraction with Large Language Models (LLMs), which help denoise, standardize cardiac concepts, and improve clinical representation learning. By fusing ECG signals with textual cardiac queries instead of fixed labels, SuPreME enables zero-shot classification of unseen conditions without further fine-tuning. We evaluate SuPreME on six downstream datasets covering 106 cardiac conditions, achieving superior zero-shot AUC performance of 77.20%, surpassing state-of-the-art eSSLs by 4.98%. Results demonstrate SuPreME’s effectiveness in leveraging structured, clinically relevant knowledge for high-quality ECG representations.

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Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique
Tej Deep Pala | Vernon Toh | Rishabh Bhardwaj | Soujanya Poria

As large language models (LLMs) are increasingly integrated into real-world applications, ensuring their safety and robustness is critical. Automated red-teaming methods generate adversarial attacks to identify vulnerabilities, but existing approaches often face challenges like slow performance, limited categorical diversity, and high resource demands. We propose Ferret, a novel method that enhances the baseline, Rainbow Teaming by generating multiple adversarial prompt mutations per iteration and ranking them using scoring functions such as reward models, Llama Guard, and LLM-as-a-judge. Ferret achieves a 95% attack success rate (ASR), a 46% improvement over baseline, and reduces time to a 90% ASR by 15.2%. Additionally, it generates transferable adversarial prompts effective on larger LLMs. Our code is available at https://github.com/declare-lab/ferret

Recently, Vision-Language-Action (VLA) models have demonstrated strong performance on a range of robotic tasks. These models rely on multimodal inputs, with language instructions playing a crucial role-not only in predicting actions, but also in robustly interpreting user intent, even when the requests are impossible to fulfill. In this work, we investigate how VLAs can recognize, interpret, and respond to false-premise instructions-natural language commands that reference objects or conditions absent from the environment. We propose — Instruct-Verify-and-Act (IVA) — a unified framework that (i) detects when an instruction cannot be executed due to a false premise, (ii) engages in language-based clarification or correction, and (iii) grounds plausible alternatives in perception and action. Towards this end, we construct a large-scale instruction tuning setup with structured language prompts and train a VLA model capable of handling both accurate and erroneous requests. Our approach leverages a contextually augmented, semi-synthetic dataset containing paired positive and false-premise instructions, enabling robust detection and natural language correction. Our experiments show that IVA can improves false premise detection accuracy by 58.89% over baselines, while increasing successful responses in false-premise scenarios by 27.89%.

Proper initialization is crucial for any system, particularly in multi-agent systems (MAS), where it plays a pivotal role in determining both the system’s efficiency and effectiveness. However, existing MAS initialization methods do not fully account for the collaborative needs of the generated agents in subsequent stages. Inspired by the principles of effective team composition, we propose , which aims to optimize the structure of agent teams. Specifically, in addition to multi-round interactions and reflections between agents during agent generation, AgentInit incorporates a Natural Language to Format mechanism to ensure consistency and standardization. Balanced team selection strategies using Pareto principles are subsequently applied to jointly consider agent team diversity and task relevance to promote effective and efficient collaboration and enhance overall system performance. Experiments show that AgentInit consistently outperforms state-of-the-art initialization methods and pre-defined strategies across various frameworks and tasks, achieving an overall performance improvement of up to 1.2 and 1.7, respectively, while also significantly reducing token consumption. Further analysis confirms its strong transferability to similar tasks and verifies the effectiveness of its key components, demonstrating its capability and adaptability as a reliable MAS initialization method. Source code and models are available at https://github.com/1737423697/AgentInit.

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Time to Revisit Exact Match
Auss Abbood | Zaiqiao Meng | Nigel Collier

Temporal question answering is an established method for evaluating temporal reasoning in large language models. Expected answers are often numeric (e.g., dates or durations), yet model responses are evaluated like regular text with exact match (EM), unable to distinguish small from large errors. In this investigative work, we frame temporal question answering as a numerical estimation task to assess the shortcomings of EM. We introduce TempAnswerQA, a benchmark distilled from Test of Time and TempTabQA, where all questions require a numerical, temporal answer, allowing us to evaluate models beyond EM. We use the forecasting metrics symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE). With sMAPE, we find that error size and EM are decoupled. Models with low EM still have low sMAPE (both 20%), and some models have high sMAPE despite high EM. Scaling errors by the deviation of the ground truth data with MASE reshuffles model rankings compared to EM, revealing gaps in models’ understanding of temporal domain knowledge, especially when trained with synthetic data. Lastly, the models’ most frequent error is to deviate by only ±1 from the ground truth. sMAPE and MASE, unlike EM, adequately weight these errors. Our findings underscore the need for specialised metrics for temporal QA tasks. Our code and data are available on https://github.com/aauss/temporal-answer-qa.

We introduce **LongTableBench**, a benchmark for evaluating long-context reasoning over semi-structured tables across diverse formats, tasks, and domains. It comprises 5,950 QA instances spanning 7 table formats (e.g., Markdown, HTML, SQL), 18 domains, and input lengths up to 128K tokens, including multi-turn and multi-table settings. To ensure data quality, we combine symbolic supervision, cross-model validation, and human review. Evaluating 52 LLMs—including general-purpose, table-specific, and reasoning-enhanced models—reveals that only the strongest models maintain robust performance under increasing context lengths and format diversity. We further show that end-to-end models outperform compression-based approaches, especially on tasks requiring semantic integration. LongTableBench provides a rigorous, scalable testbed for advancing long-context tabular understanding and highlights key limitations in current LLMs’ structural and reasoning capabilities.

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Exploring and Evaluating Multimodal Knowledge Reasoning Consistency of Multimodal Large Language Models
Boyu Jia | Junzhe Zhang | Huixuan Zhang | Xiaojun Wan

In recent years, multimodal large language models (MLLMs) have achieved significant breakthroughs, enhancing understanding across text and vision. However, current MLLMs still face challenges in effectively integrating knowledge across these modalities during multimodal knowledge reasoning, leading to inconsistencies in reasoning outcomes. To systematically explore this issue, we propose four evaluation tasks and construct a new dataset. We conduct a series of experiments on this dataset to analyze and compare the extent of consistency degradation in multimodal knowledge reasoning within MLLMs. Based on the experimental results, we identify factors contributing to the observed degradation in consistency. Our research provides new insights into the challenges of multimodal knowledge reasoning and offers valuable guidance for future efforts aimed at improving MLLMs.

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MPTA: MultiTask Personalization Assessment
Matthieu Tehenan | Eric Chamoun | Andreas Vlachos

Large language models are increasingly expected to adapt to individual users, reflecting differences in preferences, values, and communication styles. To evaluate whether models can serve diverse populations, we introduce MTPA, a benchmark that leverages large-scale survey data (WVS, EVS, GSS) to construct real, hyper-granular personas spanning demographics, beliefs, and values. Unlike prior benchmarks that rely on synthetic profiles or narrow trait prediction, MTPA conditions models on real personas and systematically tests their behavior across core alignment tasks. We show that persona conditioning exposes pluralistic misalignment: while aggregate metrics suggest models are truthful and safe, subgroup-specific evaluations reveal hidden pockets of degraded factuality, fairness disparities, and inconsistent value alignment. Alongside the benchmark, we release a dataset, toolkit, and baseline evaluations. MTPA is designed with extensibility and sustainability in mind: as the underlying survey datasets are regularly updated, MTPA supports regular integration of new populations and user traits.

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Semantic Geometry of Sentence Embeddings
Matthieu Tehenan

Sentence embeddings are central to modern natural language processing, powering tasks such as clustering, semantic search, and retrieval-augmented generation. Yet, they remain largely opaque: their internal features are not directly interpretable, and users lack fine-grained control for downstream tasks. To address this issue, we introduce a formal framework to characterize the organization of features in sentence embeddings through information-theoretic means. Building on this foundation, we develop a method to identify interpretable feature directions and show how they can be composed to capture richer semantic structures. Experiments on both synthetic and real-world datasets confirm the presence of this semantic geometry and highlight the utility of our approach for enhancing interpretability and fine-grained control in sentence embeddings.

Aligning small language models with human preferences is challenging, as weak policies struggle to generate informative on-policy samples and suffer from unstable gradients when trained on off-policy signals from stronger models. In this work, we propose ReAlign, a training framework that combines the stability of on-policy learning with the guidance of reviser-assisted supervision. In the ReAlign, we first train a lightweight reviser to improve policy-generated responses using preference-based supervision, conditioned on both the prompt and the initial output. And then, the policy is optimized using a combination of standard on-policy preference pairs and reviser-enhanced pairs constructed as a structured revision task, where the latter provide richer, more learnable feedback. Experimental results on AlpacaEval-2 and Arena-Hard demonstrate that ReAlign significantly boosts alignment performance for weak policies, outperforming strong preference optimization baselines.

State-of-the-art vision-language models (VLMs) require massive scaling that limits practical deployment. Small-scale VLMs offer a practical alternative but face out-of-domain (OOD) collapse when trained with traditional supervised fine-tuning (SFT). Through GeneralPoints experiments, we identify that OOD collapse is due to SFT’s tendency to induce visual hallucinations under distribution shifts, whereas Reinforcement Learning’s (RL) bidirectional reward-driven mechanism with iterative error correction refines visual perception. Although RL-based post-training effectively mitigates OOD degradation, it faces a critical sparse reward dilemma in complex visual reasoning tasks. To this end, we propose Curriculum Reinforcement Finetuning (Curr-ReFT), comprising two sequential stages: (1) Structured Curriculum Reinforcement Learning, which progressively evolves task formats and reward functions to match models’ growing capabilities; and (2) Rejected Sampling-based Self-improvement, which maintains the fundamental capabilities of VLMs through selective learning from high-quality examples. Extensive experiments demonstrate that Curr-ReFT achieves state-of-the-art performance across various visual tasks in both in- and out-of-domain settings and benchmarks.

Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine-tuned models often leads to degraded performance due to overlapping instruction-following components. Task Arithmetic (TA), which combines task vectors derived from fine-tuning, enables multi-task learning and task forgetting but struggles to isolate task-specific knowledge from general instruction-following behavior. To address this, we propose Layer-Aware Task Arithmetic (LATA), a novel approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. By amplifying task-relevant layers and attenuating instruction-following layers, LATA improves task learning and forgetting performance while preserving overall model utility. Experiments on multiple benchmarks, including WikiText-2, GSM8K, and HumanEval, demonstrate that LATA outperforms existing methods in both multi-task learning and selective task forgetting, achieving higher task accuracy and alignment with minimal degradation in output quality. Our findings highlight the importance of layer-wise analysis in disentangling task-specific and general-purpose knowledge, offering a robust framework for efficient model merging and editing.

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Revisiting Pruning vs Quantization for Small Language Models
Zihan Zhou | Simon Kurz | Zhixue Zhao

Deploying language models on resource-constrained devices, such as mobile phones, wearables, and on-device AI assistants, demands compact, efficient models without sacrificing performance. Compressing Small Language Models (SLMs) is particularly suited for these scenarios, yet their compression dynamics remain underexplored compared to Large Language Models (LLMs). We systematically evaluate leading post-training pruning (SparseGPT, Wanda) and quantization (GPTQ, AWQ) methods across six SLMs from 0.5 to 3.8B, seven languages, and seven downstream tasks. Our results show that quantization consistently outperforms pruning in preserving model fidelity, multilingual perplexity, and reasoning accuracy. However, quantization’s advantages diminish on complex knowledge and reasoning tasks like OpenBookQA, highlighting a disconnect between compression fidelity and downstream task performance. Notably, trends observed in LLMs (e.g., Wanda’s competitive performance to SparseGPT) do not generalize to SLMs. For practitioners, we recommend prioritizing quantization (particularly AWQ) for SLM compression and caution against relying on a single metric.

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CLaw: Benchmarking Chinese Legal Knowledge in Large Language Models - A Fine-grained Corpus and Reasoning Analysis
Xinzhe Xu | Liang Zhao | Hongshen Xu | Chenchenc

Large Language Models (LLMs) are increasingly tasked with analyzing legal texts and citing relevant statutes, yet their reliability is often compromised by general pre-training that ingests legal texts without specialized focus, obscuring the true depth of their legal knowledge. This paper introduces CLaw, a novel benchmark specifically engineered to meticulously evaluate LLMs on Chinese legal knowledge and its application in reasoning. CLaw comprises two key components: (1) a comprehensive, fine-grained corpus of all 306 Chinese national statutes, segmented to the subparagraph level and incorporating precise historical revision timesteps for rigorous recall evaluation (64,849 entries), and (2) a challenging set of 254 case-based reasoning instances derived from China Supreme Court curated materials to assess the practical application of legal knowledge. Our empirical evaluation reveals that most contemporary LLMs significantly struggle to faithfully reproduce legal provisions. As accurate retrieval and citation of legal provisions form the basis of legal reasoning, this deficiency critically undermines the reliability of their responses. We contend that achieving trustworthy legal reasoning in LLMs requires a robust synergy of accurate knowledge retrieval—potentially enhanced through supervised fine-tuning (SFT) or retrieval-augmented generation (RAG)—and strong general reasoning capabilities. This work provides an essential benchmark and critical insights for advancing domain-specific LLM reasoning, particularly within the complex legal sphere.

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polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Anagha Savit | Harikrishna Sahu | Shivank S. Shukla | Wei Xiong | Rampi Ramprasad

Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design space. Central to our approach is Pseudo-polymer SELFIES (PSELFIES), a novel representation that allows for the transfer of molecular language models to the polymer space. polyBART is, to the best of our knowledge, the first language model capable of bidirectional translation between polymer structures and properties, achieving state-of-the-art results in property prediction and design of novel polymers for electrostatic energy storage. Further, polyBART is validated through a combination of both computational and laboratory experiments. We report what we believe is the first successful synthesis and validation of a polymer designed by a language model, predicted to exhibit high thermal degradation temperature and confirmed by our laboratory measurements. Our work presents a generalizable strategy for adapting molecular language models to the polymer space and introduces a polymer foundation model, advancing generative polymer design that may be adapted for a variety of applications.

Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts. This survey synthesizes both strands under a unified reasoning-search perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and thought to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric.

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REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction
Omar Sharif | Joseph Gatto | Madhusudan Basak | Sarah Masud Preum

Event argument extraction identifies arguments for predefined event roles in text. Existing work evaluates this task with exact match (EM), where predicted arguments must align exactly with annotated spans. While suitable for span-based models, this approach falls short for large language models (LLMs), which often generate diverse yet semantically accurate arguments. EM severely underestimates performance by disregarding valid variations. Furthermore, EM evaluation fails to capture implicit arguments (unstated but inferable) and scattered arguments (distributed across a document). These limitations underscore the need for an evaluation framework that better captures models’ actual performance. To bridge this gap, we introduce REGen, a Reliable Evaluation framework for Generative event argument extraction. REGen combines the strengths of exact, relaxed, and LLM-based matching to better align with human judgment. Experiments on six datasets show that REGen reveals an average performance gain of +23.93 F1 over EM, reflecting capabilities overlooked by prior evaluation. Human validation further confirms REGen’s effectiveness, achieving 87.67% alignment with human assessments of argument correctness.

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Mitigating Interviewer Bias in Multimodal Depression Detection: An Approach with Adversarial Learning and Contextual Positional Encoding
Enshi Zhang | Christian Poellabauer

Clinical interviews are a standard method for assessing depression. Recent approaches have improved prediction accuracy by focusing on specific questions posed by the interviewer and manually selected question-answer (QA) pairs that target mental health content. However, these methods often neglect the broader conversational context, resulting in limited generalization and reduced robustness, particularly in less structured interviews, which are common in real-world clinical settings. In this work, we develop a multimodal dialogue-level transformer that captures the dynamics of dialogue within each interview by using a combination of sequential positional embedding and question context vectors. In addition to the depression prediction branch, we build an adversarial classifier with a gradient reversal layer to learn shared representations that remain invariant to the types of questions asked during the interview. This approach aims to reduce biased learning and improve the fairness and generalizability of depression detection in diverse clinical interview scenarios. Classification and regression experiments conducted on three real-world interview-based datasets and one synthetic dataset demonstrate the robustness and generalizability of our model.

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AMIA: Automatic Masking and Joint Intention Analysis Makes LVLMs Robust Jailbreak Defenders
Yuqi Zhang | Yuchun Miao | Zuchao Li | Liang Ding

We introduce AMIA, a lightweight, inference-only defense for Large Vision–Language Models (LVLMs) that (1) Automatically Masks a small set of text-irrelevant image patches to disrupt adversarial perturbations, and (2) conducts joint Intention Analysis to uncover and mitigate hidden harmful intents before response generation. Without any retraining, AMIA improves defense success rates across diverse LVLMs and jailbreak benchmarks from an average of 52.4% to 81.7%, preserves general utility with only a 2% average accuracy drop, and incurs only modest inference overhead. Ablation confirms that both masking and intention analysis are essential for robust safety–utility trade-off. Our code will be released.

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Disentangling Language Understanding and Reasoning Structures in Cross-lingual Chain-of-Thought Prompting
Khanh-Tung Tran | Nguyet-Hang Vu | Barry O’Sullivan | Hoang D. Nguyen

Cross-lingual chain-of-thought prompting techniques have proven effective for investigating diverse reasoning paths in Large Language Models (LLMs), especially for low-resource languages. Despite these empirical gains, the mechanisms underlying cross-lingual improvements remain perplexing. This study, therefore, addresses whether the benefits of cross-lingual prompting arise from language-specific reasoning structures intrinsic to each language, or are simply a consequence of improved comprehension through cross-linguistic exposure. We employ neuron intervention and perturbation techniques to analyze and deactivate language-specific reasoning neurons during cross-lingual prompting, leading to performance disparities across languages, up to 27.4%. Our findings disentangle that these neurons are essential for reasoning in their respective languages, but have minimal effect on reasoning in other languages, providing evidence for the existence of language-specific local reasoning structures and guiding the development of more interpretable and effective multilingual AI systems.

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MoRoVoc: A Large Dataset for Geographical Variation Identification of the Spoken Romanian Language
Andrei-Marius Avram | Bănescu Ema-Ioana | Anda-Teodora Robea | Dumitru-Clementin Cercel | Mihaela-Claudia Cercel

This paper introduces MoRoVoc, the largest dataset for analyzing the regional variation of spoken Romanian. It has more than 93 hours of audio and 88,192 audio samples, balanced between the Romanian language spoken in Romania and the Republic of Moldova. We further propose a multi-target adversarial training framework for speech models that incorporates demographic attributes (i.e., age and gender of the speakers) as adversarial targets, making models discriminative for primary tasks while remaining invariant to secondary attributes. The adversarial coefficients are dynamically adjusted via meta-learning to optimize performance. Our approach yields notable gains: Wav2Vec2-Base achieves 78.21% accuracy for the variation identification of spoken Romanian using gender as an adversarial target, while Wav2Vec2-Large reaches 93.08% accuracy for gender classification when employing both dialect and age as adversarial objectives.

Drawing real world social inferences usually requires taking into account information from multiple modalities. Language is a particularly powerful source of information in social settings, especially in novel situations where language can provide both abstract information about the environment dynamics and concrete specifics about an agent that cannot be easily visually observed. In this paper, we propose Language-Informed Rational Agent Synthesis (LIRAS), a framework for drawing context-specific social inferences that integrate linguistic and visual inputs. LIRAS frames multimodal social reasoning as a process of constructing structured but situation-specific agent and environment representations – leveraging multimodal language models to parse language and visual inputs into unified symbolic representations, over which a Bayesian inverse planning engine can be run to produce granular probabilistic judgments. On a range of existing and new social reasoning tasks derived from cognitive science experiments, we find that our model (instantiated with a comparatively lightweight VLM) outperforms ablations and state-of-the-art models in capturing human judgments across all domains.

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MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMs
Zaid Alyafeai | Maged S. Al-shaibani | Bernard Ghanem

Metadata extraction is essential for cataloging and preserving datasets, enabling effective research discovery and reproducibility, especially given the current exponential growth in scientific research. While Masader (CITATION) laid the groundwork for extracting a wide range of metadata attributes from Arabic NLP datasets’ scholarly articles, it relies heavily on manual annotation. In this paper, we present MOLE, a framework that leverages Large Language Models (LLMs) to automatically extract metadata attributes from scientific papers covering datasets of languages other than Arabic. Our schema-driven methodology processes entire documents across multiple input formats and incorporates robust validation mechanisms for consistent output. Additionally, we introduce a new benchmark to evaluate the research progress on this task. Through systematic analysis of context length, few-shot learning, and web browsing integration, we demonstrate that modern LLMs show promising results in automating this task, highlighting the need for further future work improvements to ensure consistent and reliable performance.

Speculative decoding significantly accelerates language model inference by enabling a lightweight draft model to propose multiple tokens that a larger target model verifies simultaneously. However, applying this technique to vision-language models (VLMs) presents two fundamental challenges: small language models that could serve as efficient drafters lack the architectural components to process visual inputs, and their token predictions fail to match those of VLM target models that consider visual context. We introduce Multimodal Adaptation and Self-Data Distillation for Speculative Decoding of Vision-Language Models (MASSV), which transforms existing small language models into effective multimodal drafters through a two-phase approach. MASSV first connects the target VLM’s vision encoder to the draft model via a lightweight trainable projector, then applies self-distilled visual instruction tuning using responses generated by the target VLM to align token predictions. Comprehensive experiments across the Qwen2.5-VL and Gemma3 model families demonstrate that MASSV increases accepted length by up to 30% and delivers end-to-end inference speedups of up to 1.46x compared to conventional text-only drafting baselines on visually-grounded tasks.

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FESTA: Functionally Equivalent Sampling for Trust Assessment of Multimodal LLMs
Debarpan Bhattacharya | Apoorva Kulkarni | Sriram Ganapathy

The accurate trust assessment of multimodal large language models (MLLMs) generated predictions, which can enable selective prediction and improve user confidence, is challenging due to the diverse multi-modal input paradigms. We propose Functionally Equivalent Sampling for Trust Assessment (FESTA), a multimodal input sampling technique for MLLMs, that generates an uncertainty measure based on the equivalent and complementary input samplings. The proposed task-preserving sampling approach for uncertainty quantification expands the input space to probe the consistency (through equivalent samples) and sensitivity (through complementary samples) of the model. FESTA uses only input-output access of the model (black-box), and does not require ground truth (unsupervised). The experiments are conducted with various off-the-shelf multi-modal LLMs, on both visual and audio reasoning tasks. The proposed FESTA uncertainty estimate achieves significant improvement (33.3% relative improvement for vision-LLMs and 29.6% relative improvement for audio-LLMs) in selective prediction performance, based on area-under-receiver-operating-characteristic curve (AUROC) metric in detecting mispredictions. The code implementation is open-sourced.

Legal claims refer to the plaintiff’s demands in a case and are essential to guiding judicial reasoning and case resolution. While many works have focused on improving the efficiency of legal professionals, the research on helping non-professionals (e.g., plaintiffs) remains unexplored. This paper explores the problem of legal claim generation based on the given case’s facts. First, we construct ClaimGen-CN, the first dataset for Chinese legal claim generation task, from various real-world legal disputes. Additionally, we design an evaluation metric tailored for assessing the generated claims, which encompasses two essential dimensions: factuality and clarity. Building on this, we conduct a comprehensive zero-shot evaluation of state-of-the-art general and legal-domain large language models. Our findings highlight the limitations of the current models in factual precision and expressive clarity, pointing to the need for more targeted development in this domain. To encourage further exploration of this important task, we will make the dataset publicly available.

Recent studies show the promise of large language models (LLMs) for few-shot tabular classification but highlight challenges due to the variability in structured data. To address this, we propose distilling data into actionable insights to enable robust and effective classification by LLMs. Drawing inspiration from human learning processes, we introduce InsightTab, an insight distillation framework guided by principles of divide-and-conquer, easy-first, and reflective learning. Our approach integrates rule summarization, strategic exemplification, and insight reflection through deep collaboration between LLMs and data modeling techniques. The obtained insights enable LLMs to better align their general knowledge and capabilities with the particular requirements of specific tabular tasks. We extensively evaluate InsightTab on nine datasets. The results demonstrate consistent improvement over state-of-the-art methods. Ablation studies further validate the principle-guided distillation process, while analyses emphasize InsightTab’s effectiveness in leveraging labeled data and managing bias.

Quantifying uncertainty in black-box LLMs is vital for reliable responses and scalable oversight. Existing methods, which gauge a model’s uncertainty through evaluating self-consistency in responses to the target query, can be misleading: an LLM may confidently provide an incorrect answer to a target query, yet give a confident and accurate answer to that same target query when answering a knowledge-preserving perturbation of the query. We systematically analyze the model behaviors and demonstrate that this discrepancy stems from suboptimal retrieval of parametric knowledge, often due to contextual biases that prevent consistent access to stored knowledge. We then introduce DiverseAgentEntropy, a novel, theoretically-grounded method employing multi-agent interaction across diverse query variations for uncertainty estimation of black-box LLMs. This approach more accurately assesses an LLM’s true uncertainty and improves hallucination detection, outperforming existing self-consistency based techniques.

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Stress-Testing the Reasoning Competence of Language Models With Formal Proofs
Konstantine Arkoudas | Serafim Batzoglou

We present a broad empirical study of state-of-the-art LLMs and LRMs (Large Reasoning Models) on ProofGrid, a new battery of challenging but tractable logical inference tasks that form a domain-independent test of constraint-based reasoning. The tasks include proof writing and proof checking across propositional and equational logic. We also introduce two novel tasks: proof inpainting and proof gap-filling. Solving these problems requires tracking the global structure of a mathematical argument, writing hierarchical subproofs, maintaining coherence across nested assumptions, performing complex case analyses, applying inference rules, reasoning about identity and term rewriting, and reasoning about proofs themselves. Our experiments reveal impressive performance by top-tier models but also systematic failure modes. Along with the benchmarks, we release a new data resource comprising over 10K formal deduction problems and corresponding proofs.

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Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization
Chuyuan Li | Austin Xu | Shafiq Joty | Giuseppe Carenini

A key challenge in Multi-Document Summarization (MDS) is effectively integrating information from multiple sources while maintaining coherence and topical relevance. While Large Language Models (LLMs) have shown impressive results in single-document summarization, their performance on MDS still leaves room for improvement. In this paper, we propose a topic-guided reinforcement learning approach to improve content selection in MDS. We first show that explicitly prompting models with topic labels enhances the informativeness. Building on this insight, we propose a novel topic reward within the Group Relative Policy Optimization (GRPO) framework to measure topic alignment between the generated summary and source documents. Experimental results on the Multi-News and Multi-XScience datasets demonstrate that our method consistently outperforms strong baselines, highlighting the effectiveness of leveraging topical cues in MDS.

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FACTCHECKMATE: Preemptively Detecting and Mitigating Hallucinations in LMs
Deema Alnuhait | Neeraja Kirtane | Muhammad Khalifa | Hao Peng

Language models (LMs) hallucinate. We inquire: Can we detect and mitigate hallucinations before they happen? This work answers this research question in the positive, by showing that the internal representations of LMs provide rich signals that can be used for this purpose. We introduce FactCheckmate, which preemptively detects hallucinations by learning a classifier that predicts whether the LM will hallucinate, based on the model’s hidden states produced over the inputs, before decoding begins. If a hallucination is detected, FactCheckmate then intervenes by adjusting the LM’s hidden states such that the model will produce more factual outputs. FactCheckmate provides fresh insights that the inner workings of LMs can be revealed by their hidden states. Practically, both its detection and mitigation models are lightweight, adding little inference overhead; FactCheckmate proves a more efficient approach for mitigating hallucinations compared to many post-hoc alternatives. We evaluate FactCheckmate over LMs of different scales and model families (including Llama, Mistral, Qwen and Gemma), across a variety of QA datasets from different domains. Our results demonstrate the effectiveness of FactCheckmate, achieving over 70% preemptive detection accuracy. On average, outputs generated by LMs with intervention are 34.4% more factual compared to those without.

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Dialectal Toxicity Detection: Evaluating LLM-as-a-Judge Consistency Across Language Varieties
Fahim Faisal | Md Mushfiqur Rahman | Antonios Anastasopoulos

There has been little systematic study on how dialectal differences affect toxicity detection by modern LLMs. Furthermore, although using LLMs as evaluators (“LLM-as-a-judge”) is a growing research area, their sensitivity to dialectal nuances is still underexplored and requires more focused attention. In this paper, we address these gaps through a comprehensive toxicity evaluation of LLMs across diverse dialects. We create a multi-dialect dataset through synthetic transformations and human-assisted translations, covering 10 language clusters and 60 varieties. We then evaluate five LLMs on their ability to assess toxicity, measuring multilingual, dialectal, and LLM-human consistency. Our findings show that LLMs are sensitive to both dialectal shifts and low-resource multilingual variation, though the most persistent challenge remains aligning their predictions with human judgments.

The biases exhibited by text-to-image (TTI) models are often treated as independent, though in reality, they may be deeply interrelated. Addressing bias along one dimension—such as ethnicity or age—can inadvertently affect another, like gender, either mitigating or exacerbating existing disparities. Understanding these interdependencies is crucial for designing fairer generative models, yet measuring such effects quantitatively remains a challenge. To address this, we introduce BiasConnect, a novel tool for analyzing and quantifying bias interactions in TTI models. BiasConnect uses counterfactual interventions along different bias axes to reveal the underlying structure of these interactions and estimates the effect of mitigating one bias axis on another. These estimates show strong correlation (+0.65) with observed post-mitigation outcomes.Building on BiasConnect, we propose InterMit, an intersectional bias mitigation algorithm guided by user-defined target distributions and priority weights. InterMit achieves lower bias (0.33 vs. 0.52) with fewer mitigation steps (2.38 vs. 3.15 average steps), and yields superior image quality compared to traditional techniques. Although our implementation is training-free, InterMit is modular and can be integrated with many existing debiasing approaches for TTI models, making it a flexible and extensible solution.

Large vision-language models (LVLMs) have demonstrated exceptional capabilities in understanding visual information with human languages but also exhibit an imbalance in multilingual capabilities. In this work, we delve into the multilingual working pattern of LVLMs and identify a salient correlation between the multilingual understanding ability of LVLMs and language-specific neuron activations in shallow layers. Building on this insight, we introduce PLAST, a training recipe that achieves efficient multilingual enhancement for LVLMs by Precise LAnguage Specific layers fine-Tuning. PLAST first identifies layers involved in multilingual understanding by monitoring language-specific neuron activations. These layers are then precisely fine-tuned with question-translation pairs to achieve multilingual alignment. Our empirical results on MMBench and MMMB demonstrate that PLAST effectively improves the multilingual capabilities of LVLMs and achieves significant efficiency with only 14% of the parameters tuned. Further analysis reveals that PLAST facilitates the language-specific visual information engagement in shallow layers.

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InfAL: Inference Time Adversarial Learning for Improving Research Ideation
Sikun Guo | Amir Hassan Shariatmadari | Peng Wang | Albert Huang | Aidong Zhang

Advancements in Large Language Models (LLMs) have opened new opportunities for scientific discovery by assisting researchers in generating novel hypotheses and ideas. In this process, a major challenge is how to optimally and efficiently utilize LLMs’ parametric knowledge obtained from their pretraining process. Inspired by Generative Adversarial Networks (GANs), we propose inference time adversarial learning (termed InfAL), implemented through multi-LLM-agent interactions, to enhance research ideation. This approach optimizes the utilization of LLMs’ parametric knowledge without requiring additional model training, making adversarial learning efficient and context-driven. To evaluate the quality of generated ideas, we propose a relative quality ranking metric as a scalable alternative to human evaluation. Our results show that InfAL significantly improves idea generation, with GPT-4o achieving a 21% increase in novelty and a 322% increase in feasibility, demonstrating its transformative potential for driving innovation in scientific research.

We propose a novel speculative decoding method tailored for multi-sample reasoning scenarios, such as self-consistency and Best-of-N sampling. Our method exploits the intrinsic consensus of parallel generation paths to synthesize high-quality draft tokens without requiring auxiliary models or external databases. By dynamically analyzing structural patterns across parallel reasoning paths through a probabilistic aggregation mechanism, it identifies consensus token sequences that align with the decoding distribution. Evaluations on mathematical reasoning and code generation benchmarks demonstrate a substantial improvement in draft acceptance rates over baselines, while reducing the latency in draft token construction. This work establishes a paradigm shift for efficient multi-sample inference, enabling seamless integration of speculative decoding with sampling-based reasoning techniques.

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LSRL: Process-Supervised GRPO on Latent Recurrent States Improves Mathematical Reasoning
Hangliang Ren

Latent-recurrent language models solve tasks by iteratively refining hidden states rather than emitting chain-of-thought tokens, yet the opacity of those hidden trajectories hinders credit assignment and limits mathematical reasoning accuracy. We propose Latent-State Supervised Reinforcement Learning (LSRL), a process-supervised variant of Guided Reward Policy Optimization (GRPO) that delivers dense rewards at every latent step. We decode each recurrent depth of a 3.5-billion-parameter Huginn model and score the partial solutions with a GPT-4.1-nano grader aligned to final-answer correctness. Using LoRA adapters, we update the policy on a single NVIDIA L40S GPU with only 500 GSM-8K training problems. Relative to the depth-8 supervised Huginn baseline, LSRL improves absolute accuracy by +4.27 points on GSM-8K and +2.06 points on MathQA. These results demonstrate that rewarding latent steps provides an efficient route to stronger mathematical reasoning in latent-recurrent language models.

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Multi-token Mask-filling and Implicit Discourse Relations
Meinan Liu | Yunfang Dong | Xixian Liao | Bonnie Webber

Previous work has shown that simple mask-filling can provide useful information about the discourse informativeness of syntactic structures. Dong et al. (2024) first adopted this approach to investigating preposing constructions. The problem with single token mask fillers was that they were, by and large, ambiguous. We address the issue by adapting the approach of Kalinsky et al. (2023) to support the prediction of multi-token connectives in masked positions. Our first experiment demonstrates that this multi-token mask-filling approach substantially outperforms the previously considered single-token approach in recognizing implicit discourse relations. Our second experiment corroborates previous findings, providing additional empirical support for the role of preposed syntactic constituents in signaling discourse coherence. Overall, our study extends existing mask-filling methods to a new discourse-level task and reinforces the linguistic hypothesis concerning the discourse informativeness of preposed structures.

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Schema Generation for Large Knowledge Graphs Using Large Language Models
Bohui Zhang | Yuan He | Lydia Pintscher | Albert Meroño-Peñuela | Elena Simperl

Schemas play a vital role in ensuring data quality and supporting usability in the Semantic Web and natural language processing. Traditionally, their creation demands substantial involvement from knowledge engineers and domain experts. Leveraging the impressive capabilities of large language models (LLMs) in tasks like ontology engineering, we explore schema generation using LLMs. To bridge the resource gap, we introduce two datasets: YAGO Schema and Wikidata EntitySchema, along with novel evaluation metrics. The LLM-based pipelines utilize local and global information from knowledge graphs (KGs) to generate schemas in Shape Expressions (ShEx). Experiments demonstrate LLMs’ strong potential in producing high-quality ShEx schemas, paving the way for scalable, automated schema generation for large KGs. Furthermore, our benchmark introduces a new challenge for structured generation, pushing the limits of LLMs on syntactically rich formalisms.

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MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree Search
Yunhai Hu | Yilun Zhao | Chen Zhao | Arman Cohan

We introduce MCTS-RAG, a novel approach that enhances the reasoning capabilities of small language models on knowledge-intensive tasks by leveraging retrieval-augmented generation (RAG) to provide relevant context and Monte Carlo Tree Search (MCTS) to refine reasoning paths. MCTS-RAG dynamically integrates retrieval and reasoning through an iterative decision-making process. Unlike standard RAG methods, which typically retrieve information independently from reasoning and thus integrate knowledge suboptimally, or conventional MCTS reasoning, which depends solely on internal model knowledge without external facts, MCTS-RAG combines structured reasoning with adaptive retrieval. This integrated approach enhances decision-making, reduces hallucinations, and ensures improved factual accuracy and response consistency. The experimental results on multiple reasoning and knowledge-intensive datasets datasets (ComplexWebQA, GPQA, and FoolMeTwice) show that our method enables small-scale LMs to achieve performance comparable to frontier LLMs like GPT-4o by effectively scaling inference-time compute, setting a new standard for reasoning in small-scale models.

Language models are often said to face a symbol grounding problem. While some have argued the problem can be solved without resort to other modalities, many have speculated that grounded learning is more efficient. We explore this question in Othello, a simplified, rule-based world that offers a controlled and interpretable testbed for studying world understanding. Building on prior work, we introduce VISOTHELLO, a multi-modal model trained jointly on move sequences and board images. Using the Othello rule understanding task, we examine whether multi-modal learning provides advantages over text-only approaches. We further evaluate robustness under semantically irrelevant perturbations and analyze the consistency of cross-modal alignment. Our results suggest that multi-modal training not only improves performance and robustness but also promotes convergence toward shared internal representations across different model architectures.

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LLM-Based Web Data Collection for Research Dataset Creation
Thomas Berkane | Marie-Laure Charpignon | Maimuna S. Majumder

Researchers across many fields rely on web data to gain new insights and validate methods. However, assembling accurate and comprehensive datasets typically requires manual review of numerous web pages to identify and record only those data points relevant to specific research objectives. The vast and scattered nature of online information makes this process time-consuming and prone to human error. To address these challenges, we present a human-in-the-loop framework that automates web-scale data collection end-to-end using large language models (LLMs). Given a textual description of a target dataset, our framework (1) automatically formulates search engine queries, (2) navigates the web to identify relevant web pages, (3) extracts the data points of interest, and (4) performs quality control to produce a structured, research-ready dataset. Importantly, users remain in the loop throughout the process and can inspect and adjust the framework’s decisions to ensure alignment with their needs. We introduce techniques to mitigate both search engine bias and LLM hallucinations during data extraction. Experiments across three diverse data collection tasks show that our framework greatly outperforms existing methods, while a user evaluation demonstrates its practical utility. We release our code at https://github.com/tberkane/web-data-collection to help other researchers create custom datasets more efficiently.

Over the years, online scams have grown dramatically,with nearly 50% of global consumersencountering scam attempts each week.These scams cause not only significant financiallosses to individuals and businesses, butalso lasting psychological trauma, largely dueto scammers’ strategic employment of psychologicaltechniques (PTs) to manipulate victims.Meanwhile, scammers continually evolve theirtactics by leveraging advances in Large LanguageModels (LLMs) to generate diverse scamvariants that easily bypass existing defenses.To address this pressing problem, we introducePsyScam, a benchmark designed to systematicallycapture the PTs employed in real-worldscam reports, and investigate how LLMs canbe utilized to generate variants of scams basedon the PTs and the contexts provided by thesescams. Specifically, we collect a wide range ofscam reports and ground its annotations of employedPTs in well-established cognitive andpsychological theories. We further demonstrateLLMs’ capabilities in generating through twodownstream tasks: scam completion, and scamaugmentation. Experimental results show thatPsyScam presents significant challenges toexisting models in both detecting and generatingscam content based on the PTs used byreal-world scammers. Our code and dataset areavailable.

As the parameter size of language models becomes extremely large, fine-tuning them with limited resources has become a challenging task. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques allow for adjustments to only a minor fraction of the parameters of these LLMs. Yet, most of PEFT methods may suffer from the following limitations: (1) As the rank decreases sharply, PEFT methods like LoRA and Adapter tuning will exhibit significant performance degradation in downstream tasks. (2) An accuracy gap between these methods and full fine-tuning (Full-FT) still exists. To tackle these problems, we propose a Low-Rank Direct Attention Adaptation (LoRaDA) method for efficient LLM fine-tuning. Specifically, we introduce a novel Low-rank Multi-head Attention Map Module (LMAM), which can bring negative attention to self-attention modules and learn low-rank attention weights directly, capturing the characteristics of downstream tasks. Furthermore, LMAM can serve as a plug-in to existing methods, such as LoRA and Adapter, providing state-of-the-art performance even with extreme low rank setting.Extensive experiments on various downstream tasks demonstrate the superior performance of our LoRaDA method. Specifically, LoRaDA even outperforms the full fine-tuning method by up to 2.1% on GLUE benchmark. As a plug-in, LMAM boosts the accuracy of LoRA by up to 27.7% with LLaMA-7B on Commonsense Reasoning benchmark.

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Inductive Reasoning on Few-Shot Knowledge Graphs with Task-Aware Language Models
Cheng Yan | Feng Zhao | Ruilin Zhao | Hong Zhang

Knowledge graphs are dynamic structures that continuously evolve as new entities emerge, often accompanied by only a handful of associated triples. Current knowledge graph reasoning methods struggle in these few-shot scenarios due to their reliance on extensive structural information.To address this limitation, we introduce ENGRAM, a novel approach that enables inductive reasoning on few-shot KGs by innovatively enriching the semantics from both textual and structural perspectives. Our key innovation lies in designing a task-aware language model that activates the language model’s in-context learning ability for structured KG tasks, effectively bridging the gap between unstructured natural language and structured tasks. Unlike prior methods that inefficiently employ classification over exhaustive candidate sets, we recast knowledge graph reasoning from a generative perspective, allowing for direct computation of inference results without iterative enumeration. Additionally, we propose a distant neighborhood awareness strategy to enrich the sparse structural features of few-shot entities.Our experimental findings indicate that our method not only achieves state-of-the-art performance in few-shot scenarios. The tunable parameters of our model are approximately 1% of those in previous language model-based methods, and the inference time has been reduced to 1/10 of that required by previous methods.

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ForestCast: Open-Ended Event Forecasting with Semantic News Forest
Zi Yu | Shaoxiang Wang | Guozheng Li | Yu Zhang | Chi Harold Liu

Open-ended event forecasting (OEEF) seeks to predict future events from a given context without being restricted to a predefined scope or format. It plays a crucial role in domains such as risk management and financial decision making. Although large language models show potential for OEEF, existing approaches and datasets often overlook the complex relationships among events, and current research lacks comprehensive evaluation methods. To address these limitations, we propose ForestCast, a prediction pipeline that extracts forecast-relevant events from news data, organizes them into a story tree, and predicts subsequent events along each path. The pipeline comprises four stages: (1) grouping news into event nodes, (2) constructing a news story tree, (3) mining the semantic structure of the tree, and (4) predicting the next event node and evaluating prediction quality. To support this pipeline, we construct NewsForest, a dataset of 12,406 event chains, each representing a chronologically and logically linked sequence of news events. In addition, we introduce a comprehensive evaluation framework that measures both the accuracy and the quality of prediction. Experimental results demonstrate that ForestCast improves the ability of LLMs to forecast events in news data.

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Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge
Mohammad Reza Rezaei | Reza Saadati Fard | Jayson Lee Parker | Rahul G Krishnan | Milad Lankarany

Large Language Models (LLMs) have greatly advanced medical Question Answering (QA) by leveraging vast clinical data and medical literature. However, the rapid evolution of medical knowledge and the labor-intensive process of manually updating domain-specific resources can undermine the reliability of these systems. We address this challenge with Agentic Medical Graph-RAG (AMG-RAG), a comprehensive framework that automates the construction and continuous updating of Medical Knowledge Graph (MKG), integrates reasoning, and retrieves current external evidence from the MKG for medical QA.Evaluations on the MEDQA and MEDMCQA benchmarks demonstrate the effectiveness of AMG-RAG, achieving an F1 score of 74.1% on MEDQA and an accuracy of 66.34% on MEDMCQA—surpassing both comparable models and those 10 to 100 times larger. By dynamically linking new findings and complex medical concepts, AMG-RAG not only boosts accuracy but also enhances interpretability for medical queries, which has a critical impact on delivering up-to-date, trustworthy medical insights.

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Text Anomaly Detection with Simplified Isolation Kernel
Yang Cao | Sikun Yang | Yujiu Yang | Lianyong Qi | Ming Liu

Two-step approaches combining pre-trained large language model embeddings and anomaly detectors demonstrate strong performance in text anomaly detection by leveraging rich semantic representations. However, high-dimensional dense embeddings extracted by large language models pose challenges due to substantial memory requirements and high computation time. To address this challenge, we introduce the Simplified Isolation Kernel (SIK), which maps high-dimensional dense embeddings to lower-dimensional sparse representations while preserving crucial anomaly characteristics. SIK has linear-time complexity and significantly reduces space complexity through its innovative boundary-focused feature mapping.Experiments across 7 datasets demonstrate that SIK achieves better detection performance than 11 SOTA anomaly detection algorithms while maintaining computational efficiency and low memory cost. All code and demonstrations are available at https://github.com/charles-cao/SIK.

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Idola Tribus of AI: Large Language Models tend to perceive order where none exists
Shin-nosuke Ishikawa | Masato Todo | Taiki Ogihara | Hirotsugu Ohba

We present a tendency of large language models (LLMs) to generate absurd patterns despite their clear inappropriateness in a simple task of identifying regularities in number series. Several approaches have been proposed to apply LLMs to complex real-world tasks, such as providing knowledge through retrieval-augmented generation and executing multi-step tasks using AI agent frameworks. However, these approaches rely on the logical consistency and self-coherence of LLMs, making it crucial to evaluate these aspects and consider potential countermeasures. To identify cases where LLMs fail to maintain logical consistency, we conducted an experiment in which LLMs were asked to explain the patterns in various integer sequences, ranging from arithmetic sequences to randomly generated integer series. While the models successfully identified correct patterns in arithmetic and geometric sequences, they frequently over-recognized patterns that were inconsistent with the given numbers when analyzing randomly generated series. This issue was observed even in multi-step reasoning models, including OpenAI o3, o4-mini, and Google Gemini 2.5 Flash Preview Thinking. This tendency to perceive non-existent patterns can be interpreted as the AI model equivalent of Idola Tribus and highlights potential limitations in their capability for applied tasks requiring logical reasoning, even when employing chain-of-thought reasoning mechanisms.

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Thunder-DeID: Accurate and Efficient De-identification Framework for Korean Court Judgments
Sungeun Hahm | Heejin Kim | Gyuseong Lee | Hyunji M. Park | Jaejin Lee

To ensure a balance between open access to justice and personal data protection, the South Korean judiciary mandates the de-identification of court judgments before they can be publicly disclosed. However, the current de-identification process is inadequate for handling court judgments at scale while adhering to strict legal requirements. Additionally, the legal definitions and categorizations of personal identifiers are vague and not well-suited for technical solutions. To tackle these challenges, we propose a de-identification framework called Thunder-DeID, which aligns with relevant laws and practices. Specifically, we (i) construct and release the first Korean legal dataset containing annotated judgments along with corresponding lists of entity mentions, (ii) introduce a systematic categorization of Personally Identifiable Information (PII), and (iii) develop an end-to-end deep neural network (DNN)-based de-identification pipeline. Our experimental results demonstrate that our model achieves state-of-the-art performance in the de-identification of court judgments.

Autonomous Driving Systems (ADSs) are revolutionizing transportation by reducing human intervention, improving operational efficiency, and enhancing safety. Large Language Models (LLMs), known for their exceptional planning and reasoning capabilities, have been integrated into ADSs to assist with driving decision-making. However, LLM-based single-agent ADSs face three major challenges: limited perception, insufficient collaboration, and high computational demands. To address these issues, recent advancements in LLM-based multi-agent ADSs have focused on improving inter-agent communication and cooperation. This paper provides a frontier survey of LLM-based multi-agent ADSs. We begin with a background introduction to related concepts, followed by a categorization of existing LLM-based approaches based on different agent interaction modes. We then discuss agent-human interactions in scenarios where LLM-based agents engage with humans. Finally, we summarize key applications, datasets, and challenges in this field to support future research (https://github.com/Yaozuwu/LLM-based_Multi-agent_ADS).

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Comprehensive Evaluation on Lexical Normalization: Boundary-Aware Approaches for Unsegmented Languages
Shohei Higashiyama | Masao Utiyama

Lexical normalization research has sought to tackle the challenge of processing informal expressions in user-generated text, yet the absence of comprehensive evaluations leaves it unclear which methods excel across multiple perspectives. Focusing on unsegmented languages, we make three key contributions: (1) creating a large-scale, multi-domain Japanese normalization dataset, (2) developing normalization methods based on state-of-the-art pre-trained models, and (3) conducting experiments across multiple evaluation perspectives. Our experiments show that both encoder-only and decoder-only approaches achieve promising results in both accuracy and efficiency.

Large Language Models (LLMs) have achieved impressive success across a range of natural language processing tasks. However, they still underperform in text classification tasks compared to fine-tuned small models. This can be linked to complexities in addressing context-dependent expressions and complex linguistic phenomena. In contrast, fine-tuned small models typically achieve high prediction accuracy but often lack explanations for predictions. Existing explanation methods that generate keywords may be less effective due to missing critical contextual information. To mitigate these challenges, we propose a novel method termed Dialectical Explanation Training (**DET**). This method introduces a new prompting strategy, Dialectical Prompting, and integrates it with Explanation-Guided Training. Dialectical Prompting uses LLMs with our designed dialectical prompt to generate explanations for possible labels. These explanations handle context-dependent expressions and complex linguistic phenomena by considering multiple perspectives and providing rich, contextually relevant information. Explanation-Guided Training employs these explanations as features for training a small model, which combines the advantages of dialectical explanations and the predictive power of fine-tuned models to improve overall accuracy and interpretability. In addition, we incorporate the theory of Evidential Deep Learning, which further enhances the model’s classification performance and quantify the uncertainty of its predictions. Extensive experiments on multiple datasets from diverse domains have demonstrated that our proposed model significantly improves accuracy and explanation quality over state-of the-art methods in text classification.

Despite LLMs’ excellent code creation capabilities, multilingual code generation remains extremely challenging. To address this, we intent to improve the multi-programming-lingual (MultiPL) performance of the base LLMs while retaining the most popular ones using restricted computational resources. We consider MultiPL to be a special case of multiple natural languages and propose a MultiPL extension of LLMs utilizing a hybrid mixture of experts (MoE), called MultiPL-MoE. Specifically, MultiPL-MoE combines two paired MoEs to optimize expert selection at both the token and segment levels. The **token-level MoE** is a standard upcycling MoE structure with a shared expert and a novel gate weight normalization approach that aids in the final fusion with the segment-level MoE. The **segment-level MoE** incorporates two innovative designs to better capture the syntactic structure and contextual patterns of programming languages: First, using a sliding window to partition the input token sequence into multiple segments; Then, adopting an expert-choice routing strategy that allows experts to select the top-k segments. The results of the experiment proved the effectiveness of MultiPL-MoE.

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AutoSpec: An Agentic Framework for Automatically Drafting Patent Specification
Ryan Shea | Zhou Yu

Patents play a critical role in driving technological innovation by granting inventors exclusive rights to their inventions. However the process of drafting a patent application is often expensive and time-consuming, making it a prime candidate for automation. Despite recent advancements in language models, several challenges hinder the development of robust automated patent drafting systems. First, the information within a patent application is highly confidential, which often prevents the use of closed-source LLMs for automating this task. Second, the process of drafting a patent application is difficult for even the most advanced language models due to their long context, technical writing style, and specialized domain knowledge. To address these challenges, we introduce AutoSpec, a secure, agentic framework for Automatically drafting patent Specification. Our approach decomposes the drafting process into a sequence of manageable subtasks, each solvable by smaller, open-source language models enhanced with custom tools tailored for drafting patent specification. To assess our system, we design a novel evaluation protocol in collaboration with experienced patent attorneys. Our automatic and expert evaluations show that AutoSpec outperforms existing baselines on a patent drafting task.

Instruction tuning (IT) is an effective approach for aligning large language models (LLMs) with human intentions. There is ongoing discourse regarding the data quality for IT. As an effort to find the robust criteria of data quality for IT, we introduce LimaCost, a data quality measure that exhibits a strong correlation with model performance. LimaCost utilizes LIMA dataset, which effectiveness in IT has already been validated by several previous works. LimaCost then estimates the value of a given data by estimating how many LIMA data points might be needed to approximate its gradient. Our experiments reveal that LimaCost enables effective data selection that derive high alignment performance. We demonstrate that selecting data based on high LimaCost proves to be more effective than existing data selection strategies.

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Two Challenges, One Solution: Robust Multimodal Learning through Dynamic Modality Recognition and Enhancement
Lanxin Bi | Yunqi Zhang | Luyi Wang | Yake Niu | Hui Zhao

Multimodal machine learning is often hindered by two critical challenges: modality missingness and modality imbalance. These challenges significantly degrade the performance of multimodal models. The majority of existing methods either require the availability of full-modality data during the training phase or necessitate explicit annotations to detect missing modalities. These dependencies severely limit the models’ applicability in the real world. To tackle these problems, we propose a Dynamic modality Recognition and Enhancement for Adaptive Multimodal fusion framework *DREAM*. Within DREAM, we innovatively employ a sample-level dynamic modality assessment mechanism to direct selective reconstruction of missing or underperforming modalities. Additionally, we introduce a soft masking fusion strategy that adaptively integrates different modalities according to their estimated contributions, enabling more accurate and robust predictions. Experimental results on three benchmark datasets consistently demonstrate that DREAM outperforms several representative baseline and state-of-the-art models, marking its robustness against modality missingness and imbalanced modality.

Post-training pruning, as one of the key techniques for compressing large language models (LLMs), plays a vital role in lightweight model deployment and model sparsity. However, current mainstream pruning methods dependent on the Hessian matrix face significant limitations in both pruning speed and practical effectiveness due to the computationally intensive nature of second-order derivative calculations. This paper presents SwiftPrune, a novel Hessian-free weight pruning method that achieves hardware-efficient model compression through two key innovations: 1) SwiftPrune eliminates the need for computationally intensive Hessian matrix calculations by introducing a contribution-based weight metric, which evaluates the importance of weights without relying on second-order derivatives. 2) we employ the Exponentially Weighted Moving Average (EWMA) technique to bypass weight sorting, enabling the selection of weights that contribute most to LLM accuracy and further reducing time complexity. Our approach is extended to support structured sparsity pruning, facilitating efficient execution on modern hardware accelerators. We validate the SwiftPrune on three LLMs (namely LLaMA2, LLaMA3, and Pythia), demonstrating that it significantly enhances compression performance. The experimental findings reveal that SwiftPrune completes the pruning process within seconds, achieving an average speedup of 12.29x (up to 56.02x) over existing SOTA approaches.

Large Language Models (LLMs) have revolutionized various domains but encounter substantial challenges in tackling optimization modeling tasks for Operations Research (OR), particularly when dealing with complex problem. In this work, we propose Step-Opt-Instruct, a framework that augments existing datasets and generates high-quality fine-tuning data tailored to optimization modeling. Step-Opt-Instruct employs iterative problem generation to systematically increase problem complexity and stepwise validation to rigorously verify data, preventing error propagation and ensuring the quality of the generated dataset. Leveraging this framework, we fine-tune open-source LLMs, including LLaMA-3-8B and Mistral-7B, to develop Step-Opt—a model that achieves state-of-the-art performance on benchmarks such as NL4OPT, MAMO, and IndustryOR. Extensive experiments demonstrate the superior performance of Step-Opt, especially in addressing complex OR tasks, with a notable 17.01% improvement in micro average accuracy on difficult problems. These findings highlight the effectiveness of combining structured validation with gradual problem refinement to advance the automation of decision-making processes using LLMs. The code and dataset are available at https://github.com/samwu-learn/Step.

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Exploiting Prompt-induced Confidence for Black-Box Attacks on LLMs
Meina Chen | Yihong Tang | Kehai Chen

Large language models (LLMs) are vulnerable to adversarial attacks even in strict black-box settings with only hard-label feedback.Existing attacks suffer from inefficient search due to lack of informative signals such as logits or probabilities. In this work, we propose Prompt-Guided Ensemble Attack (PGEA), a novel black-box framework that leverages prompt-induced confidence, which reflects variations in a model’s self-assessed certainty across different prompt templates, as an auxiliary signal to guide attacks. We first demonstrate that confidence estimates vary significantly with prompt phrasing despite unchanged predictions. We then integrate these confidence signals in a two-stage attack: (1) estimating token-level vulnerability via confidence elicitation, and (2) applying ensemble word-level substitutions guided by these estimates. Experiments on LLaMA-3-8B-Instruct and Mistral-7B-Instruct-v0.3 on three classification tasks show that PGEA improves the attack success rate and query efficiency while maintaining semantic fidelity. Our results highlight that verbalized confidence, even without access to probabilities, is a valuable and underexplored signal for black-box adversarial attacks. The code is available at https://github.com/cmn-bits/PGEA-main.

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DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment
Wei Huang | Anda Cheng | Zhao Zhang | Yinggui Wang

Current open-source training pipelines for Chinese medical language models predominantly emphasize optimizing training methodologies to enhance the performance of large language models (LLMs), yet lack comprehensive exploration into training data processing. To address this gap, we propose DPF-CM, a holistic Data Processing Framework for Chinese Medical LLMs training and deployment. DPF-CM comprises two core modules. The first module is a data processing pipeline tailored for model training. Beyond standard data processing operations, we (1) introduce a chained examples context-learning strategy to generate question-oriented instructions to mitigate the lack of instruction content, and (2) implement an ensemble-based filtering mechanism for preference data curation that averages multiple reward models to suppress noisy samples. The second module focuses on privacy preservation during model deployment. To prevent privacy risks from the inadvertent exposure of training data, we propose a Privacy Preserving Vector Database (PPVD) approach, which involves model memory search, high-risk database construction, secure database construction, and match-and-replace, four key stages to minimize privacy leakage during inference collectively. Experimental results show that DPF-CM significantly improves model accuracy, enabling our trained Chinese medical LLM to achieve state-of-the-art performance among open-source counterparts. Moreover, the framework reduces training data privacy leakage by 27%.

Reinforcement learning (RL) has been widely adopted to enhance the performance of large language models (LLMs) on Text-to-SQL tasks. However, existing methods often rely on execution-based or LLM-based Bradley–Terry reward models. The former suffers from high execution latency caused by repeated database calls, whereas the latter imposes substantial GPU memory overhead, both of which significantly hinder the efficiency and scalability of RL pipelines. To this end, we propose a novel reward model framework for RL-based Text-to-SQL named Graph-Reward-SQL, which employs the GMNScore outcome reward model. We leverage SQL graph representations to provide accurate reward signals while significantly reducing time cost and GPU memory usage. Building on this foundation, we further introduce StepRTM, a stepwise reward model that provides intermediate supervision over Common Table Expression (CTE) subqueries. This encourages both functional correctness and readability of SQL. Extensive comparative and ablation experiments on standard benchmarks, including Spider and BIRD, demonstrate that our method consistently outperforms existing reward models.

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StatsChartMWP: A Dataset for Evaluating Multimodal Mathematical Reasoning Abilities on Math Word Problems with Statistical Charts
Dan Zhu | Tianqiao Liu | Zitao Liu

Recent advancements in Large Multimodal Models (LMMs) have showcased their impressive capabilities in mathematical reasoning tasks in visual contexts. As a step toward developing AI models to conduct rigorous multi-step multimodal reasoning, we introduce StatsChartMWP, a real-world educational dataset for evaluating visual mathematical reasoning abilities on math word problems (MWPs) with statistical charts. Our dataset contains 8,514 chart-based MWPs, meticulously curated by K-12 educators within real-world teaching scenarios. We provide detailed preprocessing steps and manual annotations to help evaluate state-of-the-art models on StatsChartMWP. Comparing baselines, we find that current models struggle in undertaking meticulous multi-step mathematical reasoning among technical languages, diagrams, tables, and equations. Towards alleviate this gap, we introduce CoTAR, a chain-of-thought (CoT) augmented reasoning solution that fine-tunes the LMMs with solution-oriented CoT-alike reasoning steps. The LMM trained with CoTAR is more effective than current open-source approaches. We conclude by shedding lights on challenges and opportunities in enhancement in LMMs and steer future research and development efforts in the realm of statistical chart comprehension and analysis. The code and data are available at https://github.com/ai4ed/StatsChartMWP.

Recent Large Reasoning Models (LRMs) have demonstrated the ability to generate long chains of thought (LongCoT) before arriving at a final conclusion. Despite remarkable breakthroughs in complex reasoning capabilities, LongCoT still faces challenges such as redundancy and logical incoherence. To address these issues, we aim to equip large language models (LLMs) with rigorous and concise logical reasoning capabilities. In this work, we propose Logic-Thinker, a neural-symbolic reasoning framework that employs symbolic solvers to precisely solve problems and transforms their internal solving processes into concise and rigorous chains of thought, referred to as ThinkerCoT. Our experimental results demonstrate that Logic-Thinker achieves state-of-the-art performance in logical reasoning problems. Additionally, LLMs fine-tuned with ThinkerCoT outperform models distilled from QwQ32B on logic reasoning tasks, achieving an overall accuracy improvement of 3.6% while reducing token output by 73%-91%. Furthermore, ThinkerCoT enhances the comprehensive reasoning capabilities of LLMs, as evidenced by performance improvements on reasoning benchmarks such as GPQA and AIME.

Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex problems. However, existing benchmarks for evaluating LLMs’ tool usage face several limitations: (1) limited evaluation scenarios, often lacking assessments in real multi-turn dialogue contexts; (2) narrow evaluation dimensions, with insufficient detailed assessments of how LLMs use tools; and (3) reliance on LLMs or real API executions for evaluation, which introduces significant overhead. To address these challenges, we introduce ACEBench, a comprehensive benchmark for assessing tool usage in LLMs. ACEBench categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent. “Normal” evaluates tool usage in basic scenarios; “Special” evaluates tool usage in situations with ambiguous or incomplete instructions; “Agent” evaluates tool usage through multi-agent interactions to simulate real-world, multi-turn dialogues. We conducted extensive experiments using ACEBench, analyzing various LLMs in-depth and providing a more granular examination of error causes across different data types.

Retrieval-Augmented Generation (RAG) enriches the input to LLMs by retrieving information from the relevant knowledge database, enabling them to produce responses that are more accurate and contextually appropriate. It is worth noting that the knowledge database, being sourced from publicly available channels such as Wikipedia, inevitably introduces a new attack surface. RAG poisoning attack involves injecting malicious texts into the knowledge database, ultimately leading to the generation of the attacker’s target response (also called poisoned response). However, there are currently limited methods available for detecting such poisoning attacks. We aim to bridge the gap in this work by introducing RevPRAG, a flexible and automated detection pipeline that leverages the activations of LLMs for poisoned response detection. Our investigation uncovers distinct patterns in LLMs’ activations when generating poisoned responses versus correct responses. Our results on multiple benchmarks and RAG architectures show our approach can achieve a 98% true positive rate, while maintaining a false positive rate close to 1%.

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DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
Wei Huang | Huang Wei | Yinggui Wang

Large language models (LLMs) excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. With many open-source LLMs available, selecting the best model for fine-tuning downstream tasks is challenging, primarily focusing on how to quickly identify the optimal LLM. We introduce a Data and Model Compression Framework (DaMoC) that addresses this challenge by: 1) Data Level: A systematic categorization of data filtering methodologies for LLMs is first established, classifying them into three distinct paradigms: (1) distribution-aware methods, (2) quality-aware methods, and (3) hybrid approaches considering both dimensions. Further, we enhance the density of key tokens in the text achieving token compression. Subsequently, we use an LLM to iterative rewrite the text to optimize its expression. 2) Model Level: We use layer similarity scores to assess each layer’s importance and remove those with lower importance. Then, we introduce a sparse merging paradigm to preserve as much of the original model’s capability as possible. Extensive experiments on four datasets, medical Q&A, financial Q&A, general Q&A, and reading comprehension, show that we can select the optimal LLM while saving approximately 20-fold in training time.

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CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning
Jianfeng Pan | Senyou Deng | Shaomang Huang

Research on LLM technologies is rapidly emerging, with most of them employ a ‘fast thinking’ approach to inference. Most LLMs generate the final result based solely on a single query and LLM’s reasoning capabilities. However, with the advent of OpenAI-o1, ‘slow thinking’ techniques have garnered increasing attention because its process is closer to the human thought process. Inspired by the human ability to constantly associate and replenish knowledge during thinking, we developed the novel Chain-of-Associated-Thoughts (CoAT) framework, which introduces an innovative synergy between the Monte Carlo Tree Search (MCTS) algorithm and a dynamic mechanism for integrating new key information, termed ‘associative memory’. By combining the structured exploration capabilities of MCTS with the adaptive learning capacity of associative memory, CoAT significantly expands the LLM search space, enabling our framework to explore diverse reasoning pathways and dynamically update its knowledge base in real-time. This allows the framework to not only revisit and refine earlier inferences but also adaptively incorporate evolving information, ensuring that the final output is both accurate and comprehensive. We validate CoAT’s effectiveness across a variety of generative and reasoning tasks. Quantitative experiments show that CoAT achieves over 10% performance improvement on open-source multi-hop reasoning datasets (HotpotQA, MuSiQue) and more than 15% gain on our proprietary CRB dataset.

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ChartM³: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension
Duo Xu | Hao Cheng | Xin Lin | Zhen Xie | Hao Henry Wang

Complex chart understanding tasks demand advanced visual recognition and reasoning capabilities from multimodal large language models (MLLMs). However, current research provides limited coverage of complex chart scenarios and computation-intensive reasoning tasks prevalent in real-world applications. This study proposes an automated multi-stage code-driven pipeline for systematically generating visual reasoning datasets to address these limitations. The pipeline integrates retrieval-augmented generation (RAG) to retrieve professional chart templates and employs chain-of-thought (CoT) strategies to generate reasoning codes that simulate real data distributions, thereby driving chart rendering and question-related statistical computations. Through model-based evaluation, the pipeline enhances chart diversity and data quality. Using this framework, we construct ChartM³, a multi-dimensional and multi-step dataset containing 38K charts and 142K Q&A pairs for training, along with 2,871 high-quality evaluation samples for enabling practical performance assessment. Supervised fine-tuning (SFT) and reinforcement learning (RL) experiments demonstrate that our dataset significantly improves reasoning capabilities and cross-domain generalization performance, enabling smaller models to achieve performance comparable to larger-scale models in complex chart comprehension.

The existing assessments of planning capabilities of large language models (LLMs) remain largely limited to single-language or specific representation formats. To address this gap, we introduce the Multi-Plan benchmark comprising 204 multilingual and multi-format travel planning scenarios. In experimental results obtained with state-of-the-art LLMs, the Multi-Plan benchmark effectively highlights the performance disparities among models, notably showing superior results for reasoning-specialized models. Interestingly, language differences exhibited minimal impact, whereas mathematically structured representations significantly improved planning accuracy for most models, underscoring the crucial role of the input format. These findings enhance our understanding of planning abilities of LLMs, offer valuable insights for future research, and emphasize the need for more sophisticated AI evaluation methods. This dataset is publicly available at http://huggingface.co/datasets/Bllossom/Multi-Plan.

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MARIO-0.5B: A Multi-Agent Lightweight Model for Real-Time Open Information Extraction in Low-Resource Settings
Donghai Zhang | SHuangtao Yang | Dong Xiaozheng | Wei Song | Bo Fu

Large language models (LLMs) have shown remarkable capabilities in open information extraction. However, their substantial resource requirements often restrict their deployment in resource-constrained industrial settings, particularly on edge devices. The high computational demands also lead to increased latency, making them difficult to apply in real-time applications. In this paper, we introduce MARIO-0.5B, an ultra-lightweight model trained on instruction-based samples in Chinese, English, Korean, and Russian. We also present a novel multi-agent framework, SMOIE, which integrates schema mining, information extraction, reasoning, and decision-making to effectively support MARIO-0.5B.The experimental results show that our framework outperforms large-scale models with up to 70B parameters, reducing computational resources by 140x and delivering 11x faster response times. Moreover, it operates efficiently in CPU-only environments, which makes it well-suited for widespread industrial deployment.

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BiMax: Bidirectional MaxSim Score for Document-Level Alignment
Xiaotian Wang | Takehito Utsuro | Masaaki Nagata

Document alignment is necessary for the hierarchical mining, which aligns documents across source and target languages within the same web domain. Several high-precision sentence embedding-based methods have been developed, such as TK-PERT and Optimal Transport (OT). However, given the massive scale of web mining data, both accuracy and speed must be considered.In this paper, we propose a cross-lingual Bidirectional Maxsim score (BiMax) for computing doc-to-doc similarity,to improve efficiency compared to the OT method.Consequently, on the WMT16 bilingual document alignment task,BiMax attains accuracy comparable to OT with an approximate 100-fold speed increase.Meanwhile, we also conduct a comprehensive analysis to investigate the performance of current state-of-the-art multilingual sentence embedding models.

The rapid advancement of unsupervised representation learning and large-scale pre-trained vision-language models has significantly improved cross-modal retrieval tasks. However, existing multi-modal information retrieval (MMIR) studies lack a comprehensive exploration of document-level retrieval and suffer from the absence of cross-domain datasets at this granularity. To address this limitation, we introduce DocMMIR, a novel multi-modal document retrieval framework designed explicitly to unify diverse document formats and domains—including Wikipedia articles, scientific papers (arXiv), and presentation slides—within a comprehensive retrieval scenario. We construct a large-scale cross-domain multimodal dataset, comprising 450K training, 19.2K validation, and 19.2K test documents, serving as both a benchmark to reveal the shortcomings of existing MMIR models and a training set for further improvement. The dataset systematically integrates textual and visual information. Our comprehensive experimental analysis reveals substantial limitations in current state-of-the-art MLLMs (CLIP, BLIP2, SigLIP-2, ALIGN) when applied to our tasks, with only CLIP (ViT-L/14) demonstrating reasonable zero-shot performance. Through systematic investigation of cross-modal fusion strategies and loss function selection on the CLIP (ViT-L/14) model, we develop an optimised approach that achieves a +31% improvement in MRR@10 metrics from zero-shot baseline to fine-tuned model. Our findings offer crucial insights and practical guidance for future development in unified multimodal document retrieval tasks.

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MoVoC: Morphology-Aware Subword Construction for Ge’ez Script Languages
Hailay Kidu Teklehaymanot | Dren Fazlija | Wolfgang Nejdl

Subword-based tokenization methods often fail to preserve morphological boundaries, a limitation especially pronounced in low-resource, morphologically complex languages such as those written in the Ge‘ez script. To address this, we present MoVoC (Morpheme-aware Subword Vocabulary Construction) and train MoVoC-Tok, a tokenizer that integrates supervised morphological analysis into the subword vocabulary. This hybrid segmentation approach combines morpheme-based and Byte Pair Encoding (BPE) tokens to preserve morphological integrity while maintaining lexical meaning. To tackle resource scarcity, we curate and release manually annotated morpheme data for four Ge‘ez script languages and a morpheme-aware vocabulary for two of them. While the proposed tokenization method does not lead to significant gains in automatic translation quality, we observe consistent improvements in intrinsic metrics, MorphoScore, and Boundary Precision, highlighting the value of morphology-aware segmentation in enhancing linguistic fidelity and token efficiency. Our morpheme-annotated datasets and tokenizer dataset will be publicly available under the Open Data licenses to support further research in low-resource, morphologically rich languages.

Rather than merely to retain previously acquired generalization, achieving synergistic improvements between generalization and domain specialization in foundation models remains a significant challenge in both pre-training and post-training. As an alternative, we propose a test-time cross-domain knowledge integration method, Mixture of Multi-domain Agents (MMA), which dynamically combines the outputs of general-purpose and domain-specific models to enhance their performance on complex, domain‐specific tasks. MMA formulates the integration process as a search problem, using Monte Carlo Tree Search (MCTS) to find the path that optimally harmonizes the respective strengths of different models in generalization and domain-specific knowledge. In addition, We design specific action spaces to control the knowledge integration between multiple models, and cross-inspection reward is introduced to fairly score strategies in different domains. Experiments in diverse domains show that MMA can effectively combine the strengths of different models to enhance their performance. For instance, in legal tests, the average performance of all tasks increased from 42.57% to 53.68%. In financial tests, it improved from 56.01% to 62.68%.

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HAWK: Highlighting Entity-aware Knowledge for Alleviating Information Sparsity in Long Contexts
Seonmin Koo | Jinsung Kim | Chanjun Park | Heuiseok Lim

As the textual data given as the context of various tasks lengthens, having necessary information scattered throughout makes it more difficult for large language models (LLMs) to capture relevant details. This challenge is particularly prominent in tasks such as question answering (QA), where key information is often not evenly distributed within the context. This problem of information sparsity has led to the attempts of various approaches, such as direct context adjustment and retrieval-based methods. However, these approaches typically leverage compressed contexts, which increases the risk that key information may be contained in the dropped portions. Therefore, research from the perspective of addressing the information sparsity while not losing key details in contexts is required. To address this issue, we propose Highlighting entity-AWare Knowledge (HAWK) framework. HAWK consists of three main steps: i) entity extraction, ii) entity-aware subcontext selection, and iii) triplet construction. The core mechanism of HAWK is to highlight key information in a context and structuralize it in an entity-aware manner, facilitating knowledge-enhanced generation. Through extensive experiments and comprehensive analysis, HAWK confirms significant improvements in QA tasks with long contexts, achieving up to a 27.6-point F1 score increase and at least an average win rate of 76.75% over existing methods.

Large Language Models (LLMs) have transformed both everyday life and scientific research. However, adapting LLMs from general-purpose models to specialized tasks remains challenging, particularly in resource-constrained environments. Low-Rank Adaptation (LoRA), a prominent method within Parameter-Efficient Fine-Tuning (PEFT), has emerged as a promising approach to LLMs by approximating model weight updates using low-rank decomposition. However, LoRA is limited by its uniform rank ( r ) allocation to each incremental matrix, and existing rank allocation techniques aimed at addressing this issue remain computationally inefficient, complex, and unstable, hindering practical applications. To address these limitations, we propose Sensitivity-LoRA, an efficient fine-tuning method that dynamically allocates ranks to weight matrices based on both their global and local sensitivities. It leverages the second-order derivatives (Hessian Matrix) of the loss function to effectively capture weight sensitivity, enabling optimal rank allocation with minimal computational overhead. Our experimental results have demonstrated robust effectiveness, efficiency and stability of Sensitivity-LoRA across diverse tasks and benchmarks.

Instruction tuning has underscored the significant potential of large language models (LLMs) in producing more human controllable and effective outputs in various domains. In this work, we focus on the data selection problem for task-specific instruction tuning of LLMs. Prevailing methods primarily rely on the crafted similarity metrics to select training data that aligns with the test data distribution. The goal is to minimize instruction tuning loss on the test data, ultimately improving performance on the target task. However, it has been widely observed that instruction tuning loss (i.e., cross-entropy loss for next token prediction) in LLMs often fails to exhibit a monotonic relationship with actual task performance. This misalignment undermines the effectiveness of current data selection methods for task-specific instruction tuning. To address this issue, we introduce ROSE, a novel Reward-Oriented inStruction data sElection method which leverages pairwise preference loss as a reward signal to optimize data selection for task-specific instruction tuning. Specifically, ROSE adapts an influence formulation to approximate the influence of training data points relative to a few-shot preference validation set to select the most task-related training data points. Experimental results show that by selecting just 5% of the training data using ROSE, our approach can achieve competitive results compared to fine-tuning with the full training dataset, and it surpasses other state-of-the-art data selection methods for task-specific instruction tuning. Our qualitative analysis further confirms the robust generalizability of our method across multiple benchmark datasets and diverse model architectures.

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SimBA: Simplifying Benchmark Analysis Using Performance Matrices Alone
Nishant Subramani | Alfredo Gomez | Mona T. Diab

Modern language models are evaluated on large benchmarks, which are difficult to make sense of, especially for model selection.Looking at the raw evaluation numbers themselves using a model-centric lens, we propose SimBA, a three phase framework to Simplify Benchmark Analysis. The three phases of SimBA are: stalk, where we conduct dataset & model comparisons, prowl, where we discover a representative subset, and pounce, where we use the representative subset to predict performance on a held-out set of models. Applying SimBA to three popular LM benchmarks: HELM, MMLU, and BigBenchLite reveals that across all three benchmarks, datasets and models relate strongly to one another (stalk). We develop an representative set discovery algorithm which covers a benchmark using raw evaluation scores alone. Using our algorithm, we find that with 6.25% (1/16), 1.7% (1/58), and 28.4% (21/74) of the datasets for HELM, MMLU, and BigBenchLite respectively, we achieve coverage levels of at least 95% (prowl). Additionally, using just these representative subsets, we can both preserve model ranks and predict performance on a held-out set of models with near zero mean-squared error (pounce). Taken together, SimBA can help model developers improve efficiency during model training and dataset creators validate whether their newly created dataset differs from existing datasets in a benchmark. Our code is open source, available at https://github.com/nishantsubramani/simba.

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MarathiEmoExplain: A Dataset for Sentiment, Emotion, and Explanation in Low-Resource Marathi
Anuj Kumar | Mohammed Faisal Sayed | Satyadev Ahlawat | Yamuna Prasad

Marathi, the third most widely spoken language in India with over 83 million native speakers, remains significantly underrepresented in Natural Language Processing (NLP) research. While sentiment analysis has achieved substantial progress in high-resource languages such as English, Chinese, and Hindi, available Marathi datasets are limited to coarse sentiment labels and lack fine-grained emotional categorization or interpretability through explanations. To address this gap, we present a new annotated dataset of 10,762 Marathi sentences, each labeled with sentiment (positive, negative, or neutral), emotion (joy, anger, surprise, disgust, sadness, fear, or neutral), and a corresponding natural language justification. Justifications are written in English and generated using GPT-4 under a human-in-the-loop framework to ensure label fidelity and contextual alignment. Extensive experiments with both classical and transformer-based models demonstrate the effectiveness of the dataset for interpretable affective computing in a low-resource language setting, offering a benchmark for future research in multilingual and explainable NLP.

Large Language Models (LLMs) have demonstrated an impressive level of general knowledge. However, they often struggle in highly specialized and sensitive domains such as drug discovery and rare disease research due to the lack of expert knowledge, which is often costly to obtain. In this paper, we propose a novel framework (PU-ADKA) designed to efficiently enhance domain-specific LLMs by actively engaging domain experts within a fixed budget. Unlike traditional fine-tuning approaches, PU-ADKA proactively identifies and queries the most appropriate expert from a team, taking into account each expert’s availability, competency, knowledge boundaries, and consultation cost. We train PU-ADKA using simulations on PubMed publication data and validate it through domain expert interactions, showing promising improvements in LLM domain knowledge acquisition. Furthermore, our experiments with a real-world drug development team validate that PU-ADKA can significantly enhance LLM performance in specialized domains while adhering to strict budget constraints. In addition to outlining our methodological innovations and experimental results, we release a new benchmark dataset, CKAD, for cost-effective LLM domain knowledge acquisition to foster further research in this challenging area.

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Structure-aware Propagation Generation with Large Language Models for Fake News Detection
Mengyang Chen | Lingwei Wei | Wei Zhou | Songlin Hu

The spread of fake news on social media poses a serious threat to public trust and societal stability. While propagation-based methods improve fake news detection by modeling how information spreads, they often suffer from incomplete propagation data. Recent work leverages large language models (LLMs) to generate synthetic propagation, but typically overlooks the structural patterns of real-world discussions. In this paper, we propose a novel structure-aware synthetic propagation enhanced detection (StruSP) framework to fully capture structural dynamics from real propagation. It enables LLMs to generate realistic and structurally consistent propagation for better detection. StruSP explicitly aligns synthetic propagation with real-world propagation in both semantic and structural dimensions. Besides, we also design a new bidirectional evolutionary propagation (BEP) learning strategy to better align LLMs with structural patterns of propagation in the real world via structure-aware hybrid sampling and masked propagation modeling objective. Experiments on three public datasets demonstrate that StruSP significantly improves fake news detection performance in various practical detection scenarios. Further analysis indicates that BEP enables the LLM to generate more realistic and diverse propagation semantically and structurally.

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UniCoM: A Universal Code-Switching Speech Generator
Sangmin Lee | Woojin Chung | Seyun Um | Hong-Goo Kang

Code-switching (CS), the alternation between two or more languages within a single speaker’s utterances, is common in real-world conversations and poses significant challenges for multilingual speech technology. However, systems capable of handling this phenomenon remain underexplored, primarily due to the scarcity of suitable datasets. To resolve this issue, we propose Universal Code-Mixer (UniCoM), a novel pipeline for generating high-quality, natural CS samples without altering sentence semantics. Our approach utilizes an algorithm we call Substituting WORDs with Synonyms (SWORDS), which generates CS speech by replacing selected words with their translations while considering their parts of speech. Using UniCoM, we construct Code-Switching FLEURS (CS-FLEURS), a multilingual CS corpus designed for automatic speech recognition (ASR) and speech-to-text translation (S2TT). Experimental results show that CS-FLEURS achieves high intelligibility and naturalness, performing comparably to existing datasets on both objective and subjective metrics. We expect our approach to advance CS speech technology and enable more inclusive multilingual systems.

Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models, particularly for real-time applications. While traditional optimization approaches like pruning and quantization often compromise model quality, recent advances in generation-refinement frameworks demonstrate that this trade-off can be significantly mitigated.This survey presents a comprehensive taxonomy of generation-refinement frameworks, analyzing methods across autoregressive sequence tasks. We categorize methods based on their generation strategies (from simple n-gram prediction to sophisticated draft models) and refinement mechanisms (including single-pass verification and iterative approaches). Through systematic analysis of both algorithmic innovations and system-level implementations, we examine deployment strategies across computing environments and explore applications spanning text, images, and speech generation. This systematic examination of both theoretical frameworks and practical implementations provides a foundation for future research in efficient autoregressive decoding. In the appendix A, we additionally provide experimental comparisons of various baseline methods.

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Do We Really Need All Those Dimensions? An Intrinsic Evaluation Framework for Compressed Embeddings
Nathan Inkiriwang | Necva Bölücü | Garth Tarr | Maciej Rybinski

High-dimensional text embeddings are foundational to modern NLP but costly to store and use. While embedding compression addresses these challenges, selecting the best compression method remains difficult. Existing evaluation methods for compressed embeddings are either expensive or too simplistic. We introduce a comprehensive intrinsic evaluation framework featuring a suite of task-agnostic metrics that together provide a reliable proxy for downstream performance. A key contribution is \operatorname{EOS}_k, a novel spectral fidelity measure specifically designed to be robust to embedding anisotropy. Through extensive experiments on diverse embeddings across four downstream tasks, we demonstrate that our intrinsic metrics reliably predict extrinsic performance and reveal how different embedding architectures depend on distinct geometric properties. Our framework provides a practical, efficient, and interpretable alternative to standard evaluations for compressed embeddings.

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Mixture of LoRA Experts for Continual Information Extraction with LLMs
Zitao Wang | Xinyi Wang | Wei Hu

We study continual information extraction (IE), which aims to extract emerging information across diverse IE tasks incessantly while avoiding forgetting. Existing approaches are either task-specialized for a single IE task or suffer from catastrophic forgetting and insufficient knowledge transfer in continual IE. This paper proposes a new continual IE model using token-level mixture of LoRA experts with LLMs. We leverage a LoRA router to route each token to the most relevant LoRA experts, facilitating effective knowledge transfer among IE tasks. We guide task experts’ selection by task keys to retain the IE task-specific knowledge and mitigate catastrophic forgetting. We design a gate reflection method based on knowledge distillation to address forgetting in the LoRA router and task keys. The experimental results show that our model achieves state-of-the-art performance, effectively mitigating catastrophic forgetting and enhancing knowledge transfer in continual IE.

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Spelling-out is not Straightforward: LLMs’ Capability of Tokenization from Token to Characters
Tatsuya Hiraoka | Kentaro Inui

Large language models (LLMs) can spell out tokens character by character with high accuracy, yet they struggle with more complex character-level tasks, such as identifying compositional subcomponents within tokens. In this work, we investigate how LLMs internally represent and utilize character-level information during the spelling-out process. Our analysis reveals that, although spelling out is a simple task for humans, it is not handled in a straightforward manner by LLMs. Specifically, we show that the embedding layer does not fully encode character-level information, particularly beyond the first character. As a result, LLMs rely on intermediate and higher Transformer layers to reconstruct character-level knowledge, where we observe a distinct “breakthrough” in their spelling behavior. We validate this mechanism through three complementary analyses: probing classifiers, identification of knowledge neurons, and inspection of attention weights.

Recently, Agentic AI has become an increasingly popular field of research. However, we argue that current practices on agent research are far from standard, rigorous scientific research, which makes it hard to conduct apples-to-apples comparisons among and against existing methods. As a result, it is still obscure how different design choices in an agent framework impact its effectiveness, and measuring progress on agent research remains very hard. In this work, we conduct a systematic empirical study on the GAIA benchmark to investigate the impact of different popular design choices within key agent components in a fair and rigorous way. To begin with, we find that the lack of a standard evaluation protocol makes previous works, even the open-sourced ones, not reproducible, and the variance between different random runs is often non-negligible. Therefore, we first introduce a more robust evaluation protocol to make comparisons more stable. Our empirical study then unveils which components and designs, as well as correlations between these designs, are the keys for building effective agents, while others are not and redundant, despite seemingly making sense. With the insights gained from our empirical study, we build and open-source OAgents, a new foundation agent framework that achieves state-of-the-art performance among open-source projects, providing a good starting point and guidelines for building effective agents. More importantly, supports various design choices for agent components in a modularized way, facilitating future scientific research on Agentic AI.

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2Columns1Row: A Russian Benchmark for Textual and Multimodal Table Understanding and Reasoning
Vildan Saburov | Daniil Vodolazsky | Danil Sazanakov | Alena Fenogenova

Table understanding is a crucial task in document processing and is commonly encountered in practical applications. We introduce 2Columns1Row, the first open-source benchmark for the table question answering task in Russian. This benchmark evaluates the ability of models to reason about the relationships between rows and columns in tables, employing both textual and multimodal inputs. 2Columns1Row consists of six datasets, 28,800 tables, that vary in the complexity of the text within the table contents and the consistency of the values in the cells. We evaluate the models using text-only and multimodal approaches and analyze their performance. Through extensive evaluation, we demonstrate the limitations of current multimodal models on this task and prove the feasibility of a dynamic text-based system utilizing our benchmark. Our results highlight significant opportunities for advancing table understanding and reasoning, providing a solid foundation for future research in this domain.

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Permitted Knowledge Boundary: Evaluating the Knowledge-Constrained Responsiveness of Large Language Models
Wenrui Bao | Kai Wang | Siqiang Luo | Xiang Li

With the advancement of large language models (LLMs), recent research has raised concerns about their controllability.. In this paper, we argue for the importance of Knowledge-Constrained Responsiveness (KCR), ensuring that LLMs comply with human-defined constraints. However, KCR is an implicit and unobservable capability of LLMs, functioning as a black box that currently eludes quantitative assessment. To address this issue, we first introduce the definition of “permitted boundary” and define the “boundary bias” to depict KCR. We propose six metrics to quantify the boundary bias of LLMs and subsequently assess the KCR. Furthermore, we establish a benchmark with two new datasets, KCR-SimpleQA and KCR-WebNLG, to evaluate the performance of LLMs. Our extensive experiments show that several tested LLMs still struggle to varying degrees when adhering to constraints, especially without the corresponding knowledge.

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A Closer Look at Bias and Chain-of-Thought Faithfulness of Large (Vision) Language Models
Sriram Balasubramanian | Samyadeep Basu | Soheil Feizi

Chain-of-thought (CoT) reasoning enhances performance of large language models, but questions remain about whether these reasoning traces faithfully reflect the internal processes of the model. We present the first comprehensive study of CoT faithfulness in large vision-language models (LVLMs), investigating how both text-based and previously unexplored image-based biases affect reasoning and bias articulation. Our work introduces a novel, fine-grained evaluation pipeline for categorizing bias articulation patterns, enabling significantly more precise analysis of CoT reasoning than previous methods. This framework reveals critical distinctions in how models process and respond to different types of biases, providing new insights into LVLM CoT faithfulness. Our findings reveal that subtle image-based biases are rarely articulated compared to explicit text-based ones, even in models specialized for reasoning. Additionally, many models exhibit a previously unidentified phenomenon we term “inconsistent” reasoning - correctly reasoning before abruptly changing answers, serving as a potential canary for detecting biased reasoning from unfaithful CoTs. We then apply the same evaluation pipeline to revisit CoT faithfulness in LLMs across various levels of implicit cues. Our findings reveal that current language-only reasoning models continue to struggle with articulating cues that are not overtly stated.

Despite the rapid development of large language models (LLMs), existing benchmark datasets often focus on low-level cognitive tasks, such as factual recall and basic comprehension, while providing limited coverage of higher-level reasoning skills, including analysis, evaluation, and creation. In this work, we systematically assess the cognitive depth of popular LLM benchmarks using Bloom’s Taxonomy to evaluate both the cognitive and knowledge dimensions.Our analysis reveals a pronounced imbalance: most datasets concentrate on “Remembering” and “Understanding”, with metacognitive and creative reasoning largely underrepresented. We also find that incorporating higher-level cognitive instructions into the current instruction fine-tuning process improves model performance. These findings highlight the importance of future benchmarks incorporating metacognitive evaluations to more accurately assess and enhance model performance.

This study explores how bilingual language models develop complex internal representations.We employ sparse autoencoders to analyze internal representations of bilingual language models with a focus on the effects of training steps, layers, and model sizes.Our analysis shows that language models first learn languages separately, and then gradually form bilingual alignments, particularly in the mid layers. We also found that this bilingual tendency is stronger in larger models.Building on these findings, we demonstrate the critical role of bilingual representations in model performance by employing a novel method that integrates decomposed representations from a fully trained model into a mid-training model.Our results provide insights into how language models acquire bilingual capabilities.

Multi-condition information retrieval (IR) presents a significant, yet underexplored challenge for existing systems. This paper introduces MultiConIR, the first benchmark specifically designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios across five diverse domains. We systematically assess model capabilities through three critical tasks: complexity robustness, relevance monotonicity, and query format sensitivity. Our extensive experiments on 15 models reveal a critical vulnerability: most retrievers and rerankers exhibit severe performance degradation as query complexity increases. Key deficiencies include widespread failure to maintain relevance monotonicity, and high sensitivity to query style and condition placement. The superior performance GPT-4o reveals the performance gap between IR systems and advanced LLM for handling sophisticated natural language queries. Furthermore, this work delves into the factors contributing to reranker performance deterioration and examines how condition positioning within queries affects similarity assessment, providing crucial insights for advancing IR systems towards complex search scenarios.

As general large language models continue to advance, their real-world adaptation through effective fine-tuning remains a significant challenge. We introduce Hierarchical Multilevel Contrastive Learning (HMCL), a new contrastive learning framework that improves task-specific text representation for general models. HMCL integrates 3-level semantic differentiation (positive, weak-positive, and negative) and unifies contrastive learning, pair classification, and ranking objectives into a cohesive optimization strategy. HMCL demonstrates exceptional results across multi-domain and multilingual benchmarks, including text similarity, retrieval, reranking and Retrieval-Augmented Generation (RAG) tasks. It outperforms top unsupervised methods and supervised fine-tuning approaches while maintaining broad compatibility with architectures ranging from BERT to Qwen, 330M to 7B. In real-world merchant consultation scenarios, HMCL shows a 0.70-6.24 point improvement over original fine-tuning methods in large-scale base models. This establishes HMCL as a versatile solution that bridges the gap between general-purpose models and specialized industrial applications.

Although retrieval-augmented generation (RAG) remains essential for knowledge-based question answering (KBQA), current paradigms face critical challenges under specific domains. Existing methods struggle with targeted adaptation on small-scale KBs: vanilla unsupervised training exhibits poor effectiveness, while fine-tuning incurs prohibitive costs of external signals. We present KBAlign, a self-supervised framework that enhances RAG systems through efficient model adaptation. Our key insight is to leverage the model’s intrinsic capabilities for knowledge alignment through two innovative mechanisms: multi-grained self-annotation that captures global knowledge for data construction, and iterative tuning that accelerates convergence through self verification. This framework enables cost-effective model adaptation to specific textual KBs, without human supervision or external model assistance. Experiments demonstrate that KBAlign can achieve 90% of the performance gain obtained through GPT-4-supervised adaptation, while relying entirely on self-annotation of much smaller models. KBAlign significantly improves downstream QA accuracy across multiple domains with tiny costs, particularly benefiting scenarios requiring deep knowledge integration from specialized corpora. We release our experimental data, models, and process analyses to the community for further exploration(https://anonymous.4open.science/r/KBAlign-D160).

In-Context Learning (ICL) is an essential emergent ability of Large Language Models (LLMs), and recent studies introduce CoT to exemplars of ICL to enhance the reasoning capability, especially in mathematics tasks. However, given the continuous advancement of model capabilities, it remains unclear whether CoT exemplars still benefit recent, stronger models in such tasks. Through systematic experiments, we find that for recent strong models such as the Qwen2.5 series, adding traditional CoT exemplars does not improve reasoning performance compared to Zero-Shot CoT. Instead, their primary function is to align the output format with human expectations. We further investigate the effectiveness of enhanced CoT exemplars, constructed using answers from advanced models such as Qwen2.5-Max and DeepSeek-R1. Experimental results indicate that these enhanced exemplars still fail to improve the model’s reasoning performance. Further analysis reveals that models tend to ignore the exemplars and focus primarily on the instructions, leading to no observable gain in reasoning ability. Overall, our findings highlight the limitations of the current ICL+CoT framework in mathematical reasoning, calling for a re-examination of the ICL paradigm and the definition of exemplars.

Recent advancements in Large Language Models (LLMs) have shown outstanding potential for role-playing applications. Evaluating these capabilities is becoming crucial yet remains challenging. Existing benchmarks mostly adopt a character-centric approach, simplify user-character interactions to isolated Q&A tasks, and fail to reflect real-world applications. To address this limitation, we introduce RMTBench, a comprehensive user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds. RMTBench includes custom characters with detailed backgrounds and abstract characters defined by simple traits, enabling evaluation across various user scenarios. Our benchmark constructs dialogues based on explicit user motivations rather than character descriptions, ensuring alignment with practical user applications. Furthermore, we construct an authentic multi-turn dialogue simulation mechanism. With carefully selected evaluation dimensions and LLM-based scoring, this mechanism captures the complex intention of conversations between the user and the character. By shifting focus from character background to user intention fulfillment, RMTBench bridges the gap between academic evaluation and practical deployment requirements, offering a more effective framework for assessing role-playing capabilities in LLMs. All code and datasets will be released soon.

Large Language Models (LLMs) are powerful but prone to hallucinations due to static knowledge. Retrieval-Augmented Generation (RAG) helps by injecting external information, but current methods often are costly, generalize poorly, or ignore the model’s internal knowledge.In this paper, we introduce Smart-Searcher, a novel framework designed to train LLMs to adaptively leverage both internal and external knowledge sources. Smart-Searcher employs a two-stage training strategy: an initial SFT Cold-start phase for preliminary format learning, followed by RL for Dynamic Knowledge Acquisition. The RL stage uses outcome-supervision to encourage exploration, incorporates a reward mechanism for internal knowledge utilization, and integrates a memorization mechanism to continuously assimilate retrieved information, thereby enriching the model’s internal knowledge. By leveraging internal knowledge and external search engine, the model continuously improves its capabilities, enabling efficient retrieval-augmented reasoning.Our experiments demonstrate that Smart-Searcher outperforms previous RAG and reasoning methods and achieves efficient retrieval.The code is available at https://github.com/RUCAIBox/R1-Searcher-plus.

Grounding large language models (LLMs) in external knowledge sources is a promising method for faithful prediction. While existing grounding approaches work well for simple queries, many real-world information needs require synthesizing multiple pieces of evidence. We introduce “integrative grounding” – the challenge of retrieving and verifying multiple inter-dependent pieces of evidence to support a hypothesis query. To systematically study this problem, we repurpose data from four domains for evaluating integrative grounding capabilities. Our investigation reveals two critical findings: First, in groundedness verification, while LLMs are robust to redundant evidence, they tend to rationalize using internal knowledge when information is incomplete. Second, in examining retrieval planning strategies, we find that undirected planning can degrade performance through noise introduction, while premise abduction emerges as a promising approach due to its logical constraints. Additionally, LLMs’ zero-shot self-reflection capabilities consistently improve grounding quality. These insights provide valuable direction for developing more effective integrative grounding systems.

The ability of critique is vital for models to self-improve and serve as reliable AI assistants. While extensively studied in language-only settings, multimodal critique of Large Multimodal Models (LMMs) remains underexplored despite their growing capabilities in tasks like captioning and visual reasoning. In this work, we introduce e MM-CRITIC, a holistic benchmark for evaluating the critique ability of LMMs across multiple dimensions: basic, correction, and comparison. Covering 8 main task types and over 500 tasks, e MM-CRITIC collects responses from various LMMs with different model sizes and is composed of 4471 samples. To enhance the evaluation reliability, we integrate expert-informed ground answers into scoring rubrics that guide GPT-4o in annotating responses and generating reference critiques, which serve as anchors for trustworthy judgments. Extensive experiments validate the effectiveness of e MM-CRITIC and provide a comprehensive assessment of leading LMMs’ critique capabilities under multiple dimensions. Further analysis reveals some key insights, including the correlation between response quality and critique, and varying critique difficulty across evaluation dimensions. Our code is available at https://github.com/MichealZeng0420/MM-Critic.

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On the Correspondence between the Squared Norm and Information Content in Text Embeddings
Enrique Amigo | Adrian Ghajari | Alejandro Benito-Santos | Diego De La Fuente Rodríguez

Previous work has reported both empirical and theoretical evidence, for specific training models, of the correspondence between the squared norm of an embedding and the information content of the text it represents.In this paper, we investigate the relationship at the theoretical and empirical levels, focusing on the mechanisms and composition functions used to combine token embeddings. i) We formally derive two sufficient theoretical conditions for this correspondence to hold in embedding models. ii) We empirically examine the correspondence and the validity of these conditions at the word level for both static and contextual embeddings and different subword token composition mechanisms.iii) Building on Shannon’s Constant Entropy Rate (CER) principle, we explore whether embedding mechanisms exhibit a linearly monotonic increase in information content as text length increases.Our formal analysis and experiments reveal that:i) At the word embedding level, models satisfy the sufficient conditions and show a strong correspondence when certain subword composition functions are applied.ii) Only scaled embedding averages proposed in this paper and certain information-theoretic composition functions preserve the correspondence. Some non-compositional representations—such as the CLS token in BERT or the EOS token in LLaMA—tend to converge toward a fixed point. The CLS token in ModernBERT, however, exhibits behavior that aligns more closely with the CER hypothesis.

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Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training
Fenghua Weng | Jian Lou | Jun Feng | Minlie Huang | Wenjie Wang

Safety alignment is critical in pre-trained large language models (LLMs) to generate responses aligned with human values and refuse harmful queries. Unlike LLM, the current safety alignment of VLMs is often achieved with post-hoc safety fine-tuning. However, these methods are less effective to white-box attacks. To address this, we propose Adversary-aware DPO (ADPO), a novel training framework that explicitly considers adversary. Adversary-aware DPO (ADPO) integrates adversarial training into DPO to enhance the safety alignment of VLMs under worst-case adversarial perturbations. ADPO introduces two key components: (1) an adversarial-trained reference model that generates human-preferred responses under worst-case perturbations, and (2) an adversary-aware DPO loss that generates winner-loser pairs accounting for adversarial distortions. By combining these innovations, ADPO ensures that VLMs remain robust and reliable even in the presence of sophisticated jailbreak attacks. Extensive experiments demonstrate that ADPO outperforms baselines in terms of both safety alignment and general utility of VLMs.

Link prediction in knowledge graphs (KGs) requires integrating structural information and semantic context to infer missing entities. While large language models (LLMs) offer strong generative reasoning capabilities, their limited exploitation of structural signals often results in *structural sparsity* and *semantic ambiguity*, especially under incomplete or zero-shot settings. To address these challenges, we propose **SLiNT** (**S**tructure-aware **L**anguage model with **I**njection and co**N**trastive **T**raining), a modular framework that injects KG-derived structural context into a frozen LLM backbone with lightweight LoRA-based adaptation for robust link prediction. Specifically, **Structure-Guided Neighborhood Enhancement (SGNE)** retrieves pseudo-neighbors to enrich sparse entities and mitigate missing context; **Dynamic Hard Contrastive Learning (DHCL)** introduces fine-grained supervision by interpolating hard positives and negatives to resolve entity-level ambiguity; and **Gradient-Decoupled Dual Injection (GDDI)** performs token-level structure-aware intervention while preserving the core LLM parameters. Experiments on WN18RR and FB15k-237 show that SLiNT achieves superior or competitive performance compared with both embedding-based and generation-based baselines, demonstrating the effectiveness of structure-aware representation learning for scalable knowledge graph completion.

KV Cache is commonly used to accelerate LLM inference with long contexts, yet its high memory demand drives the need for cache compression. Existing compression methods, however, are largely heuristic and lack dynamic budget allocation. To address this limitation, we introduce a unified framework for cache compression by minimizing information loss in Transformer residual streams. Building on it, we analyze the layer attention output loss and derive a new metric to compare cache entries across heads, enabling layer-wise compression with dynamic head budgets. Additionally, by contrasting cross-layer information, we also achieve dynamic layer budgets. LAVa is the first unified strategy for cache eviction and dynamic budget allocation that, unlike prior methods, does not rely on training or the combination of multiple strategies. Experiments with four benchmarks (LongBench, Needle-In-A-Haystack, Ruler, and InfiniteBench) demonstrate its superiority over strong baselines. Moreover, our experiments reveal a new insight: dynamic layer budgets are crucial for generation tasks (e.g., code completion), while dynamic head budgets play a key role in extraction tasks (e.g., extractive QA). As a fully dynamic compression method, LAVa consistently maintains top performance across task types.

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LoRA-PAR: A Flexible Dual-System LoRA Partitioning Approach to Efficient LLM Fine-Tuning
Yining Huang | Bin Li | Keke Tang | Meilian Chen

Large-scale generative models like DeepSeek-R1 and OpenAI-O1 benefit substantially from chain-of-thought (CoT) reasoning, yet pushing their performance typically requires vast data, large model sizes, and full-parameter fine-tuning. While parameter-efficient fine-tuning (PEFT) helps reduce cost, most existing approaches primarily address domain adaptation or layer-wise allocation rather than explicitly tailoring data and parameters to different response demands. Inspired by “Thinking, Fast and Slow,” which characterizes two distinct modes of thought—System 1 (fast, intuitive, often automatic) and System 2 (slower, more deliberative and analytic)—we draw an analogy that different “subregions” of an LLM’s parameters might similarly specialize for tasks that demand quick, intuitive responses versus those requiring multi-step logical reasoning. Therefore, we propose LoRA-PAR, a dual-system LoRA framework that partitions both data and parameters by System 1 or System 2 demands, using fewer yet more focused parameters for each task. Specifically, we classify task data via multi-model role-playing and voting, and partition parameters based on importance scoring, then adopt a two-stage fine-tuning strategy of training System 1 tasks with supervised fine-tuning (SFT) to enhance knowledge and intuition and refine System 2 tasks with reinforcement learning (RL) to reinforce deeper logical deliberation next. Extensive experiments show that the two-stage fine-tuning strategy, SFT and RL, lowers active parameter usage while matching or surpassing SOTA PEFT baselines.

Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information retrieval. However, existing approaches face critical limitations that lack high-quality training trajectories or suffer from the distributional mismatches in simulated environments and prohibitive computational costs for real-world deployment. This paper introduces SimpleDeepSearcher, a lightweight yet effective framework that bridges this gap through strategic data engineering rather than complex training paradigms. Our approach synthesizes high-quality training data by simulating realistic user interactions in live web search environments, coupled with a multi-criteria curation strategy that optimizes the diversity and quality of input and output side. Experiments on five benchmarks across diverse domains demonstrate that SFT on only 871 curated samples yields significant improvements over RL-based baselines. Our work establishes SFT as a viable pathway by systematically addressing the data-scarce bottleneck, offering practical insights for efficient deep search systems. Our anonymous code is available at https://github.com/RUCAIBox/SimpleDeepSearcher

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LLaVE: Large Language and Vision Embedding Models with Hardness-Weighted Contrastive Learning
Zhibin Lan | Liqiang Niu | Fandong Meng | Jie Zhou | Jinsong Su

Universal multimodal embedding models play a critical role in tasks such as interleaved image-text retrieval, multimodal RAG, and multimodal clustering. However, our empirical results indicate that existing LMM-based embedding models trained with the standard InfoNCE loss exhibit a high degree of overlap in similarity distribution between positive and negative pairs, making it challenging to distinguish hard negative pairs effectively. To deal with this issue, we propose a simple yet effective framework that dynamically improves the embedding model’s representation learning for negative pairs based on their discriminative difficulty. Within this framework, we train a series of models, named LLaVE, and evaluate them on the MMEB benchmark, which covers 4 meta-tasks and 36 datasets. Experimental results show that LLaVE establishes stronger baselines that achieve state-of-the-art (SOTA) performance while demonstrating strong scalability and efficiency. Specifically, LLaVE-2B surpasses the previous SOTA 7B models, while LLaVE-7B achieves a further performance improvement of 6.2 points. Although LLaVE is trained on image-text data, it can generalize to text-video retrieval tasks in a zero-shot manner and achieve strong performance, demonstrating its remarkable potential for transfer to other embedding tasks.

Existing pretraining data mixing methods for large language models (LLMs) typically follow a domain-wise methodology, a top-down process that first determines domain weights and then performs uniform data sampling across each domain. However, these approaches neglect significant inter-domain overlaps and commonalities, failing to control the global diversity of the constructed training dataset. Further, uniform sampling within domains ignores fine-grained sample-specific features, potentially leading to suboptimal data distribution. To address these shortcomings, we propose a novel sample-wise data mixture approach based on a bottom-up paradigm. This method performs global cross-domain sampling by systematically evaluating the quality and diversity of each sample, thereby dynamically determining the optimal domain distribution. Comprehensive experiments across multiple downstream tasks and perplexity assessments demonstrate that SampleMix surpasses existing domain-based methods. Meanwhile, SampleMix requires 1.4x to 2.1x fewer training steps to achieve the baselines’ performance, highlighting the substantial potential of SampleMix to optimize pre-training data.

Recent advances in test-time scaling of large language models (LLMs), exemplified by DeepSeek-R1 and OpenAI’s o1, show that extending the chain of thought during inference can significantly improve general reasoning performance. However, the impact of this paradigm on legal reasoning remains insufficiently explored. To address this gap, we present the first systematic evaluation of 12 LLMs, including both reasoning-focused and general-purpose models, across 17 Chinese and English legal tasks spanning statutory and case-law traditions. In addition, we curate a bilingual chain-of-thought dataset for legal reasoning through distillation from DeepSeek-R1 and develop Legal-R1, an open-source model specialized for the legal domain. Experimental results show that Legal-R1 delivers competitive performance across diverse tasks. DeepSeek-R1 exhibits clear advantages in Chinese legal reasoning, while OpenAI’s o1 achieves comparable results on English tasks. We further conduct a detailed error analysis, which reveals recurring issues such as outdated legal knowledge, limited capacity for legal interpretation, and susceptibility to factual hallucinations. These findings delineate the main obstacles confronting legal-domain LLMs and suggest promising directions for future research. We release the dataset and model at https://github.com/YinghaoHu/Legal-R1-14B.

Large Language Model (LLM) agents are transforming education by automating complex pedagogical tasks and enhancing both teaching and learning processes. In this survey, we present a systematic review of recent advances in applying LLM agents to address key challenges in educational settings, such as feedback comment generation, curriculum design, etc. We analyze the technologies enabling these agents, including representative datasets, benchmarks, and algorithmic frameworks. Additionally, we highlight key challenges in deploying LLM agents in educational settings, including ethical issues, hallucination and overreliance, and integration with existing educational ecosystems. Beyond the core technical focus, we include in Appendix A a comprehensive overview of domain-specific educational agents, covering areas such as science learning, language learning, and professional development.

As the utilization of language models in interdisciplinary, human-centered studies grow, expectations of their capabilities continue to evolve. Beyond excelling at conventional tasks, models are now expected to perform well on user-centric measurements involving confidence and human (dis)agreement- factors that reflect subjective preferences. While modeling subjectivity plays an essential role in cognitive science and has been extensively studied, its investigation at the intersection with NLP remains under-explored. In light of this gap, we explore how language models can quantify subjectivity in cognitive appraisal by conducting comprehensive experiments and analyses with both fine-tuned models and prompt-based large language models (LLMs). Our quantitative and qualitative results demonstrate that personality traits and demographic information are critical for measuring subjectivity, yet existing post-hoc calibration methods often fail to achieve satisfactory performance. Furthermore, our in-depth analysis provides valuable insights to guide future research at the intersection of NLP and cognitive science.

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Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness
Lotem Peled-Cohen | Maya Zadok | Nitay Calderon | Hila Gonen | Roi Reichart

Cognitive decline often surfaces in language years before diagnosis. It is frequently non-experts, such as those closest to the patient, who first sense a change and raise concern. As LLMs become integrated into daily communication and used over prolonged periods, it may even be an LLM that notices something is off. But what exactly do they notice–and should be noticing–when making that judgment? This paper investigates how dementia is perceived through language by non-experts. We presented transcribed picture descriptions to non-expert humans and LLMs, asking them to intuitively judge whether each text was produced by someone healthy or with dementia. We introduce an explainable method that uses LLMs to extract high-level, expert-guided features representing these picture descriptions, and use logistic regression to model human and LLM perceptions and compare with clinical diagnoses. Our analysis reveals that human perception of dementia is inconsistent and relies on a narrow, and sometimes misleading, set of cues. LLMs, by contrast, draw on a richer, more nuanced feature set that aligns more closely with clinical patterns. Still, both groups show a tendency toward false negatives, frequently overlooking dementia cases. Through our interpretable framework and the insights it provides, we hope to help non-experts better recognize the linguistic signs that matter.

Large Vision-Language Models (LVLMs) suffer from serious hallucination problems, where the model-generated responses are inconsistent with the visual inputs. Existing hallucination mitigation methods are mainly based on preference alignment and require external human annotations or auxiliary models for preference data collection, which increase costs and limit sustainable improvement. To tackle these challenges, we propose Autonomous Preference Alignment via Self-Injection (APASI), a novel and generalizable method that mitigates hallucinations without external dependencies. APASI leverages the target LVLM to self-inject hallucinations into a generated response, creating a pair of responses with varying preference levels. During the self-injection process, the dis-preferred response is generated based on three key observations of hallucinations, ensuring it simulates real hallucination patterns. This fidelity offers an accurate learning signal for hallucination mitigation. Moreover, APASI incorporates an iterative alignment training strategy combined with curriculum learning to periodically update the preference data with increasing challenge, enabling stable and continuous enhancement of the LVLM. Extensive experiments across six benchmarks show that APASI not only effectively mitigates hallucinations for three baseline models but also achieves comparable or even superior performance to alignment-based methods with external dependency, thereby demonstrating its effectiveness and generalization capability.

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How Much Do Large Language Models Know about Human Motion? A Case Study in 3D Avatar Control
Kunhang Li | Jason Naradowsky | Yansong Feng | Yusuke Miyao

We explore the human motion knowledge of Large Language Models (LLMs) through 3D avatar control. Given a motion instruction, we prompt LLMs to first generate a high-level movement plan with consecutive steps (**High-level Planning**), then specify body part positions in each step (**Low-level Planning**), which we linearly interpolate into avatar animations. Using 20 representative motion instructions that cover fundamental movements and balance body part usage, we conduct comprehensive evaluations, including human and automatic scoring of both high-level movement plans and generated animations, as well as automatic comparison with oracle positions in low-level planning. Our findings show that LLMs are strong at interpreting high-level body movements but struggle with precise body part positioning. While decomposing motion queries into atomic components improves planning, LLMs face challenges in multi-step movements involving high-degree-of-freedom body parts. Furthermore, LLMs provide reasonable approximations for general spatial descriptions, but fall short in handling precise spatial specifications. Notably, LLMs demonstrate promise in conceptualizing creative motions and distinguishing culturally specific motion patterns.

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The Search for Conflicts of Interest: Open Information Extraction in Scientific Publications
Garima Gaur | Oana Balalau | Ioana Manolescu | Prajna Upadhyay

A conflict of interest (COI) appears when a person or a company has two or more interests that may directly conflict. This happens, for instance, when a scientist whose research is funded by a company audits the same company. For transparency and to avoid undue influence, public repositories of relations of interest are increasingly recommended or mandated in various domains, and can be used to avoid COIs. In this work, we propose an LLM-based open information extraction (OpenIE) framework for extracting financial or other types of interesting relations from scientific text. We target scientific publications in which authors declare funding sources or collaborations in the acknowledgment section, in the metadata, or in the publication, following editors’ requirements. We introduce an extraction methodology and present a knowledge base (KB) with a comprehensive taxonomy of COI centric relations. Finally, we perform a comparative study of disclosures of two journals in the field of toxicology and pharmacology.

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On Collaborating Small and Large Models For Few-shot Intent Detection
Peng Chen | Bang Wang

Few-shot intent detection (FSID) targets the classification of user queries into in-scope intent categories or detecting them as out-of-scope, with only a few or even zero labeled examples per class. Existing PLM-based methods struggle in low-resource situations; while LLM-based methods face high inference cost and label interference. To harness their complementary strengths, we propose the FCSLM, a framework that collaborates a small prediction model with a large language model for the FSID task. During training, we leverage LLMs for data augmentation in self-supervised pretraining and supervised fine-tuning a task-specific prediction model. During inference, a multi-round reasoning process first applies the small prediction model to output candidate intents with uncertainty estimations, then invokes an LLM with enriched intent descriptions for refined prediction and OOS detection. Extensive experiments on three benchmark datasets demonstrate that our FCSLM outperforms strong competitors, achieving the new state-of-the-art performance in both intent classification and OOS detection. Our code is available at: https://github.com/hustchenpeng/FCSLM

Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. We create a novel taxonomy of LLMs for story generation consisting of two major paradigms: (i) independent story generation by an LLM, and (ii) author-assistance for story generation – a collaborative approach with LLMs supporting human authors. We compare existing works based on their methodology, datasets, generated story types, evaluation methods, and LLM usage. With a comprehensive survey, we identify potential directions for future work

Drug repurposing plays a critical role in accelerating treatment discovery, especially for complex and rare diseases. Biomedical knowledge graphs (KGs), which encode rich clinical associations, have been widely adopted to support this task. However, existing methods largely overlook common-sense biomedical concept knowledge in real-world labs, such as mechanistic priors indicating that certain drugs are fundamentally incompatible with specific treatments. To address this gap, we propose LLaDR, a Large Language Model-assisted framework for Drug Repurposing, which improves the representation of biomedical concepts within KGs. Specifically, we extract semantically enriched treatment-related textual representations of biomedical entities from large language models (LLMs) and use them to fine-tune knowledge graph embedding (KGE) models. By injecting treatment-relevant knowledge into KGE, LLaDR largely improves the representation of biomedical concepts, enhancing semantic understanding of under-studied or complex indications. Experiments based on benchmarks demonstrate that LLaDR achieves state-of-the-art performance across different scenarios, with case studies on Alzheimer’s disease further confirming its robustness and effectiveness.

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SKRAG: A Retrieval-Augmented Generation Framework Guided by Reasoning Skeletons over Knowledge Graphs
Xiaotong Xu | Yizhao Wang | Yunfei Liu | Shengyang Li

In specialized domains such as space science and utilization, question answering (QA) systems are required to perform complex multi-fact reasoning over sparse knowledge graphs (KGs). Existing KG-based retrieval-augmented generation (RAG) frameworks often face challenges such as inefficient subgraph retrieval, limited reasoning capabilities, and high computational costs. These issues limit their effectiveness in specialized domains. In this paper, we propose SKRAG, a novel Skeleton-guided RAG framework for knowledge graph question answering (KGQA). SKRAG leverages a lightweight language model enhanced with the Finite State Machine (FSM) constraint to produce structurally grounded reasoning skeletons, which guide accurate subgraph retrieval. The retrieved subgraph is then used to prompt a general large language model (LLM) for answer generation. We also introduce SSUQA, a KGQA dataset in the space science and utilization domain. Experiments show that SKRAG outperforms strong baselines on SSUQA and two general-domain benchmarks, demonstrating its adaptability and practical effectiveness.

Formulating information retrieval as a variant of generative modeling, specifically using autoregressive models to generate relevant identifiers for a given query, has recently attracted considerable attention. However, its application to personalized sticker retrieval remains largely unexplored and presents unique challenges: existing relevance-based generative retrieval methods typically lack personalization, leading to a mismatch between diverse user expectations and the retrieved results. To address this gap, we propose PEARL, a novel generative framework for personalized sticker retrieval, and make two key contributions: (i) To encode user-specific sticker preferences, we design a representation learning model to learn discriminative user representations. It is trained on three prediction tasks that leverage personal information and click history; and (ii) To generate stickers aligned with a user’s query intent, we propose a novel intent-aware learning objective that prioritizes stickers associated with higher-ranked intents. Empirical results from both offline evaluations and online tests demonstrate that PEARL significantly outperforms state-of-the-art methods.

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Bridging Semantic and Modality Gaps in Zero-Shot Captioning via Retrieval from Synthetic Data
Zhiyue Liu | Wenkai Zhou

Zero-shot image captioning, which aims to generate image descriptions without relying on annotated data, has recently attracted increasing research interest. Pre-trained text-to-image generation models enable the creation of synthetic pairs solely from text data, while existing methods fall short in mitigating the discrepancy caused by the inability of synthetic images to fully capture the semantics of the textual input, resulting in unreliable cross-modal correspondences. To address this, we propose a retrieval-based framework that leverages only existing synthetic image-text pairs as its search corpus to systematically bridge the gap when using synthetic data for captioning. For the semantic gap between a synthetic image and its input text, our framework retrieves supplementary visual features from similar synthetic examples and integrates them to refine the image embedding. Then, it extracts image-related textual descriptions to mitigate the modality gap during decoding. Moreover, we introduce a plug-and-play visual semantic module that detects visual entities, further facilitating the construction of semantic correspondences between images and text. Experimental results on benchmark datasets demonstrate that our method obtains state-of-the-art results.

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Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics
Yuriel Ryan | Rui Yang Tan | Kenny Tsu Wei Choo | Roy Ka-Wei Lee

Understanding humor is a core aspect of social intelligence, yet it remains a significant challenge for Large Multimodal Models (LMMs). We introduce PixelHumor, a benchmark dataset of 2,800 annotated multi-panel comics designed to evaluate LMMs’ ability to interpret multimodal humor and recognize narrative sequences. Experiments with state-of-the-art LMMs reveal substantial gaps: for instance, top models achieve only 61% accuracy in panel sequencing, far below human performance. This underscores critical limitations in current models’ integration of visual and textual cues for coherent narrative and humor understanding. By providing a rigorous framework for evaluating multimodal contextual and narrative reasoning, PixelHumor aims to drive the development of LMMs that better engage in natural, socially aware interactions.

We introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. It enables multi-turn conversation in Arabic and English and supports diverse medical imaging modalities, including radiology, CT, and histology. To train BiMediX2, we curate BiMed-V, an extensive Arabic-English bilingual healthcare dataset consisting of 1.6M samples of diverse medical interactions. This dataset supports a range of medical Large Language Model (LLM) and Large Multimodal Model (LMM) tasks, including multi-turn medical conversations, report generation, and visual question answering (VQA). We also introduce BiMed-MBench, the first Arabic-English medical LMM evaluation benchmark, verified by medical experts. BiMediX2 demonstrates excellent performance across multiple medical LLM and LMM benchmarks, achieving state-of-the-art results compared to other open-sourced models. On BiMed-MBench, BiMediX2 outperforms existing methods by over 9% in English and more than 20% in Arabic evaluations. Additionally, it surpasses GPT-4 by approximately 9% in UPHILL factual accuracy evaluations and excels in various medical VQA, report generation, and report summarization tasks. Our trained models, instruction set, and source code are available at - https://github.com/mbzuai-oryx/BiMediX2

Multi-agent systems (MAS) powered by large language models (LLMs) have shown potential in tackling multifaceted problems through advanced understanding and reasoning. However, they struggle to adapt to evolving task dependencies and to handle uncertainties, such as shifting priorities or unpredictable disruptions. These constraints undermine their ability to dynamically adjust long-term strategies and inter-agent collaboration. To address these challenges, we propose DeMAC, a Dynamic Environment-Aware Manager-Player Agents Coordination framework that enhances multi-agent coordination through long-term strategic planning. DeMAC uses a dynamically updated directed acyclic graph (DAG) and a Manager-Player Dual-Feedback mechanism to align strategic and operational decisions. Moreover, DeMAC enables agents to maintain collaboration and dynamically adapt to changing environmental conditions, outperforming traditional reinforcement learning and human-agent collaboration in the Overcooked simulation. Experimental results highlight DeMAC’s ability to tackle complex coordination tasks, demonstrating its potential to advance LLM-based MAS in dynamic, complex task dependency environments.

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Coherence of Argumentative Dialogue Snippets: A New Method for Large Scale Evaluation with an Application to Inference Anchoring Theory
Paul Piwek | Jacopo Amidei | Svetlana Stoyanchev

This paper introduces a novel method for testing the components of theories of (dialogue) coherence through utterance substitution. The method is described and then applied to Inference Anchoring Theory (IAT) in a large scale experimental study with 933 dialogue snippets and 87 annotators. IAT has been used for substantial corpus annotation and practical applications. To address the aim of finding out if and to what extent two aspects of IAT – illocutionary acts and propositional relations – contribute to dialogue coherence, we designed an experiment for systematically comparing the coherence ratings for several variants of short debate snippets. The comparison is between original human-human debate snippets, snippets generated with an IAT-compliant algorithm and snippets produced with ablated versions of the algorithm. This allows us to systematically compare snippets that have identical underlying structures as well as IAT-deficient structures with each other. We found that propositional relations do impact on dialogue coherence (at a statistically highly significant level) whereas we found no such effect for illocutionary act expression. This result suggests that fine-grained inferential relations impact on dialogue coherence, complementing the higher-level coherence structures of, for instance, Rhetorical Structure Theory.

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Angular Dispersion Accelerates k-Nearest Neighbors Machine Translation
Evgeniia Tokarchuk | Sergey Troshin | Vlad Niculae

Augmenting neural machine translation with external memory at decoding time, in the form of k-nearest neighbors machine translation (k-NN MT), is a well-established strategy for increasing translation performance. k-NN MT retrieves a set of tokens that occurred in the most similar contexts recorded in a prepared data store, using hidden state representations of translation contexts as vector lookup keys. One of the main disadvantages of this method is the high computational cost and memory requirements. Since an exhaustive search is not feasible in large data stores practitioners commonly use approximate k-NN lookup, yet even such algorithms are a bottleneck. In contrast to research directions seeking to accelerate k-NN MT by reducing data store size or the number of lookup calls, we pursue an orthogonal direction based on the performance properties of approximate k-NN lookup data structures. In particular, we propose encouraging angular dispersion of the neural hidden representations of contexts. We show that improving dispersion leads to better balance in the retrieval data structures, accelerating retrieval and slightly improving translations.

Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence. We propose CP-Bench, a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning the integration of verbal content with non-verbal cues like emotion and prosody. The benchmark includes two curated question answering (QA) datasets requiring both linguistic and empathetic understanding. We evaluate state-of-the-art speech-LLMs from both open and closed-source models and perform a comprehensive analysis across different question types. The top two models were further analyzed under temperature tuning to understand its effect on this task. Our benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent speech-capable LLMs.

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This is not a Disimprovement: Improving Negation Reasoning in Large Language Models via Prompt Engineering
Joshua Jose Dias Barreto | Abhik Jana

Negation reasoning remains a challenge for large language models (LLMs), often causing incorrect interpretations of negated statements. In this study, we analyze various LLMs for their handling of negation and propose two genres of prompts (*Warning-based* and *Persona-based*), which improve overall absolute accuracy by up to 3.17% and distractor negation accuracy by up to 25.14% over most competitive baselines. Next, we assess the robustness of LLMs by reordering prompts while preserving meaning, observing instability linked to positional encoding schemes. Further, we introduce a negative token attention score (NTAS) to quantify attention to negation words. From the comprehensive analysis, we point out that within a specific LLM family, the performance of a model (measured using accuracy) correlates more with NTAS than with model size. The code is publicly available: [https://github.com/Joshua-Dias-Barreto/This-is-not-a-Disimprovement](https://github.com/Joshua-Dias-Barreto/This-is-not-a-Disimprovement)

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Make Every Letter Count: Building Dialect Variation Dictionaries from Monolingual Corpora
Robert Litschko | Verena Blaschke | Diana Burkhardt | Barbara Plank | Diego Frassinelli

Dialects exhibit a substantial degree of variation due to the lack of a standard orthography. At the same time, the ability of Large Language Models (LLMs) to process dialects remains largely understudied. To address this gap, we use Bavarian as a case study and investigate the lexical dialect understanding capability of LLMs by examining how well they recognize and translate dialectal terms across different parts-of-speech. To this end, we introduce DiaLemma, a novel annotation framework for creating dialect variation dictionaries from monolingual data only, and use it to compile a ground truth dataset consisting of 100K human-annotated German-Bavarian word pairs. We evaluate how well nine state-of-the-art LLMs can judge Bavarian terms as dialect translations, inflected variants, or unrelated forms of a given German lemma. Our results show that LLMs perform best on nouns and lexically similar word pairs, and struggle most in distinguishing between direct translations and inflected variants. Interestingly, providing additional context in the form of example usages improves the translation performance, but reduces their ability to recognize dialect variants. This study highlights the limitations of LLMs in dealing with orthographic dialect variation and emphasizes the need for future work on adapting LLMs to dialects.

Recent advancements in large language models (LLMs) have revolutionized natural language processing through their remarkable capabilities in understanding and executing diverse tasks. While supervised fine-tuning, particularly in Retrieval-Augmented Generation (RAG) scenarios, effectively enhances task-specific performance, it often leads to catastrophic forgetting, where models lose their previously acquired knowledge and general capabilities. Existing solutions either require access to general instruction data or face limitations in preserving the model’s original distribution. To overcome these limitations, we propose SelfAug, a self-distribution alignment method that aligns input sequence logits to preserve the model’s semantic distribution, thereby mitigating catastrophic forgetting and improving downstream performance. Extensive experiments demonstrate that SelfAug achieves a superior balance between downstream learning and general capability retention. Our comprehensive empirical analysis reveals a direct correlation between distribution shifts and the severity of catastrophic forgetting in RAG scenarios, highlighting how the absence of RAG capabilities in general instruction tuning leads to significant distribution shifts during fine-tuning. Our findings not only advance the understanding of catastrophic forgetting in RAG contexts but also provide a practical solution applicable across diverse fine-tuning scenarios.

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SEKE: Specialised Experts for Keyword Extraction
Matej Martinc | Thi Hong Hanh Tran | Senja Pollak | Boshko Koloski

Keyword extraction involves identifying the most descriptive words in a document, allowing automatic categorisation and summarisation of large quantities of diverse textual data. Relying on the insight that real-world keyword detection often requires handling of diverse content, we propose a novel supervised keyword extraction approach based on the mixture of experts (MoE) technique. MoE uses a learnable routing sub-network to direct information to specialised experts, allowing them to specialise in distinct regions of the input space. SEKE, a mixture of Specialised Experts for supervised Keyword Extraction, uses DeBERTa as the backbone model and builds on the MoE framework, where experts attend to each token, by integrating it with a bidirectional Long short-term memory (BiLSTM) network, to allow successful extraction even on smaller corpora, where specialisation is harder due to lack of training data. The MoE framework also provides an insight into inner workings of individual experts, enhancing the explainability of the approach. We benchmark SEKE on multiple English datasets, achieving state-of-the-art performance compared to strong supervised and unsupervised baselines. Our analysis reveals that depending on data size and type, experts specialise in distinct syntactic and semantic components, such as punctuation, stopwords, parts-of-speech, or named entities. Code is available at https://github.com/matejMartinc/SEKE_keyword_extraction.

Large Language Models (LLMs) have demonstrated remarkable proficiency in language comprehension and generation; however, their widespread adoption is constrained by substantial bandwidth and computational demands. While pruning and low-rank approximation have each demonstrated promising performance individually, their synergy for LLMs remains underexplored. We introduce Synergistic Sparse and Low-Rank Compression (SSLC) methods for LLMs, which leverages the strengths of both techniques: low-rank approximation compresses the model by retaining its essential structure with minimal information loss, whereas sparse optimization eliminates non-essential weights, preserving those crucial for generalization. Based on theoretical analysis, we first formulate the joint low-rank approximation and sparse optimization as a unified problem and solve it by iterative optimization algorithm. Experiments on LLaMA and Qwen2.5 models (7B-70B) show that SSLC, without any additional training steps, consistently surpasses standalone methods, achieving state-of-the-arts results. Notably, SSLC compresses Qwen2.5 by 50% with no performance drop and achieves at least 1.63× speedup, offering a practical solution for efficient LLM deployment.

Pre-training on large, high-quality datasets is essential for improving the reasoning abilities of Large Language Models (LLMs), particularly in specialized fields like mathematics. However, the field of Multimodal LLMs (MLLMs) lacks a comprehensive, open-source dataset for mathematical reasoning. To fill this gap, we present InfiMM-WebMath-40B, a high-quality dataset of interleaved image-text documents. It consists of 24 million web pages, 85 million image URLs, and 40 billion text tokens, all carefully extracted and filtered from CommonCrawl. We outline our data collection and processing pipeline in detail. Models trained on InfiMM-WebMath-40B demonstrate strong performance in both text-only and multimodal settings, setting a new state-of-the-art on multimodal math benchmarks such as MathVerse and We-Math.

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Zero-Shot Defense Against Toxic Images via Inherent Multimodal Alignment in LVLMs
Wei Zhao | Zhe Li | Yige Li | Jun Sun

Large Vision-Language Models (LVLMs) have made significant strides in multimodal comprehension, thanks to extensive pre-training and fine-tuning on large-scale visual datasets. However, despite their robust textual safety mechanisms, they remain vulnerable to harmful visual inputs. Existing safeguards—typically relying on pre-filtering or fine-tuning—incur high costs and diminish overall utility. To address this critical vulnerability, we introduce SafeCLIP, a lightweight method that leverages LVLMs’ inherent multimodal alignment for zero-shot toxic image detection. By projecting CLIP’s discarded CLS token into its text space and matching it with toxic descriptors, SafeCLIP detects harmful content without any architectural changes—adding minimal latency and enabling dynamic safety corrections during inference and fine-tuning. Experiments show that SafeCLIP achieves a 66.9% defense success rate with only 3.2% false positive rate and 7.2% overhead. In contrast, state-of-the-art methods achieve 52.9% success but have a 10.7% false positive rate and 210% overhead. Our work demonstrates that leveraging inherent multimodal alignment can yield efficient, low-cost LVLM safety. Code is available at anonymous.4open.science/r/safeclip-2C01.

This work investigates retrieval augmented generation as an efficient strategy for automatic context discovery in context-aware Automatic Speech Recognition (ASR) system, in order to improve transcription accuracy in the presence of rare or out-of-vocabulary terms. However, identifying the right context automatically remains an open challenge. This work proposes an efficient embedding-based retrieval approach for automatic context discovery in ASR. To contextualize its effectiveness, two alternatives based on large language models (LLMs) are also evaluated: (1) large language model (LLM)-based context generation via prompting, and (2) post-recognition transcript correction using LLMs. Experiments on the TED-LIUMv3, Earnings21 and SPGISpeech demonstrate that the proposed approach reduces WER by up to 17% (percentage difference) relative to using no-context, while the oracle context results in a reduction of up to 24.1%.

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pFedRAG: A Personalized Federated Retrieval-Augmented Generation System with Depth-Adaptive Tiered Embedding Tuning
Hangyu He | Xin Yuan | Kai Wu | Ren Ping Liu | Wei Ni

Large Language Models (LLMs) can undergo hallucinations in specialized domains, and standard Retrieval-Augmented Generation (RAG) often falters due to general-purpose embeddings ill-suited for domain-specific terminology. Though domain-specific fine-tuning enhances retrieval, centralizing data introduces privacy risks. The use of federated learning (FL) can alleviate this to some extent, but faces challenges of data heterogeneity, poor personalization, and expensive training data generation. We propose pFedRAG, a novel Personalized Federated RAG framework, which enables efficient collaborative fine-tuning of embedding models to address these challenges. The key contribution is a new Depth-Adaptive Tiered Embedding (DATE) architecture, which comprises a Global Shared Layer, combined using FL to capture common knowledge, and a Personalized Layer with adjustable depth tailored for local data and training results of each client. The depth is locally controlled based on crafted metrics and scoring criteria. Also, pFedRAG incorporates a fully client-side pipeline leveraging local small LLMs and vector database filtering to construct high-quality query-document pairs. Experiments on diverse medical non-IID document datasets demonstrate that pFedRAG significantly reduces communication costs, handles data heterogeneity, and improves retrieval performance. Human evaluations confirm the enhanced response quality of pFedRAG.

Recent advances in Chain-of-Thought (CoT) prompting have substantially improved the reasoning capabilities of Large Language Models (LLMs). However, these methods often suffer from overthinking, leading to unnecessarily lengthy or redundant reasoning traces. Existing approaches attempt to mitigate this issue through curating multiple reasoning chains for training LLMs, but their effectiveness is often constrained by the quality of the generated data and prone to overfitting. To address the challenge, we propose Reasoning Compression Through Stepwise Trials (ReCUT), a novel method aimed at balancing the accuracy and length of reasoning trajectory. Specifically, ReCUT employs a stepwise exploration mechanism and a long-short switched sampling strategy, enabling LLMs to incrementally generate diverse reasoning paths. These paths are evaluated and used to construct preference pairs to train two specialized models (Gemini LLMs)—one optimized for reasoning accuracy, the other for shorter reasoning. A final integrated model is obtained by interpolating the parameters of these two models. Experimental results across multiple math reasoning datasets and backbone models demonstrate that ReCUT significantly reduces reasoning lengths by approximately 30-50%, while maintaining or improving reasoning accuracy compared to various baselines. All codes and data will be released via https://github.com/NEUIR/ReCUT.

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CURE: Controlled Unlearning for Robust Embeddings — Mitigating Conceptual Shortcuts in Pre-Trained Language Models
Aysenur Kocak | Shuo Yang | Bardh Prenkaj | Gjergji Kasneci

Pre-trained language models have achieved remarkable success across diverse applications but remain susceptible to spurious, concept-driven correlations that impair robustness and fairness. In this work, we introduce CURE, a novel and lightweight framework that systematically disentangles and suppresses conceptual shortcuts while preserving essential content information. Our method first extracts concept-irrelevant representations via a dedicated content extractor reinforced by a reversal network, ensuring minimal loss of task-relevant information. A subsequent controllable debiasing module employs contrastive learning to finely adjust the influence of residual conceptual cues, enabling the model to either diminish harmful biases or harness beneficial correlations as appropriate for the target task. Evaluated on the IMDB and Yelp datasets using three pre-trained architectures, CURE achieves an absolute improvement of +10 points in F1 score on IMDB and +2 points on Yelp, while introducing minimal computational overhead. Our approach establishes a flexible, unsupervised blueprint for combating conceptual biases, paving the way for more reliable and fair language understanding systems.

As machine learning (ML) application continues to expand across diverse fields, there is a rising demand for ML code generation. In this paper, we aim at a critical research question: Can machines autonomously generate ML code for sophisticated, human-designed algorithms or solutions? To answer this question, we introduce a novel benchmark, MLAlgo-Bench, which includes two challenging tasks: 1) Generating code for ML algorithms including both traditional ML and modern deep learning-based methods, and 2) Giving humans solution sketches, writing ML code for solving practical tasks in Kaggle competitions. This benchmark is unique in its focus on the challenges of interpreting intricate human instructions and producing multi-step, high-complexity code, offering a rigorous test for current Large Language Model (LLM) capabilities. We introduce an automatic evaluation framework with comprehensive metrics such as task pass rate, relative performance metric, and time overhead. Currently, the top-performing models (Claude3.5-Sonet) achieve a 48.8% task completion rate on realizing machine learning algorithms, and a 21.6% rate for completing Kaggle competitions. Further analysis suggests substantial room for improvement.

Text-Attributed Graphs (TAGs), which integrate text and graph structures, have recently gained traction, especially in web applications. However, as a graph structure, TAG representation learning (TAGRL) naturally inherits issues from Graph Neural Networks (GNNs), such as fairness. Moreover, previous TAGRL research has mainly focused on using LM-as-encoder to boost downstream task performance, with little consideration given to whether this process may raise additional concerns related to fairness and other safety-related issues. As the first work to explore fairness in TAGRL, this paper proposes the concept of evolving LM-as-encoder to LM-as-fair-encoder, developing a two-stage fairness-aware alignment process called FairTAG based on the observed issues. Specifically, we first mitigate the tendency of LMs to overfit to homophily during downstream tasks fine-tuning, followed by subgraph-level connection behavior preference optimization for selected anchor nodes. We provide theoretical support and demonstrate the feasibility of LM-as-fair-encoder through extensive experiments and ablation studies. We also show that FairTAG can be seamlessly integrated with fairness-enhancing strategies on the GNNs decoder side, thus innovatively constructing a plug-and-play learning framework.

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Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations
Zekun Wang | Jingjie Zeng | Yingxu Li | Liang Yang | Hongfei Lin

Large language models (LLMs) achieve remarkable performance across various domains, largely due to training on massive datasets. However, this also raises growing concerns over the exposure of sensitive and private information, making model unlearning increasingly critical.However, existing methods often struggle to balance effective forgetting with maintaining model utility. In this work, we propose HyperUnlearn, a human-inspired unlearning framework. We construct two types of fuzzy data—local and global—to simulate forgetting, and represent them in hyperbolic and Euclidean spaces, respectively. Unlearning is performed on a model with frozen early layers to isolate forgetting and preserve useful knowledge.Experiments demonstrate that HyperUnlearn effectively forgets sensitive content while maintaining the model’s language understanding, fluency, and benchmark performance, offering a practical trade-off between forgetting and capability preservation.

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Do Influence Functions Work on Large Language Models?
Zhe Li | Wei Zhao | Yige Li | Jun Sun

Influence functions are important for quantifying the impact of individual training data points on a model’s predictions. Although extensive research has been conducted on influence functions in traditional machine learning models, their application to large language models (LLMs) has been limited. In this work, we conduct a systematic study to address a key question: do influence functions work on LLMs? Specifically, we evaluate influence functions across multiple tasks and find that they consistently perform poorly in most settings. Our further investigation reveals that their poor performance can be attributed to: (1) inevitable approximation errors when estimating the iHVP component due to the scale of LLMs, (2) uncertain convergence during fine-tuning, and, more fundamentally, (3) the definition itself, as changes in model parameters do not necessarily correlate with changes in LLM behavior. Thus, our study suggests the need for alternative approaches for identifying influential samples.

Large language models (LLMs) are increasingly integral as productivity assistants, but existing benchmarks fall short in rigorously evaluating their real-world instruction-following capabilities. Current benchmarks often (i) lack sufficient multilinguality, (ii) fail to capture the implicit constraints inherent in user requests, and (iii) overlook the complexities of multi-turn dialogue. To address these critical gaps and provide a more realistic assessment, we introduce TRUEBench (Trustworthy Real-world Usage Evaluation Benchmark), a novel benchmark specifically designed for LLM-based productivity assistants. TRUEBench distinguishes itself by featuring input prompts across 12 languages, incorporating intra-instance multilingual instructions, employing rigorous evaluation criteria to capture both explicit and implicit constraints, and including complex multi-turn dialogue scenarios with both accumulating constraints and context switches. Furthermore, to ensure reliability in evaluation, we refined constraints using an LLM validator. Extensive experiments demonstrate that TRUEBench presents significantly greater challenges than existing benchmarks; for instance, a strong model like OpenAI o1 achieved only a 69.07% overall pass rate. TRUEBench offers a demanding and realistic assessment of LLMs in practical productivity settings, highlighting their capabilities and limitations.

Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy sequences of actions, single-agent approaches often face challenges related to inefficiency and limited fault tolerance. Despite these issues, research on multi-agent collaboration remains scarce. In this paper, we propose CausalMACE, a holistic causality planning framework designed to enhance multi-agent systems, in which we incorporate causality to manage dependencies among subtasks. Technically, our proposed framework introduces two modules: an overarching task graph for global task planning and a causality-based module for dependency management, where inherent rules are adopted to perform causal intervention. Experimental results demonstrate our approach achieves state-of-the-art performance in multi-agent cooperative tasks of Minecraft. The code will be open-sourced upon the acceptance of this paper.

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Harry Potter is Still Here! Probing Knowledge Leakage in Targeted Unlearned Large Language Models
Bang Trinh Tran To | Thai Le

This work presents LURK (Latent Unlearned Knowledge), a novel framework that probes for undesired knowledge retention in unlearned LLMs through adversarial suffix prompting. LURK automatically generates adversarial prompt suffixes designed to elicit residual knowledge about the Harry Potter domain, a commonly used benchmark for unlearning. Our experiments reveal that even models deemed successfully unlearned can leak idiosyncratic information under targeted adversarial conditions, highlighting critical limitations of current unlearning evaluation standards. By uncovering implicit knowledge through indirect probing, LURK offers a more rigorous and diagnostic tool for assessing the robustness of unlearning algorithms. Code and data will be available at https://github.com/Rachel1809/LURK.

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Learning Trajectories of Figurative Language for Pre-Trained Language Models
Nicola Arici | Luca Putelli | Ejdis Gjinika | Ivan Serina | Alfonso Gerevini

Figurative language and figures of speech, such as metaphors and hyperboles, are used every day in written and oral communication among human beings.Nonetheless, this imaginative use of words in a non literal way requires a solid understanding of semantics and a deep real-world knowledge.In the longstanding debate about whether Neural Language Models (NLMs) really have a full understanding of text, analysing how they can recognise figurative language can provide some intuition of their functioning, their capabilities and their limits.Therefore, in this paper, we exploit probing tasks to study how several NLMs of different sizes recognise four different figures of speech: hyperboles, metaphors, oxymorons and pleonasms. We analyse whether this information is learned and how it is acquired during the training of the model, describing its learning trajectory. Moreover, we analyse which layers have a better comprehension of figurative language and the influence of pre-training data. Datasets and code are available at https://github.com/nicolarici/learning-trajectories.

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BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion
Sike Xiang | Shuang Chen | Amir Atapour-Abarghouei

As multimodal large language models (MLLMs) advance, their large-scale architectures pose challenges for deployment in resource-constrained environments. In the age of large models, where energy efficiency, computational scalability and environmental sustainability are paramount, the development of lightweight and high-performance models is critical for real-world applications. As such, we propose a lightweight MLLM framework for end-to-end visual question answering. Our proposed approach centres on BreezeCLIP, a compact yet powerful vision-language encoder optimised for efficient multimodal understanding. With only 1.2 billion parameters overall, our model significantly reduces computational cost while achieving performance comparable to standard-size MLLMs. Experiments conducted on multiple datasets further validate its effectiveness in balancing accuracy and efficiency. The modular and extensible design enables generalisation to broader multimodal tasks. The proposed lightweight vision-language framework is denoted as BcQLM (BreezeCLIP-enhanced Q-Gated Multimodal Language Model). It offers a promising path toward deployable MLLMs under practical hardware constraints. The source code is available at https://github.com/thico0224/BcQLM.

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HapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals
Guimin Hu | Daniel Hershcovich | Hasti Seifi

Haptic signals, from smartphone vibrations to virtual reality touch feedback, can effectively convey information and enhance realism, but designing signals that resonate meaningfully with users is challenging. To facilitate this, we introduce a multimodal dataset and task, of matching user descriptions to vibration haptic signals, and highlight two primary challenges: (1) lack of large haptic vibration datasets annotated with textual descriptions as collecting haptic descriptions is time-consuming, and (2) limited capability of existing tasks and models to describe vibration signals in text.To advance this area, we create HapticCap, the first fully human-annotated haptic-captioned dataset, containing 92,070 haptic-text pairs for user descriptions of sensory, emotional, and associative attributes of vibrations. Based on HapticCap, we propose the haptic-caption retrieval task and present the results of this task from a supervised contrastive learning framework that brings together text representations within specific categories and vibrations. Overall, the combination of language model T5 and audio model AST yields the best performance in the haptic-caption retrieval task, especially when separately trained for each description category. The dataset is available at https://huggingface.co/datasets/GuiminHu/HapticCap.

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SubDocTrans: Enhancing Document-level Machine Translation with Plug-and-play Multi-granularity Knowledge Augmentation
Hanghai Hong | Yibo Xie | Jiawei Zheng | Xiaoli Wang

Large language models (LLMs) have recently achieved remarkable progress in sentence-level machine translation, but scaling to document-level machine translation (DocMT) remains challenging, particularly in modeling long-range dependencies and discourse phenomena across sentences and paragraphs. Document translations generated by LLMs often suffer from poor consistency, weak coherence, and omission errors. To address these issues, we propose SubDocTrans, a novel DocMT framework that enables LLMs to produce high-quality translations through plug-and-play, multi-granularity knowledge extraction and integration. SubDocTrans first performs topic segmentation to divide a document into coherent topic sub-documents. For each sub-document, both global and local knowledge are extracted including bilingual summary, theme, proper nouns, topics, and transition hint. We then incorporate this multi-granularity knowledge into the prompting strategy, to guide LLMs in producing consistent, coherent, and accurate translations. We conduct extensive experiments across various DocMT tasks, and the results demonstrate the effectiveness of our framework, particularly in improving consistency and coherence, reducing omission errors, and mitigating hallucinations.

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Social Bias Evaluation for Large Language Models Requires Prompt Variations
Rem Hida | Masahiro Kaneko | Naoaki Okazaki

Warning: This paper contains examples of stereotypes and biases. Large Language Models (LLMs) exhibit considerable social biases, and various studies have tried to evaluate and mitigate these biases accurately. Previous studies use downstream tasks to examine the degree of social biases for evaluation and mitigation. While the output of LLMs highly depends on prompts, prior works evaluating and mitigating bias have often relied on a limited variety of prompts. In this paper, we investigate the sensitivity of LLMs when changing prompt variations (task instruction, few-shot examples, debias-prompt) by analyzing task performance and social bias of LLMs. Our experimental results reveal that LLM rankings fluctuate across prompts for both task performance and social bias. We also confirmed that the impact of format changes can differ for each bias category. Performance improvement from prompt settings may not result in reduced bias. Moreover, the ambiguity of instances is a common factor in LLM sensitivity to prompts across advanced LLMs. We recommend using diverse prompts, as in this study, to compare the effects of prompts on social bias in LLMs.

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Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding
Taowen Liu | Marta Andronic | Deniz Gunduz | George Anthony Constantinides

LLM training is resource-intensive. Quantized training improves computational and memory efficiency but introduces quantization noise, which can hinder convergence and degrade model accuracy. Stochastic Rounding (SR) has emerged as a theoretically attractive alternative to deterministic rounding, offering unbiased gradient estimates. However, its interaction with other training factors—especially batch size—remains underexplored. In this paper, we present a theoretical and empirical study of mini-batch stochastic gradient descent (SGD) with SR, showing that increased batch sizes can compensate for reduced precision during backpropagation. Furthermore, we show that quantizing weights and activations impacts gradient variance in distinct ways. Our experiments validate these theoretical insights. Our experiments validate these theoretical insights.

Large language models (LLMs) have achieved remarkable success in generative tasks, yet they often fall short in ensuring the factual accuracy of their outputs thus limiting their reliability in real-world applications where correctness is critical. In this paper, we present FactReasoner, a novel neuro-symbolic based factuality assessment framework that employs probabilistic reasoning to evaluate the truthfulness of long-form generated responses. FactReasoner decomposes a response into atomic units, retrieves relevant contextual information from external knowledge sources, and models the logical relationships (e.g., entailment, contradiction) between these units and their contexts using probabilistic encodings. It then estimates the posterior probability that each atomic unit is supported by the retrieved evidence. Our experiments on both labeled and unlabeled benchmark datasets demonstrate that FactReasoner often outperforms state-of-the-art prompt-based methods in terms of factual precision and recall.

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Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA
Yuchen Wu | Liang Ding | Li Shen | Dacheng Tao

Large language models (LLMs) encode vast amounts of world knowledge but remain static once trained, making timely integration of emerging facts prohibitively expensive via full retraining. Knowledge-editing techniques have thus emerged to inject or overwrite specific facts into LLMs, yet they either over-rely on superficial cues or incur complex, iterative pipelines that collapse under noisy, multi-hop conditions. We introduce **Reason-KE**, an end-to-end reasoning-chain-based editing framework that steers a pretrained LLM through four structured stages—fact acknowledgment, relevance determination, selective application, and final reasoning—to filter distractors in a single pass. Trained on MQuAKE-CF with up to four irrelevant facts, Reason-KE elevates Qwen2.5-7B’s multi-hop QA accuracy to 90.2% (↑17.6 pp) while suffering merely 6.3% drop under heavy distraction and <1% when answers are leaked. Our quantitative analysis confirms Reason-KE’s resilience and efficiency, establishing a new state of the art for reliable LLM knowledge updates. The code will be released.

The rapid advancements in large language models (LLMs) have led to the emergence of routing techniques, which aim to efficiently select the optimal LLM from diverse candidates to tackle specific tasks, optimizing performance while reducing costs. Current LLM routing methods are limited in effectiveness due to insufficient exploration of the intrinsic connection between user queries and the characteristics of LLMs. To address this issue, in this paper, we present **RadialRouter**, a novel framework for LLM routing which employs a lightweight Transformer-based backbone with a radial structure named **RadialFormer** to articulate the query-LLMs relationship. The optimal LLM selection is performed based on the final states of RadialFormer. The pipeline is further refined by an objective function that combines Kullback-Leibler divergence with the query-query contrastive loss to enhance robustness. Experimental results on RouterBench show that RadialRouter significantly outperforms existing routing methods by 9.2% and 5.8% in the *Balance* and *Cost First* scenarios, respectively. Additionally, its adaptability toward different performance-cost trade-offs and the dynamic LLM pool demonstrates practical application potential.

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Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Language Models
Wataru Hashimoto | Hidetaka Kamigaito | Taro Watanabe

Decoding strategies manipulate the probability distribution underlying the output of a language model and can therefore affect both generation quality and its uncertainty. In this study, we investigate the impact of decoding strategies on uncertainty estimation in Large Language Models (LLMs). Our experiments show that Contrastive Search, which mitigates repetition, yields better uncertainty estimates on average across a range of preference-aligned LLMs. In contrast, the benefits of these strategies sometimes diverge when the model is only post-trained with supervised fine-tuning, i.e. without explicit alignment.

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Elucidating Mechanisms of Demographic Bias in LLMs for Healthcare
Hiba Ahsan | Arnab Sen Sharma | Silvio Amir | David Bau | Byron C Wallace

We know from prior work that LLMs encode social biases, and that this manifests in clinical tasks. In this work we adopt tools from mechanistic interpretability to unveil sociodemographic representations and biases within LLMs in the context of healthcare. Specifically, we ask: Can we identify activations within LLMs that encode sociodemographic information (e.g., gender, race)? We find that, in three open weight LLMs, gender information is highly localized in MLP layers and can be reliably manipulated at inference time via patching. Such interventions can surgically alter generated clinical vignettes for specific conditions, and also influence downstream clinical predictions which correlate with gender, e.g., patient risk of depression. We find that representation of patient race is somewhat more distributed, but can also be intervened upon, to a degree. To our knowledge, this is the first application of mechanistic interpretability methods to LLMs for healthcare.

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Can You Trick the Grader? Adversarial Persuasion of LLM Judges
Yerin Hwang | Dongryeol Lee | Taegwan Kang | Yongil Kim | Kyomin Jung

As large language models (LLMs) take on growing roles as automated evaluators in practical settings, a critical question arises: Can individuals persuade an LLM judge to assign unfairly high scores? This study is the first to reveal that strategically embedded persuasive language can bias LLM judges when scoring mathematical reasoning tasks, where correctness should be independent of stylistic variation. Grounded in Aristotle’s rhetorical principles, we formalize seven persuasion techniques (Majority, Consistency, Flattery, Reciprocity, Pity, Authority, Identity) and embed them into otherwise identical responses. Across six math benchmarks, we find that persuasive language leads LLM judges to assign inflated scores to incorrect solutions, by up to 8% on average, with Consistency causing the most severe distortion. Notably, increasing model size does not substantially mitigate this vulnerability. Further analysis demonstrates that combining multiple persuasion techniques amplifies the bias, and pairwise evaluation is likewise susceptible. Moreover, the persuasive effect persists under counter-prompting strategies, highlighting a critical vulnerability in LLM-as-a-Judge pipelines and underscoring the need for robust defenses against persuasion-based attacks.

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Navigating the Unknown: Intent Classification and Out-of-Distribution Detection Using Large Language Models
Yusuf Sali | Sıtkı Can Toraman

Out-of-Distribution (OOD) detection is a challenging task that requires great generalization capability for the practicality and safety of task-oriented dialogue systems (TODS). With the dawn of large language models (LLMs), their enhanced ability to handle diverse patterns and contexts may aid in addressing this challenging task. In this paper, we investigate the current performance of LLMs in the near-OOD setting, where OOD queries belong to the same domain but different intents. To take advantage of out-of-the-shelf capabilities of LLMs, we do not use fine-tuning. We study the performance of one of the leading frontier models, GPT-4o, in 3 well-known public datasets and 3 in-house datasets, using 10 different methods and prompt variations. We study the performance of different prompts and techniques in Gemini 1.5 Flash and Llama 3.1-70b. We investigate the effect of increasing the number of In-Distribution (ID) intents. We propose a novel hybrid method that is cost-efficient, high-performing, highly robust, and versatile enough to be used with smaller LLMs without sacrificing performance. This is achieved by combining ID success of smaller text classification models and high generalization capabilities of LLMs in OOD detection.

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Trust Me, I’m Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer
Adi Simhi | Itay Itzhak | Fazl Barez | Gabriel Stanovsky | Yonatan Belinkov

Prior work on large language model (LLM) hallucinations has associated them with model uncertainty or inaccurate knowledge. In this work, we define and investigate a distinct type of hallucination, where a model can consistently answer a question correctly, but a seemingly trivial perturbation, which can happen in real-world settings, causes it to produce a hallucinated response with high certainty. This phenomenon, which we dub CHOKE (Certain Hallucinations Overriding Known Evidence), is particularly concerning in high-stakes domains such as medicine or law, where model certainty is often used as a proxy for reliability. We show that CHOKE examples are consistent across prompts, occur in different models and datasets, and are fundamentally distinct from other hallucinations. This difference leads existing mitigation methods to perform worse on CHOKE examples than on general hallucinations. Finally, we introduce a probing-based mitigation that outperforms existing methods on CHOKE hallucinations. These findings reveal an overlooked aspect of hallucinations, emphasizing the need to understand their origins and improve mitigation strategies to enhance LLM safety.

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QUARTZ: QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarization
Mohamed Imed Eddine Ghebriout | Gaël Guibon | Ivan Lerner | Emmanuel Vincent

Dialogue summarization aims to distill the core meaning of a conversation into a concise text. This is crucial for reducing the complexity and noise inherent in dialogue-heavy applications. While recent approaches typically train language models to mimic human-written summaries, such supervision is costly and often results in outputs that lack task-specific focus limiting their effectiveness in downstream applications, such as medical tasks. In this paper, we propose QUARTZ, a framework for task-oriented utility-based dialogue summarization. QUARTZ starts by generating multiple summaries and task-oriented question-answer pairs from a dialogue in a zero-shot manner using a pool of large language models (LLMs). The quality of the generated summaries is evaluated by having LLMs answer task-related questions before (i) selecting the best candidate answers and (ii) identifying the most informative summary based on these answers. Finally, we fine-tune the best LLM on the selected summaries. When validated on multiple datasets, QUARTZ demonstrates its effectiveness by achieving competitive results in various zero-shot settings, rivaling fully-supervised State-of-the-Art (SotA) methods. Code will be released publicly.

Multimodal Dialogue Summarization (MDS) is a critical task with wide-ranging applications. To support the development of effective MDS models, robust automatic evaluation methods are essential for reducing both cost and human effort. However, such methods require a strong meta-evaluation benchmark grounded in human annotations. In this work, we introduce MDSEval, the first meta-evaluation benchmark for MDS, consisting image-sharing dialogues, corresponding summaries, and human judgments across eight well-defined quality aspects. To ensure data quality and richfulness, we propose a novel filtering framework leveraging Mutually Exclusive Key Information (MEKI) across modalities. Our work is the first to identify and formalize key evaluation dimensions specific to MDS. Finally, we benchmark state-of-the-art modal evaluation methods, revealing their limitations in distinguishing summaries from advanced MLLMs and their susceptibility to various bias.

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PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models
ChenZhuo Zhao | Ziqian Liu | Xinda Wang | Junting Lu | Chaoyi Ruan

Prompt optimization is a practical and widely applicable alternative to fine tuning for improving large language model performance. Yet many existing methods evaluate candidate prompts by sampling full outputs, often coupled with self critique or human annotated preferences, which limits scalability, especially for smaller models or models that are not instruction tuned. We present PMPO (Probabilistic Metric Prompt Optimization), a unified framework that uses token level cross entropy as a direct, lightweight evaluation signal. PMPO locates low quality prompt segments via a masking based analysis and iteratively rewrites them to propose improved variants. Crucially, during evaluation, PMPO selects among variants by minimizing loss in a single forward pass, eliminating output sampling and human or judge based scoring for selection while still using standard generation only to propose rewrites. This unified, loss based strategy supports both supervised and preference based tasks. Across model sizes and datasets, PMPO outperforms prior prompt optimizers: it achieves the highest average accuracy on BBH, performs strongly on GSM8K and AQuA RAT, and raises AlpacaEval 2.0 win rates by over 19 points. These results demonstrate PMPO’s effectiveness, efficiency, and broad applicability.

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Evaluating the Creativity of LLMs in Persian Literary Text Generation
Armin Tourajmehr | Mohammad Reza Modarres | Yadollah Yaghoobzadeh

Large language models (LLMs) have demonstrated notable creative abilities in generating literary texts, including poetry and short stories. However, prior research has primarily centered on English, with limited exploration of non-English literary traditions and without standardized methods for assessing creativity. In this paper, we evaluate the capacity of LLMs to generate Persian literary text enriched with culturally relevant expressions. We build a dataset of user-generated Persian literary spanning 20 diverse topics and assess model outputs along four creativity dimensions—originality, fluency, flexibility, and elaboration—by adapting the Torrance Tests of Creative Thinking. To reduce evaluation costs, we adopt an LLM as a judge for automated scoring and validate its reliability against human judgments using intraclass correlation coefficients, observing strong agreement. In addition, we analyze the models’ ability to understand and employ four core literary devices: simile, metaphor, hyperbole, and antithesis. Our results highlight both the strengths and limitations of LLMs in Persian literary text generation, underscoring the need for further refinement.

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SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection
Taichi Aida | Danushka Bollegala

In Semantic Change Detection (SCD), it is a common problem to obtain embeddings that are both interpretable and high-performing. However, improving interpretability often leads to a loss in the SCD performance, and vice versa. To address this problem, we propose SCDTour, a method that orders and merges interpretable axes to alleviate the performance degradation of SCD. SCDTour considers both (a) semantic similarity between axes in the embedding space, as well as (b) the degree to which each axis contributes to semantic change. Experimental results show that SCDTour preserves performance in semantic change detection while maintaining high interpretability. Moreover, agglomerating the sorted axes produces a more refined set of word senses, which achieves comparable or improved performance against the original full-dimensional embeddings in the SCD task. These findings demonstrate that SCDTour effectively balances interpretability and SCD performance, enabling meaningful interpretation of semantic shifts through a small number of refined axes.

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Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing
Bhiman Kumar Baghel | Emma Jordan | Zheyuan Ryan Shi | Xiang Lorraine Li

Large Language Models (LLMs) are widely deployed in downstream tasks, but keeping their knowledge up-to-date via retraining or fine-tuning is often computationally expensive. Model editing provides a more efficient alternative by updating a targeted subset of parameters, which often follows the locate-and-edit paradigm. Despite this efficiency, existing methods are limited: edits may fail to inject knowledge (UnderEdit) or unintentionally disrupt unrelated neighboring knowledge (OverEdit). To address these challenges, we propose two complementary methods: **iterative model editing**, which applies successive edits to mitigate UnderEdit, and **neighbor-assisted model editing**, which incorporates neighboring knowledge during editing to reduce OverEdit. Our extensive experiments show that these techniques improve editing performance across multiple LLMs, algorithms, and benchmarks, reducing UnderEdit by up to 38 percentage points and OverEdit by up to 6, while remaining broadly applicable to any locate-and-edit method.

Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive capability in visual tasks, but their fine-tuning often suffers from bias in class-imbalanced scenes. Recent works have introduced large language models (LLMs) to enhance VLM fine-tuning withsupplementaryy semantic information. However, they often overlook inherent class imbalance in VLMs’ pre-training, which may lead to bias accumulation in downstream tasks. To address this problem, this paper proposes a Multi-dimensional Dynamic Prompt Routing (MDPR) framework. MDPR constructs a comprehensive knowledge base for classes, spanning multiple visual-semantic dimensions. During fine-tuning, the dynamic routing mechanism aligns global visual classes, retrieves optimal prompts, and balances fine-grained semantics, yielding stable predictions through logits fusion. Extensive experiments on long-tailed benchmarks, including CIFAR-LT, ImageNet-LT, and Places-LT, demonstrate that MDPR achieves comparable results with current SOTA methods. Ablation studies further confirm the effectiveness of our semantic library for tail classes and show that our dynamic routing operates with a slight increase in computational overhead, making MDPR a flexible and efficient enhancement for VLM fine-tuning under data imbalance. The codes are available in https://github.com/Sha843/MDPR.

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HGAdapter: Hypergraph-based Adapters in Language Models for Code Summarization and Clone Detection
Guang Yang | Yujie Zhu

Pre-trained language models (PLMs) are increasingly being applied to code-related tasks. Although PLMs have achieved good results, they do not take into account potential high-order data correlations within the code. We propose three types of high-order correlations in code tokens, i.e. abstract syntax tree family correlation, lexical correlation, and line correlation. We design a tokens and hyperedges generator to capture these high-order data correlations. We improve the architecture of hypergraph neural networks and combine it with adapter tuning to propose a novel hypergraph-based adapter (HGAdapter) to fine-tune PLMs. HGAdapter can encode high-order data correlations and is allowed to be inserted into various PLMs to enhance performance. Experiments were conducted on several public datasets, including six languages of code summarization and code clone detection tasks. Our methods improved the performance of PLMs in datasets to varying degrees. Experimental results validate the introduction of high-order data correlations that contribute to improved effectiveness.

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Evaluating distillation methods for data-efficient syntax learning
Takateru Yamakoshi | Thomas L. Griffiths | R. Thomas McCoy | Robert D. Hawkins

Data-efficient training requires strong inductive biases. To the extent that transformer attention matrices encode syntactic relationships, we would predict that knowledge distillation (KD) targeting attention should selectively accelerate syntax acquisition relative to conventional logit-based KD. To test this hypothesis, we train GPT-2 student models on datasets ranging from 10K to 5M sentences using both distillation methods, evaluating them on both syntactic benchmarks and perplexity. Surprisingly, while logit-based KD dramatically improves data-efficiency, attention-based KD provides minimal benefit even for syntactic tasks. This suggests that output distributions provide sufficient supervisory signal for syntax acquisition, indicating that syntactic knowledge may be distributed throughout the network rather than localized in attention patterns.

Emotion context sensitivity—the ability to adjust emotional responses based on contexts—is a core component of human emotional intelligence. For example, being told, “You can come with me if you want,” may elicit joy if the destination is a mall, but provoke fear if the destination is a trap house. As large language models (LLMs) are increasingly deployed in socially interactive settings, understanding this human ability becomes crucial for generating context-appropriate, emotion-aware responses. In this work, we introduce Trace, a novel benchmark for evaluating whether LLMs can understand emotion context sensitivity of humans. This benchmark consists of 1,626 social scenarios and comprises two complementary tests: a sensitivity test, which measures whether models can detect emotional shifts caused by context changes, and a robustness test, which evaluates whether models can maintain stable emotion predictions when context changes are emotionally irrelevant. Each scenario pair keeps the core event constant while systematically varying contextual details—time, place, or agent—based on insights from behavioral theory and emotion psychology. Experimental results show that even the best-performing LLMs lag behind human performance by 20% in the sensitivity test and 15% in the robustness test, indicating substantial room for improvement in emotion-aware reasoning.

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[MASK]ED - Language Modeling for Explainable Classification and Disentangling of Socially Unacceptable Discourse.
Dimitra Niaouri | Mohamed Rayane Ghilene | Michele Linardi | Julien Longhi

Analyzing Socially Unacceptable Discourse (SUD) online is a critical challenge for regulators and platforms amidst growing concerns over harmful content. While Pre-trained Masked Language Models (PMLMs) have proven effective for many NLP tasks, their performance often degrades in multi-label SUD classification due to overlapping linguistic cues across categories. In this work, we propose an artifact-guided pre-training strategy that injects statistically salient linguistic features, referred to as artifacts, into the masked language modelling objective. By leveraging context-sensitive tokens, we guide an importance-weighted masking scheme during pre-training to enhance generalization across discourse types. We further use these artifact signals to inform a lightweight dataset curation procedure that highlights noisy or ambiguous instances. This supports targeted relabeling and filtering, enabling more explainable and consistent annotation with minimal changes to the original data. Our approach provides consistent improvements in 10 datasets extensively used in SUD classification benchmarks.*Disclaimer: This article contains some extracts of unacceptable and upsetting language.*

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A Survey of Cognitive Distortion Detection and Classification in NLP
Archie Sage | Jeroen Keppens | Helen Yannakoudakis

As interest grows in applying natural language processing (NLP) techniques to mental health, an expanding body of work explores the automatic detection and classification of cognitive distortions (CDs). CDs are habitual patterns of negatively biased or flawed thinking that distort how people perceive events, judge themselves, and react to the world. Identifying and addressing them is a central goal of therapy. Despite this momentum, the field remains fragmented, with inconsistencies in CD taxonomies, task formulations, and evaluation practices limiting comparability across studies. This survey presents the first comprehensive review of 38 studies spanning two decades, mapping how CDs have been implemented in computational research and evaluating the methods applied. We provide a consolidated CD taxonomy reference, summarise common task setups, and highlight persistent challenges to support more coherent and reproducible research. Alongside our review, we introduce practical resources, including curated evaluation metrics from surveyed papers, a standardised datasheet template, and an ethics flowchart, available online.

As large language models (LLMs) grow more capable, they face increasingly diverse and complex tasks, making reliable evaluation challenging. The paradigm of LLMs as judges has emerged as a scalable solution, yet prior work primarily focuses on simple settings. Their reliability in complex tasks—where multi-faceted rubrics, unstructured reference answers, and nuanced criteria are critical—remains understudied. In this paper, we constructed ComplexEval Bench, a challenge benchmark designed to systematically expose and quantify Auxiliary Information Induced Biases. We systematically investigated and validated 6 previously unexplored biases across 12 basic and 3 advanced scenarios. Key findings reveal: (1) all evaluated models exhibit significant susceptibility to these biases, with bias magnitude scaling with task complexity; (2) notably, Large Reasoning Models (LRMs) show paradoxical vulnerability. Our in-depth analysis offers crucial insights for improving the accuracy and verifiability of evaluation signals, paving the way for more general and robust evaluation models.

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Self-Training Large Language Models with Confident Reasoning
Hyosoon Jang | Yunhui Jang | Sungjae Lee | Jungseul Ok | Sungsoo Ahn

Large language models (LLMs) have shown impressive performance by generating reasoning paths before final answers, but learning such a reasoning path requires costly human supervision. To address this issue, recent studies have explored self-training methods that improve reasoning capabilities using pseudo-labels generated by the LLMs themselves. Among these, confidence-based self-training fine-tunes LLMs to prefer reasoning paths with high-confidence answers, where confidence is estimated via majority voting. However, such methods exclusively focus on the quality of the final answer and may ignore the quality of the reasoning paths, as even an incorrect reasoning path leads to a correct answer by chance. Instead, we advocate the use of reasoning-level confidence to identify high-quality reasoning paths for self-training, supported by our empirical observations. We then propose a new self-training method, **CORE-PO**, that fine-tunes LLMs to prefer high-**CO**nfidence **RE**asoning paths through **P**olicy **O**ptimization. Our experiments show that CORE-PO improves the accuracy of outputs on four in-distribution and two out-of-distribution benchmarks, compared to existing self-training methods.

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Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision
Tej Deep Pala | Panshul Sharma | Amir Zadeh | Chuan Li | Soujanya Poria

Large Language Models (LLMs) are prone to hallucination, especially during multi‐hop and reasoning-intensive tasks such as mathematical problem solving. While Outcome Reward Models verify only final answers, Process Reward Models (PRMs) score each intermediate step to steer generation toward coherent solutions. We introduce PathFinder‐PRM, a novel hierarchical, error‐aware discriminative PRM that first classifies math and consistency errors at each step, then combines these fine‐grained signals to estimate step correctness. To train PathFinder‐PRM, we construct a 400K‐sample dataset by enriching the human‐annotated PRM800K corpus and RLHFlow Mistral traces with three‐dimensional step‐level labels. On PRMBench, PathFinder‐PRM achieves a new state‐of‐the‐art PRMScore of 67.7, outperforming the prior best (65.5) while using 3× less data. When applied to reward guided greedy search, our model yields prm@8 48.3, a +1.5 point gain over the strongest baseline. These results demonstrate that decoupled error detection and reward estimation not only boost fine‐grained error detection but also substantially improve end‐to‐end, reward‐guided mathematical reasoning with greater data efficiency. Our code is available at https://github.com/declare-lab/PathFinder-PRM.

Large language models (LLMs) have been used to synthesize persuasive dialogues for studying persuasive behavior. However, existing approaches often suffer from issues such as stance oscillation and low informativeness. To address these challenges, we propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation. We further introduce multilingual prompting, which aligns language use with speakers’ native languages to better capture cultural nuances. Our experiments involving speakers from eight countries show that continually reinforcing speaker profiles and cultural context improves argument diversity, enhances informativeness, and stabilizes speaker stances. Moreover, our analysis of inter-group versus intra-group persuasion reveals that speakers engaging within their own cultural groups employ more varied persuasive strategies than in cross-cultural interactions. These findings underscore the importance of speaker and cultural awareness in LLM-based persuasion modeling and suggest new directions for developing more personalized, ethically grounded, and culturally adaptive LLM-generated dialogues.

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An LLM-based Temporal-spatial Data Generation and Fusion Approach for Early Detection of Late Onset Alzheimer’s Disease (LOAD) Stagings Especially in Chinese and English-speaking Populations
Yang Han | Jacqueline C.k. Lam | Victor O.k. Li | Lawrence Y. L. Cheung

Alzheimer’s Disease (AD), the 7th leading cause of death globally, demands scalable methods for early detection. While speech-based diagnostics offer promise, existing approaches struggle with temporal-spatial (T-S) challenges in capturing subtle linguistic shifts across different disease stages (temporal) and in adapting to cross-linguistic variability (spatial). This study introduces a novel Large Language Model (LLM)-driven T-S fusion framework that integrates multilingual LLMs, contrastive learning, and interpretable marker discovery to revolutionize Late Onset AD (LOAD) detection. Our key innovations include: (1) T-S Data Imputation: Leveraging LLMs to generate synthetic speech transcripts across different LOAD stages (NC, Normal Control; eMCI, early Mild Cognitive Impairment; lMCI, late Mild Cognitive Impairment; AD) and languages (Chinese, English, Spanish), addressing data scarcity while preserving clinical relevance (expert validation: 86% agreement with LLM-generated labels). (2) T-S Transformer with Contrastive Learning: A multilingual model that disentangles stage-specific (temporal) and language-specific (spatial) patterns, achieving a notable improvement of 10.9–24.7% in F1-score over existing baselines. (3) Cross-Linguistic Marker Discovery: Identifying language-agnostic markers and language-specific patterns to enhance interpretability for clinical adoption. By unifying temporal LOAD stages and spatial diversity, our framework achieves state-of-the-art performance in early LOAD detection while enabling cross-linguistic diagnostics. This study bridges NLP and clinical neuroscience, demonstrating LLMs’ potential to amplify limited biomedical data and advance equitable healthcare AI.

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Side Effects of Erasing Concepts from Diffusion Models
Shaswati Saha | Sourajit Saha | Manas Gaur | Tejas Gokhale

Concerns about text-to-image (T2I) generative models infringing on privacy, copyright, and safety have led to the development of concept erasure techniques (CETs). The goal of an effective CET is to prohibit the generation of undesired “target” concepts specified by the user, while preserving the ability to synthesize high-quality images of other concepts. In this work, we demonstrate that concept erasure has side effects and CETs can be easily circumvented. For a comprehensive measurement of the robustness of CETs, we present the Side Effect Evaluation (SEE) benchmark that consists of hierarchical and compositional prompts describing objects and their attributes. The dataset and an automated evaluation pipeline quantify side effects of CETs across three aspects: impact on neighboring concepts, evasion of targets, and attribute leakage. Our experiments reveal that CETs can be circumvented by using superclass-subclass hierarchy, semantically similar prompts, and compositional variants of the target. We show that CETs suffer from attribute leakage and a counterintuitive phenomenon of attention concentration or dispersal. We release our benchmark and evaluation tools to aid future work on robust concept erasure.

Knowledge Graph Embedding (KGE) seeks to learn latent representations of entities and relations to support knowledge-driven AI systems. However, existing KGE approaches often exhibit a growing discrepancy between the learned embedding space and the intrinsic structural semantics of the underlying knowledge graph. This divergence primarily stems from the over-reliance on geometric criteria for assessing triple plausibility, whose effectiveness is inherently limited by the sparsity of factual triples and the disregard of higher-order structural dependencies in the knowledge graph. To overcome this limitation, we introduce Structure-aware Calibration (SaCa), a versatile framework designed to calibrate KGEs through the integration of global structural patterns. SaCa designs two new components: (i) Structural Proximity Measurement, which captures multi-order structural signals from both entity and entity-relation perspectives; and (ii) KG-Induced Soft-weighted Contrastive Learning (KISCL), which assigns soft weights to hard-to-distinguish positive and negative pairs, enabling the model to better reflect nuanced structural dependencies. Extensive experiments on seven benchmarks demonstrate that SaCa consistently boosts performance across ten KGE models on link prediction and entity classification tasks with minimal overhead.

Large Language Model (LLMs) can be used to write or modify documents, presenting a challenge for understanding the intent behind their use. For example, benign uses may involve using LLM on a human-written document to improve its grammar or to translate it into another language. However, a document entirely produced by a LLM may be more likely to be used to spread misinformation than simple translation (, from use by malicious actors or simply by hallucinating). Prior works in Machine Generated Text (MGT) detection mostly focus on simply identifying whether a document was human or machine written, ignoring these fine-grained uses. In this paper, we introduce a HiErarchical, length-RObust machine-influenced text detector (HERO), which learns to separate text samples of varying lengths from four primary types: human-written, machine-generated, machine-polished, and machine-translated. HERO accomplishes this by combining predictions from length-specialist models that have been trained with Subcategory Guidance. Specifically, for categories that are easily confused (, different source languages), our Subcategory Guidance module encourages separation of the fine-grained categories, boosting performance. Extensive experiments across five LLMs and six domains demonstrate the benefits of our HERO, outperforming the state-of-the-art by 2.5-3 mAP on average.

Can Large Language Models (LLMs) simulate humans in making important decisions? Recent research has unveiled the potential of using LLMs to develop role-playing language agents (RPLAs), mimicking mainly the knowledge and tones of various characters. However, imitative decision-making necessitates a more nuanced understanding of personas. In this paper, we benchmark the ability of LLMs in persona-driven decision-making. Specifically, we investigate whether LLMs can predict characters’ decisions provided by the preceding stories in high-quality novels. Leveraging character analyses written by literary experts, we construct a dataset LIFECHOICE comprising 2,512 characters’ decision points from 470 books. Then, we conduct comprehensive experiments on LIFECHOICE with various LLMs and RPLA methodologies. The results demonstrate that state-of-the-art LLMs exhibit promising capabilities in this task, yet substantial room for improvement remains. Hence, we further propose the CHARMAP method, which adopts persona-based memory retrieval and significantly advances RPLAs on this task.

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Neutral Is Not Unbiased: Evaluating Implicit and Intersectional Identity Bias in LLMs Through Structured Narrative Scenarios
Saba Ghanbari Haez | Mauro Dragoni

Large Language Models often reproduce societal biases, yet most evaluations overlook how such biases evolve across nuanced contexts or intersecting identities. We introduce a scenario-based evaluation framework built on 100 narrative tasks, designed to be neutral at baseline and systematically modified with gender and age cues. Grounded in the theory of Normative-Narrative Scenarios, our approach provides ethically coherent and socially plausible settings for probing model behavior. Analyzing responses from five leading LLMs—GPT-4o, LLaMA 3.1, Qwen2.5, Phi-4, and Mistral—using Critical Discourse Analysis and quantitative linguistic metrics, we find consistent evidence of bias. Gender emerges as the dominant axis of bias, with intersectional cues (e.g., age and gender combined) further intensifying disparities. Our results underscore the value of dynamic narrative progression for detecting implicit, systemic biases in Large Language Models.

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BTW: A Non-Parametric Variance Stabilization Framework for Multimodal Model Integration
Jun Hou | Le Wang | Xuan Wang

Mixture-of-Experts (MoE) models have become increasingly powerful in multimodal learning by enabling modular specialization across modalities. However, their effectiveness remains unclear when additional modalities introduce more noise than complementary information. Existing approaches, such as the Partial Information Decomposition, struggle to scale beyond two modalities and lack the resolution needed for instance-level control. We propose **B**eyond **T**wo-modality **W**eighting (**BTW**), a bi-level, non-parametric weighting framework that combines instance-level Kullback-Leibler (KL) divergence and modality-level mutual information (MI) to dynamically adjust modality importance during training. Our method does not require additional parameters and can be applied to an arbitrary number of modalities. Specifically, BTW computes per-example KL weights by measuring the divergence between each unimodal and the current multimodal prediction, and modality-wide MI weights by estimating global alignment between unimodal and multimodal outputs. Extensive experiments on sentiment regression and clinical classification demonstrate that our method significantly improves regression performance and multiclass classification accuracy.

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Can LLMs Be Efficient Predictors of Conversational Derailment?
Kaustubh Olpadkar | Vikram Sunil Bajaj | Leslie Barrett

Conversational derailment — when online discussions stray from their intended topics due to toxic or inappropriate remarks — is a common issue on online platforms. These derailments can have negative impacts on users and the online community. While previous work has focused on post hoc identification of toxic content, recent efforts emphasize proactive prediction of derailments before they occur, enabling early moderation. However, forecasting derailment is difficult due to the context-dependent emergence of toxicity and the need for timely alerts. We prompt pre-trained large language models (LLMs) to predict conversational derailment without task-specific fine-tuning. We compare a range of prompting strategies, including chain-of-thought reasoning (CoT) and few-shot exemplars, across small and large scale models, and evaluate their performance and inference-cost trade-offs on derailment benchmarks. Our experiments show that the best prompting configuration attains state-of-the-art performance, and forecasts derailments earlier than existing approaches. These results demonstrate that LLMs, even without fine-tuning, can serve as an effective tool for proactive conversational moderation.

Query rewriting plays a pivotal role in Retrieval-Augmented Generation (RAG) by refining real-world queries of varying complexity. Existing approaches typically rely on outcome-supervised training or heuristic rules to guide the rewriting process. However, these paradigms often struggle to handle queries with varying levels of complexity, posing over- and under-refinement problems. We identify the root cause of these issues as the absence of supervision signals for intermediate steps. To fully construct and utilize such signals, we propose Q-PRM, a novel query rewriting framework. Q-PRM reformulates the rewriting process as a Markov Decision Process (MDP) composed of atomic rewriting steps. In this way, Q-PRM can apply process-level supervision to each atomic step according to the query type, offering more targeted and effective guidance. Q-PRM comprises three key stages: (1) applying Monte Carlo Tree Search to generate step-level process supervision signals; (2) performing reinforced self-training for progressive process refinement; and (3) employing PRM-guided decoding during inference. Experiments on several open-domain QA benchmarks demonstrate that Q-PRM consistently outperforms baselines across different levels of query complexity.

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Factuality Beyond Coherence: Evaluating LLM Watermarking Methods for Medical Texts
Rochana Prih Hastuti | Rian Adam Rajagede | Mansour Al Ghanim | Mengxin Zheng | Qian Lou

As large language models (LLMs) are adapted to sensitive domains such as medicine, their fluency raises safety risks, particularly regarding provenance and accountability. Watermarking embeds detectable patterns to mitigate these risks, yet its reliability in medical contexts remains untested. Existing benchmarks focus on detection-quality tradeoffs and overlook factual risks. In medical text, watermarking often reweights low-entropy tokens, which are highly predictable and often carry critical medical terminology. Shifting these tokens can cause inaccuracy and hallucinations, risks that prior general-domain benchmarks fail to capture.We propose a medical-focused evaluation workflow that jointly assesses factual accuracy and coherence. Using GPT-Judger and further human validation, we introduce the Factuality-Weighted Score (FWS), a composite metric prioritizing factual accuracy beyond coherence to guide watermarking deployment in medical domains. Our evaluation shows current watermarking methods substantially compromise medical factuality, with entropy shifts degrading medical entity representation. These findings underscore the need for domain-aware watermarking approaches that preserve the integrity of medical content.

Recent advances in Large Language Model (LLM)-based Role-Playing Language Agents (RPLAs) have attracted broad attention in various applications. While chain-of-thought reasoning has shown importance in many tasks for LLMs, the internal thinking processes of RPLAs remain unexplored. Understanding characters’ inner thoughts is crucial for developing advanced RPLAs. In this paper, we introduce ROLETHINK, a novel benchmark constructed from literature for evaluating character thought generation. We propose the task of inner thought reasoning, constructing 6,058 data entries from 76 books, which includes two sets: the gold set that compares generated thoughts with original character monologues, and the silver set that uses expert-synthesized character analyses as references. To address this challenge, we propose MIRROR, a chain-of-thought approach that generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations. Through extensive experiments, we demonstrate the importance of inner thought reasoning for RPLAs, and MIRROR consistently outperforms existing methods.

Sparse Mixture-of-Experts (SMoE) architectures are widely used in large language models (LLMs) due to their computational efficiency. However, though only a few experts are activated for each token, SMoE still requires loading all expert parameters, leading to high memory usage and challenges in deployment. Previous work has tried to reduce the overhead by pruning and merging experts, but primarily focused on expert-level operations, leaving neuron-level structure underexplored. We propose **DERN** (**D**ropping **E**xperts, **R**ecombining **N**eurons), a task-agnostic and retraining-free framework for expert pruning and reconstruction. We observe that experts are often misaligned and contain semantic conflicts at the neuron level, which poses challenges for direct merging. To solve this, DERN works in three steps: it first prunes redundant experts using router statistics; then it decomposes them into neuron-level expert segments, assigning each segment to its most compatible retained expert; and finally, it merges segments within each retained expert to build a compact representation. Experiments on Mixtral, Qwen, and DeepSeek SMoE models show that DERN improves performance by more than 5% on commonsense reasoning and MMLU benchmarks under 50% expert sparsity, without extra training. It also greatly reduces the number of experts and memory usage, making SMoE LLMs easier to deploy in practice.

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BiasFilter: An Inference-Time Debiasing Framework for Large Language Models
Xiaoqing Cheng | Ruizhe Chen | Hongying Zan | Yuxiang Jia | Min Peng

Mitigating social bias in large language models (LLMs) has become an increasingly important research objective. However, existing debiasing methods often incur high human and computational costs, exhibit limited effectiveness, and struggle to scale to larger models and open-ended generation tasks. To address these limitations, this paper proposes BiasFilter, a model-agnostic, inference-time debiasing framework that integrates seamlessly with both open-source and API-based LLMs. Instead of relying on retraining with balanced data or modifying model parameters, BiasFilter enforces fairness by filtering generation outputs in real time. Specifically, it periodically evaluates intermediate outputs every few tokens, maintains an active set of candidate continuations, and incrementally completes generation by discarding low-reward segments based on a fairness reward signal. To support this process, we construct a fairness preference dataset and train an implicit reward model to assess token-level fairness in generated responses. Extensive experiments demonstrate that BiasFilter effectively mitigates social bias across a range of LLMs while preserving overall generation quality.

Long-form egocentric video understanding provides rich contextual information and unique insights into long-term human behaviors, holding significant potential for applications in embodied intelligence, long-term activity analysis, and personalized assistive technologies. However, existing benchmark datasets primarily focus on single, short (e.g., minutes to tens of minutes) to moderately long videos, leaving a substantial gap in evaluating extensive, ultra-long egocentric video recordings. To address this, we introduce X-LeBench, a novel benchmark dataset meticulously designed to fill this gap by focusing on tasks requiring a comprehensive understanding of extremely long egocentric video recordings. Our X-LeBench develops a life-logging simulation pipeline that produces realistic, coherent daily plans aligned with real-world video data. This approach enables the flexible integration of synthetic daily plans with real-world footage from Ego4D—a massive-scale egocentric video dataset covers a wide range of daily life scenarios—resulting in 432 simulated video life logs spanning from 23 minutes to 16.4 hours. The evaluations of several baseline systems and multimodal large language models (MLLMs) reveal their poor performance across the board, highlighting the inherent challenges of long-form egocentric video understanding, such as temporal localization and reasoning, context aggregation, and memory retention, and underscoring the need for more advanced models.

Multi-modal intent recognition (MIR) requires integrating non-verbal cues from real-world contexts to enhance human intention understanding, which has attracted substantial research attention in recent years. Despite promising advancements, a comprehensive survey summarizing recent advances and new frontiers remains absent. To this end, we present a thorough and unified review of MIR, covering different aspects including (1) Extensive survey: we take the first step to present a thorough survey of this research field covering textual, visual (image/video), and acoustic signals. (2) Unified taxonomy: we provide a unified framework including evaluation protocol and advanced methods to summarize the current progress in MIR. (3) Emerging frontiers: We discuss some future directions such as multi-task, multi-domain, and multi-lingual MIR, and give our thoughts respectively. (4) Abundant resources: we collect abundant open-source resources, including relevant papers, data corpora, and leaderboards. We hope this survey can shed light on future research in MIR.

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Will Annotators Disagree? Identifying Subjectivity in Value-Laden Arguments
Amir Homayounirad | Enrico Liscio | Tong Wang | Catholijn M Jonker | Luciano Cavalcante Siebert

Aggregating multiple annotations into a single ground truth label may hide valuable insights into annotator disagreement, particularly in tasks where subjectivity plays a crucial role. In this work, we explore methods for identifying subjectivity in recognizing the human values that motivate arguments. We evaluate two main approaches: inferring subjectivity through value prediction vs. directly identifying subjectivity. Our experiments show that direct subjectivity identification significantly improves the model performance of flagging subjective arguments. Furthermore, combining contrastive loss with binary cross-entropy loss does not improve performance but reduces the dependency on per-label subjectivity. Our proposed methods can help identify arguments that individuals may interpret differently, fostering a more nuanced annotation process.

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LLMs Can Compensate for Deficiencies in Visual Representations
Sho Takishita | Jay Gala | Abdelrahman Mohamed | Kentaro Inui | Yova Kementchedjhieva

Many vision-language models (VLMs) that prove very effective at a range of multimodal task, build on CLIP-based vision encoders, which are known to have various limitations. We investigate the hypothesis that the strong language backbone in VLMs compensates for possibly weak visual features by contextualizing or enriching them. Using three CLIP-based VLMs, we perform controlled self-attention ablations on a carefully designed probing task. Our findings show that despite known limitations, CLIP visual representations offer ready-to-read semantic information to the language decoder. However, in scenarios of reduced contextualization in the visual representations, the language decoder can largely compensate for the deficiency and recover performance. This suggests a dynamic division of labor in VLMs and motivates future architectures that offload more visual processing to the language decoder.

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Adapting Large Language Models for Character-based Augmentative and Alternative Communication
Dylan Gaines | Keith Vertanen

Users of Augmentative and Alternative Communication (AAC) may write letter-by-letter via an interface that uses a character language model. However, most state-of-the-art large pretrained language models predict subword tokens of variable length. We investigate how to practically use such models to make accurate and efficient character predictions. Our algorithm for producing character predictions from a subword large language model (LLM) provides more accurate predictions than using a classification layer, a byte-level LLM, or an n-gram model. Additionally, we investigate a domain adaptation procedure based on a large dataset of sentences we curated based on scoring how useful each sentence might be for spoken or written AAC communication. We find our procedure further improves model performance on simple, conversational text.

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Token-Level Metrics for Detecting Incorrect Gold Annotations in Named Entity Recognition
Elena Merdjanovska | Alan Akbik

Annotated datasets for supervised learning tasks often contain incorrect gold annotations, i.e. label noise. To address this issue, many noisy label learning approaches incorporate metrics to filter out unreliable samples, for example using heuristics such as high loss or low confidence. However, when these metrics are integrated into larger pipelines, it becomes difficult to compare their effectiveness, and understand their individual contribution to reducing label noise. This paper directly compares popular sample metrics for detecting incorrect annotations in named entity recognition (NER). NER is commonly approached as token classification, so the metrics are calculated for each training token and we flag the incorrect ones by defining metrics thresholds. We compare the metrics based on (i) their accuracy in detecting the incorrect labels and (ii) the test scores when retraining a model using the cleaned dataset. We show that training dynamics metrics work the best overall. The best metrics effectively reduce the label noise across different noise types. The errors that the model has not yet memorized are more feasible to detect, and relabeling these tokens is a more effective strategy than excluding them from training.

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Exploring Paraphrasing Strategies for CEFR A1-Level Constraints in LLMs
Eugenio Marzona | Maria Goikhman | Alessio Palmero Aprosio | Massimo Zancanaro

Large language models are increasingly used for teaching and self-learning foreign languages. However, their capability to meet specific linguistic constraints is still underexplored. This study compares the effectiveness of prompt engineering in guiding ChatGPT (4o and 4o-mini), and Llama 3 to rephrase general-domain texts to meet CEFR A1-level constraints in English and Italian, making them suitable for beginner learners. It compares 4 prompt engineering approaches, built upon iterative paraphrasing method that gradually refines original texts for CEFR compliance. The approaches compared include paraphrasing with or without Chain-of-Thought, as well as grammar and vocabulary simplification performed either simultaneously or as separate steps. The findings suggest that for English the best approach is combining COT with separate grammar and vocabulary simplification, while for Italian one-step strategies have better effect on grammar, and two-step strategies work better for covering the vocabulary. The paraphrasing approach can approve compliance, although at this point it is not cost-effective. We release a dataset of pairs original sentence-beginner level paraphrase (both in Italian and in English) on which further work could be based.

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Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction
Zhexiong Liu | Diane Litman

Large Language Models (LLMs) have shown extraordinary success across various text generation tasks; however, their potential for simple yet essential text classification remains underexplored, as LLM pre-training tends to emphasize generation over classification. While LLMs with instruction tuning can transform classification into a generation task, they often struggle to categorize nuanced texts. One such example is text revision, which involves nuanced edits between pairs of texts. Although simply fine-tuning LLMs for revision classification seems plausible, it requires a large amount of revision annotations, which are exceptionally expensive and scarce in the community. To address this issue, we introduce a plug-and-play layer-wise parameter-efficient fine-tuning (PEFT) framework, i.e., IR-Tuning, which fine-tunes a subset of important LLM layers that are dynamically selected based on their gradient norm distribution, while freezing those of redundant layers. Extensive experiments suggest that IR-Tuning surpasses several layer-wise PEFT baselines over diverse text revisions, while achieving fast convergence, low GPU memory consumption, and effectiveness on small revision corpora.

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ConText-LE: Cross-Distribution Generalization for Longitudinal Experiential Data via Narrative-Based LLM Representations
Ahatsham Hayat | Bilal Khan | Mohammad Rashedul Hasan

Longitudinal experiential data offers rich insights into dynamic human states, yet building models that generalize across diverse contexts remains challenging. We propose ConText-LE, a framework that systematically investigates text representation strategies and output formulations to maximize large language model cross-distribution generalization for behavioral forecasting. Our novel Meta-Narrative representation synthesizes complex temporal patterns into semantically rich narratives, while Prospective Narrative Generation reframes prediction as a generative task aligned with LLMs’ contextual understanding capabilities. Through comprehensive experiments on three diverse longitudinal datasets addressing the underexplored challenge of cross-distribution generalization in mental health and educational forecasting, we show that combining Meta-Narrative input with Prospective Narrative Generation significantly outperforms existing approaches. Our method achieves up to 12.28% improvement in out-of-distribution accuracy and up to 11.99% improvement in F1 scores over binary classification methods. Bidirectional evaluation and architectural ablation studies confirm the robustness of our approach, establishing ConText-LE as an effective framework for reliable behavioral forecasting across temporal and contextual shifts.

The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to users’ emotional needs. Existing supervised fine-tuning (SFT) struggles to address these issues, as it rigidly trains models on single gold-standard responses without modeling nuanced strategy trade-offs. To overcome these limitations, we propose a novel two-stage framework that optimizes strategy selection preferences at each dialogue turn. We first leverage Monte Carlo Tree Search to construct ESC-Pro, a high-quality preference dataset with turn-level strategy-response pairs. Then training on ESC-Pro with Chain-of-Strategy Optimization (CSO) improves both strategy accuracy and bias mitigation, enabling LLMs to generate more empathetic and contextually appropriate responses. Experiments on LLaMA-3.1-8B, Gemma-2-9B, and Qwen2.5-7B demonstrate that CSO outperforms standard SFT, highlighting the efficacy of fine-grained, turn-level preference modeling in ESC.

Legal research depends on headnotes: concise summaries that help lawyers quickly identify relevant cases. Yet, many court decisions lack them due to the high cost of manual annotation. To address this gap, we introduce the Swiss Landmark Decisions Summarization (SLDS) dataset containing 20K rulings from the Swiss Federal Supreme Court, each with headnotes in German, French, and Italian. SLDS has the potential to significantly improve access to legal information and transform legal research in Switzerland. We fine-tune open models (Qwen2.5, Llama 3.2, Phi-3.5) and compare them to larger general-purpose and reasoning-tuned LLMs, including GPT-4o, Claude 3.5 Sonnet, and the open-source DeepSeek R1. Using an LLM-as-a-Judge framework, we find that fine-tuned models perform well in terms of lexical similarity, while larger models generate more legally accurate and coherent summaries. Interestingly, reasoning-focused models show no consistent benefit, suggesting that factual precision is more important than deep reasoning in this task. We release SLDS under a CC BY 4.0 license to support future research in cross-lingual legal summarization.

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Context Minimization for Resource-Constrained Text Classification: Optimizing Performance-Efficiency Trade-offs through Linguistic Features
Nahid Hossain | Md Faisal Kabir

Pretrained language models have transformed text classification, yet their computational demands often render them impractical for resource-constrained settings. We propose a linguistically-grounded framework for context minimization that leverages theme-rheme structure to preserve critical classification signals while reducing input complexity. Our approach integrates positional, syntactic, semantic, and statistical features, guided by functional linguistics, to identify optimal low-context configurations. We present a methodical iterative feature exploration protocol across 6 benchmarks, including our novel CMLA11 dataset. Results demonstrate substantial efficiency gains: 69-75% reduction in GPU memory, 81-87% decrease in training time, and 82-88% faster inference. Despite these resource savings, our configurations maintain near-parity with full-length inputs, with F1 (macro) reductions averaging just 1.39-3.10%. Statistical significance testing confirms minimal practical impact, with some configurations outperforming the baseline. SHAP analysis reveals specific feature subsets contribute most significantly across datasets, and these recurring configurations offer transferable insights, reducing the need for exhaustive feature exploration. Our method also yields remarkable data compression (72.57% average reduction, reaching 92.63% for longer documents). Ablation studies confirm synergistic feature contributions, establishing our context minimization as an effective solution for resource-efficient text classification with minimal performance trade-offs.

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FLAIRR-TS - Forecasting LLM-Agents with Iterative Refinement and Retrieval for Time Series
Gunjan Jalori | Preetika Verma | Sercan O Arik

Time series Forecasting with large language models (LLMs) requires bridging numerical patterns and natural language. Effective forecasting on LLM often relies on extensive pre-processing and fine-tuning. Recent studies show that a frozen LLM can rival specialized forecasters when supplied with a carefully engineered natural-language prompt, but crafting such a prompt for each task is itself onerous and ad-hoc. We introduce FLAIRR-TS, a test-time prompt optimization framework that utilizes an agentic system: a Forecaster-agent generates forecasts using an initial prompt, which is then refined by a refiner agent, informed by past outputs and retrieved analogs. This adaptive prompting generalizes across domains using creative prompt templates and generates high-quality forecasts without intermediate code generation. Experiments on benchmark datasets show FLAIRR-TS improves forecasting over static prompting and retrieval-augmented baselines, approaching the performance of specialized prompts.FLAIRR-TS provides a practical alternative to fine-tuning, achieving strong performance via its agentic approach to adaptive prompt refinement and retrieval.

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ULTRABENCH: Benchmarking LLMs under Extreme Fine-grained Text Generation
Longfei Yun | Letian Peng | Jingbo Shang

Fine-grained control is essential for precise and customizable text generation, yet existing benchmarks evaluate models on only a few attributes, typically fewer than five. We introduce UltraBench, a new benchmark for extremely fine-grained controllable generation (EFCG), which evaluates large language models (LLMs) under dense, multi-attribute constraints. Each sample includes approximately 70 attributes, combining LLM-extracted soft constraints (e.g., style and tone) with programmatically enforced hard constraints (e.g., word count). Using UltraBench, we conduct a comprehensive evaluation of state-of-the-art LLMs and prompting strategies. Models such as GPT-4.1 and Qwen3-8B perform well on individual constraints, achieving instruction-level accuracy above 70%, but consistently fail to satisfy all constraints simultaneously. To understand this limitation, we analyze model behavior across different dimensions. First, we observe a clear position bias: models tend to prioritize constraints presented later in the prompt while neglecting those that appear earlier. Second, we find that structural and formatting-related constraints are significantly more difficult to satisfy than content- or style-based ones, suggesting that current models struggle to coordinate global structure with token-level control. Finally, our error analysis reveals distinct failure modes: GPT-4.1 often attempts to follow constraints but falls short in precision, whereas LLaMA frequently omits constraints, particularly in multi-turn settings. These findings highlight fundamental limitations in EFCG and underscore the need for new methods that support dense, instruction-sensitive generation.

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The Price of Format: Diversity Collapse in LLMs
Longfei Yun | Chenyang An | Zilong Wang | Letian Peng | Jingbo Shang

Instruction-tuned large language models (LLMs) employ structured templates, such as role markers and special tokens, to enforce format consistency during inference. However, we identify a critical limitation of such formatting: it induces a phenomenon we term diversity collapse, where the model generates semantically similar outputs for open-ended inputs, undermining creativity and variability. We systematically evaluate this effect across tasks like story completion and free-form generation, finding that (1) diversity collapse persists even under high-temperature sampling, and (2) structural tokens in templates significantly constrain the model’s output space. To contextualize these findings, we fine-tune using a range of structured prompts and then evaluate them across three axes: downstream task performance, alignment behavior, and output diversity. Our analysis shows that format consistency between fine-tuning and inference is crucial for structure-sensitive tasks (e.g., GSM8K, IFEval), but has marginal influence on knowledge-heavy tasks (e.g., MMLU, WebQuestions). In contrast, output diversity is primarily governed by the presence or absence of structural tokens, with minimal formatting yielding the most diverse outputs. These findings reveal that current prompting conventions, while beneficial for alignment, may inadvertently suppress output diversity, underscoring the need for diversity-aware prompt design and instruction tuning.

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Zipf’s and Heaps’ Laws for Tokens and LLM-generated Texts
Nikolay Mikhaylovskiy

The frequency distribution of words in human-written texts roughly follows a simple mathematical form known as Zipf’s law. Somewhat less well known is the related Heaps’ law, which describes a sublinear power-law growth of vocabulary size with document size. We study the applicability of Zipf’s and Heaps’ laws to texts generated by Large Language Models (LLMs). We empirically show that Heaps’ and Zipf’s laws only hold for LLM-generated texts in a narrow model-dependent temperature range. These temperatures have an optimal value close to t=1 for all the base models except the large Llama models, are higher for instruction-finetuned models and do not depend on the model size or prompting. This independently confirms the recent discovery of sampling temperature dependent phase transitions in LLM-generated texts.

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LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet?
Rushil Gupta | Jason Hartford | Bang Liu

Large language models (LLMs) have recently been proposed as general-purpose agents for experimental design, with claims that they can perform in-context experimental design. We evaluate this hypothesis using open-source instruction-tuned LLMs applied to genetic perturbation and molecular property discovery tasks. We find that LLM-based agents show no sensitivity to experimental feedback: replacing true outcomes with randomly permuted labels has no impact on performance. Across benchmarks, classical methods such as linear bandits and Gaussian process optimization consistently outperform LLM agents. We further propose a simple hybrid method, LLM-guided Nearest Neighbour (LLMNN) sampling, that combines LLM prior knowledge with nearest-neighbor sampling to guide the design of experiments. LLMNN achieves competitive or superior performance across domains without requiring significant in-context adaptation. These results suggest that current open-source LLMs do not perform in-context experimental design in practice and highlight the need for hybrid frameworks that decouple prior-based reasoning from batch acquisition with updated posteriors.

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A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers
Roxana Petcu | Samarth Bhargav | Maarten de Rijke | Evangelos Kanoulas

Understanding and solving complex reasoning tasks is vital for addressing the information needs of a user. Although dense neural models learn contextualised embeddings, they underperform on queries containing negation. To understand this phenomenon, we study negation in traditional neural information retrieval and LLM-based models. We (1) introduce a taxonomy of negation that derives from philosophical, linguistic, and logical definitions; (2) generate two benchmark datasets that can be used to evaluate the performance of neural information retrieval models and to fine-tune models for a more robust performance on negation; and (3) propose a logic-based classification mechanism that can be used to analyze the performance of retrieval models on existing datasets. Our taxonomy produces a balanced data distribution over negation types, providing a better training setup that leads to faster convergence on the NevIR dataset. Moreover, we propose a classification schema that reveals the coverage of negation types in existing datasets, offering insights into the factors that might affect the generalization of fine-tuned models on negation. Our code is publicly available on GitHub, and the datasets are available on HuggingFace.

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Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models
Maeda Hanafi | Ishan Jindal | Yannis Katsis | Lucian Popa | Huaiyu Zhu

Instruction fine-tuning enhances the alignment of autoregressive language models (ArLMs) with human intent but relies on large-scale annotated datasets prone to label and text noise. In this paper, we show that existing noise detection techniques designed for autoencoder models (AeLMs) do not directly generalize to ArLMs due to differences in learning dynamics. We propose TDRanker, a novel approach leveraging training dynamics to rank datapoints from easy-to-learn to hard-to-learn, effectively identifying noisy instances. Our method demonstrates robustness across multiple model architectures covering both autoencoder and autoregressive language models (GPT-2, BERT, LaMini-Cerebras-256M) and across various dataset noise levels, achieving at least 2x faster denoising than previous techniques. Applied to real-world classification and generative tasks, TDRanker significantly improves data quality and model performance. These findings suggest that TDRanker provides a scalable solution for refining instruction-tuning datasets, enhancing the reliability of fine-tuned ArLMs in practical applications.

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Can Multiple Responses from an LLM Reveal the Sources of Its Uncertainty?
Yang Nan | Pengfei He | Ravi Tandon | Han Xu

Large language models (LLMs) have delivered significant breakthroughs across diverse domains but can still produce unreliable or misleading outputs, posing critical challenges for real-world applications. While many recent studies focus on quantifying model uncertainty, relatively little work has been devoted to diagnosing the source of uncertainty. In this study, we show that, when an LLM is uncertain, the patterns of disagreement among its multiple generated responses contain rich clues about the underlying cause of uncertainty. To illustrate this point, we collect multiple responses from a target LLM and employ an auxiliary LLM to analyze their patterns of disagreement. The auxiliary model is tasked to reason about the likely source of uncertainty, such as whether it stems from ambiguity in the input question, a lack of relevant knowledge, or both. In cases involving knowledge gaps, the auxiliary model also identifies the specific missing facts or concepts contributing to the uncertainty. In our experiment, we validate our framework on AmbigQA, OpenBookQA, and MMLU-Pro, confirming its generality in diagnosing distinct uncertainty sources. Such diagnosis shows the potential for relevant manual interventions that improve LLM performance and reliability.

Language models built from various sources are the foundation of today’s NLP progress. However, for many low-resource languages, the diversity of domains is often limited, more biased to a religious domain, which impacts their performance when evaluated on distant and rapidly evolving domains such as social media. Domain adaptive pre-training (DAPT) and task-adaptive pre-training (TAPT) are popular techniques to reduce this bias through continual pre-training for BERT-based models, but they have not been explored for African multilingual encoders. In this paper, we explore DAPT and TAPT continual pre-training approaches for African languages social media domain. We introduce AfriSocial, a large-scale social media and news domain corpus for continual pre-training on several African languages. Leveraging AfriSocial, we show that DAPT consistently improves performance (from 1% to 30% F1 score) on three subjective tasks: sentiment analysis, multi-label emotion, and hate speech classification, covering 19 languages. Similarly, leveraging TAPT on the data from one task enhances performance on other related tasks. For example, training with unlabeled sentiment data (source) for a fine-grained emotion classification task (target) improves the baseline results by an F1 score ranging from 0.55% to 15.11%. Combining these two methods (i.e. DAPT + TAPT) further improves the overall performance. The data and model resources are available at HuggingFace.

Large language models (LLMs) are increasingly expected to function as collaborative partners, engaging in back-and-forth dialogue to solve complex, ambiguous problems. However, current LLMs often falter in real-world settings, defaulting to passive responses or narrow clarifications when faced with incomplete or under-specified prompts—falling short of proactively gathering the missing information that is crucial for high-quality solutions. In this work, we introduce a new task paradigm: proactive information gathering, where LLMs must identify gaps in the provided context and strategically elicit implicit user knowledge through targeted questions. To systematically study and train this capability, we design a scalable framework that generates partially specified, real-world tasks, masking key information and simulating authentic ambiguity. Within this setup, our core innovation is a reinforcement finetuning strategy rewards questions that elicit genuinely new, implicit user information—such as hidden domain expertise or fine-grained requirements—that would otherwise remain unspoken. Experiments demonstrate that our trained Qwen-2.5-7B model significantly outperforms o3-mini by 18% on automatic evaluation metrics. More importantly, human evaluation reveals that clarification questions and final outlines generated by our model are favored by human annotators by 42% and 28% respectively. Together, these results highlight the value of proactive clarification in elevating LLMs from passive text generators to genuinely collaborative thought partners.

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Linguistic Alignment Predicts Learning in Small Group Tutoring Sessions
Dorothea French | Robert Moulder | Kelechi Ezema | Katharina von der Wense | Sidney K. DMello

Cognitive science offers rich theories of learning and communication, yet these are often difficult to operationalize at scale. We demonstrate how natural language processing can bridge this gap by applying psycholinguistic theories of discourse to real-world educational data. We investigate linguistic alignment – the convergence of conversational partners’ word choice, grammar, and meaning – in a longitudinal dataset of real-world tutoring interactions and associated student test scores. We examine (1) the extent of alignment, (2) role-based patterns among tutors and students, and (3) the relationship between alignment and learning outcomes. We find that both tutors and students exhibit lexical, syntactic, and semantic alignment, with tutors aligning more strongly to students. Crucially, tutor lexical alignment predicts student learning gains, while student lexical alignment negatively predicts them. As a lightweight, interpretable metric, linguistic alignment offers practical applications in intelligent tutoring systems, educator dashboards, and tutor training.

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EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning
Sanchit Ahuja | Praneetha Vaddamanu | Barun Patra

Despite recent advances in Reasoning Language Models (RLMs), most research focuses solely on English, even though many models are pretrained on multilingual data. In this work, we investigate: Is English the most token-efficient language for reasoning? We evaluate three open-source RLMs: DeepSeek R1, Qwen 2.5, and Qwen 3, across four math datasets and seven typologically diverse languages. We find that reasoning in non-English languages not only reduces token usage, but also preserves accuracy. These gains persist even after translating the reasoning traces into English, suggesting genuine shifts in reasoning behavior rather than surface-level linguistic effects. The extent of improvement, however, depends on the model’s multilingual strength. Our findings motivate a broader view of reasoning in language models, highlighting the potential of multilingual reasoning and the importance of strong multilingual foundations. The code for our work can be found: https://github.com/microsoft/EfficientXLang.

Position bias, the tendency of Large Language Models (LLMs) to select content based on its structural position in a document rather than its semantic relevance, has been viewed as a key limitation in automatic summarization. To measure position bias, prior studies rely heavily on n-gram matching techniques, which fail to capture semantic relationships in abstractive summaries where content is extensively rephrased. To address this limitation, we apply a cross-encoder-based alignment method that jointly processes summary-source sentence pairs, enabling more accurate identification of semantic correspondences even when summaries substantially rewrite the source. Experiments with five LLMs across six summarization datasets reveal significantly different position bias patterns than those reported by traditional metrics. Our findings suggest that these patterns primarily reflect rational adaptations to document structure and content rather than true model limitations. Through controlled experiments and analyses across varying document lengths and multi-document settings, we show that LLMs use content from all positions more effectively than previously assumed, challenging common claims about “lost-in-the-middle” behaviour.

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FuzzAug: Data Augmentation by Coverage-guided Fuzzing for Neural Test Generation
Yifeng He | Jicheng Wang | Yuyang Rong | Hao Chen

Testing is essential to modern software engineering for building reliable software.Given the high costs of manually creating test cases,automated test case generation, particularly methods utilizing large language models,has become increasingly popular.These neural approaches generate semantically meaningful tests that are more maintainable compared with traditional automated testing methods such as fuzzing.However, the diversity and volume of unit tests in current datasets are limited, especially for newer but important languages.In this paper, we present a novel data augmentation technique, *FuzzAug*,that brings the benefits of fuzzing to large language models by incorporating valid testing semantics and providing diverse coverage-guided inputs.Doubling the size of training datasets,FuzzAug improves performance over the baselines significantly.This technique demonstrates the potential of introducing prior knowledge from dynamic software analysisto improve neural test generation,offering significant enhancements in this task.Our code is open-sourced at https://github.com/SecurityLab-UCD/FuzzAug.

Although large language models (LLMs) have demonstrated outperforming human experts in medical examinations, it remains challenging to adopt LLMs in real-world clinical decision-making that typically involves multi-hop medical reasoning. Common practices include prompting commercial LLMs and fine-tuning LLMs on medical data. However, in the clinical domain, using commercial LLMs raises privacy concerns regarding sensitive patient data. Fine-tuning competitive medical LLMs for different tasks usually requires extensive data and computing resources, which are difficult to acquire, especially in medical institutions with limited infrastructure. We propose DrAgent, which can build LLMs as agents to deliver accurate medical decision-making and reasoning. In implementation, we take a lightweight LLM as the backbone to collaborate with diverse clinical tools. To make efficient use of data, DrAgent introduces recursive curriculum learning to optimize the LLM in an easy-to-hard progression. The results show that our approach achieves competitive performance on diverse datasets.

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XRAG: Cross-lingual Retrieval-Augmented Generation
Wei Liu | Sony Trenous | Leonardo F. R. Ribeiro | Bill Byrne | Felix Hieber

We propose XRAG, a novel benchmark designed to evaluate the generation abilities of LLMs in cross-lingual Retrieval-Augmented Generation (RAG) settings where the user language does not match the retrieval results. XRAG is constructed from recent news articles to ensure that its questions require external know-ledge to be answered. It covers the real-world scenarios of monolingual and multilingual retrieval, and provides relevancy annotations for each retrieved document. Our novel dataset construction pipeline results in questions that require complex reasoning, as evidenced by the significant gap between human and LLM performance. Consequently, XRAG serves as a valuable benchmark for studying LLM reasoning abilities, even before considering the additional cross-lingual complexity. Experimental results on five LLMs uncover two previously unreported challenges in cross-lingual RAG: 1) in the monolingual retrieval setting, all evaluated models struggle with response language correctness; 2) in the multilingual retrieval setting, the main challenge lies in reasoning over retrieved information across languages rather than generation of non-English text.

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Can VLMs Recall Factual Associations From Visual References?
Dhananjay Ashok | Ashutosh Chaubey | Hirona Jacqueline Arai | Jonathan May | Jesse Thomason

Through a controlled study, we identify a systematic deficiency in the multimodal grounding of Vision Language Models (VLMs). While VLMs can recall factual associations when provided a textual reference to an entity, their ability to do so is significantly diminished when the reference is visual instead. Forcing VLMs to rely on image representations of an entity halves their ability to recall factual knowledge, suggesting that VLMs struggle to link their internal knowledge of an entity with its image representation. We show that such linking failures are correlated with the expression of distinct patterns in model internal states, and that probes on these internal states achieve over 92% accuracy at flagging cases where the VLM response is unreliable. These probes can be applied, without retraining, to identify when a VLM will fail to correctly answer a question that requires an understanding of multimodal input. When used to facilitate selective prediction on a visual question answering task, the probes increase coverage by 7.87% (absolute) while also reducing the risk of error by 0.9% (absolute). Addressing the systematic, detectable deficiency is an important avenue in language grounding, and we provide informed recommendations for future directions.

Ensuring the moral reasoning capabilities of Large Language Models (LLMs) is a growing concern as these systems are used in socially sensitive tasks. Nevertheless, current evaluation benchmarks present two major shortcomings: a lack of annotations that justify moral classifications, which limits transparency and interpretability; and a predominant focus on English, which constrains the assessment of moral reasoning across diverse cultural settings. In this paper, we introduce MFTCXplain, a multilingual benchmark dataset for evaluating the moral reasoning of LLMs via multi-hop hate speech explanations using the Moral Foundations Theory. MFTCXplain comprises 3,000 tweets across Portuguese, Italian, Persian, and English, annotated with binary hate speech labels, moral categories, and text span-level rationales. Our results show a misalignment between LLM outputs and human annotations in moral reasoning tasks. While LLMs perform well in hate speech detection (F1 up to 0.836), their ability to predict moral sentiments is notably weak (F1 < 0.35). Furthermore, rationale alignment remains limited mainly in underrepresented languages. Our findings show the limited capacity of current LLMs to internalize and reflect human moral reasoning.

In our era of widespread false information, human fact-checkers often face the challenge of duplicating efforts when verifying claims that may have already been addressed in other countries or languages. As false information transcends linguistic boundaries, the ability to automatically detect previously fact-checked claims across languages has become an increasingly important task. This paper presents the first comprehensive evaluation of large language models (LLMs) for multilingual previously fact-checked claim detection. We assess seven LLMs across 20 languages in both monolingual and cross-lingual settings. Our results show that while LLMs perform well for high-resource languages, they struggle with low-resource languages. Moreover, translating original texts into English proved to be beneficial for low-resource languages. These findings highlight the potential of LLMs for multilingual previously fact-checked claim detection and provide a foundation for further research on this promising application of LLMs.

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Debating for Better Reasoning in Vision-Language Models
Ashutosh Adhikari | Mirella Lapata

As Large Language Models (LLMs) gain expertise across diverse domains and modalities, scalable oversight becomes increasingly challenging, particularly when their capabilities may surpass human evaluators. Debate has emerged as a promising mechanism for enabling such oversight. We extend the debate paradigm to a multimodal setting, exploring its potential for blind models to supervise and enhance the performance of sighted ones. We focus on visual question answering (VQA), where two “sighted” expert vision-language models debate an answer, while a “blind” (text-only) judge adjudicates based solely on the quality of the arguments. In our framework, the experts only defend answers aligned with their beliefs, thereby obviating the need for explicit role-playing and concentrating the debate on instances of expert disagreement. Experiments on several multimodal tasks demonstrate that the debate framework consistently outperforms individual expert models. Moreover, judgments from blind LLMs can be used to instil reasoning capabilities in vision-language models through fine-tuning.

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Fine-tuning LLMs with Cross-Attention-based Weight Decay for Bias Mitigation
Farsheed Haque | Zhe Fu | Depeng Xu | Shuhan Yuan | Xi Niu

Large Language Models (LLMs) excel in Natural Language Processing (NLP) tasks but often propagate societal biases from their training data, leading to discriminatory outputs. These biases are amplified by the models’ self-attention mechanisms, which disproportionately emphasize biased correlations with sensitive tokens, like “he” or “she”, reflecting the sensitive attributes such as gender and race. To address this issue, we propose a novel fine-tuning method, called Cross-Attention-based Weight Decay (CrAWD), which modifies the LLM architecture to mitigate bias. CrAWD introduces a cross-attention mechanism between an input sequence and a sensitive token sequence, enabling the model to identify and selectively decay the attention weights of tokens associated with sensitive tokens. This reduces the influence of biased association on the model’s generation while maintaining task performance. Evaluations on real-world datasets demonstrate the effectiveness of our proposed CrAWD method. Notably, our method can handle multiple sensitive attributes by adjusting the sensitive token sequence, and it does not require full knowledge of sensitive tokens presented in the dataset, underscoring CrAWD’s versatility in promoting fair LLMs across various applications.

Large Language Models (LLMs) have demonstrated impressive capabilities in text generation but raise concerns regarding potential copyright infringement. While prior research has explored mitigation strategies like content filtering and alignment, the impact of adversarial persuasion techniques in eliciting copyrighted content remains underexplored. This paper investigates how structured persuasion strategies, including logical appeals, emotional framing, and compliance techniques, can be used to manipulate LLM outputs and potentially increase copyright risks. We introduce a structured persuasion workflow, incorporating query mutation, intention-preserving filtering, and few-shot prompting, to systematically analyze the influence of persuasive prompts on LLM responses. Through experiments on state-of-the-art LLMs, including GPT-4o-mini and Claude-3-haiku, we quantify the effectiveness of different persuasion techniques and assess their implications for AI safety. Our results highlight the vulnerabilities of LLMs to adversarial persuasion and provide empirical evidence of the increased risk of generating copyrighted content under such influence. We conclude with recommendations for strengthening model safeguards and future directions for enhancing LLM robustness against manipulation. Code is available at https://github.com/Rongite/Persuasion.

Responsible use of Authorship Verification (AV) systems not only requires high accuracy but also interpretable solutions. More importantly, for systems to be used to make decisions with real-world consequences requires the model’s prediction to be explainable using interpretable features that can be traced to the original texts. Neural methods achieve high accuracies, but their representations lack direct interpretability. Furthermore, LLM predictions cannot be explained faithfully – if there is an explanation given for a prediction, it doesn’t represent the reasoning process behind the model’s prediction. In this paper, we introduce Residualized Similarity (RS), a novel method that supplements systems using interpretable features with a neural network to improve their performance while maintaining interpretability. Authorship verification is fundamentally a similarity task, where the goal is to measure how alike two documents are. The key idea is to use the neural network to predict a similarity residual, i.e. the error in the similarity predicted by the interpretable system. Our evaluation across four datasets shows that not only can we match the performance of state-of-the-art authorship verification models, but we can show how and to what degree the final prediction is faithful and interpretable.

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Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation
Tunazzina Islam | Dan Goldwasser

Climate change communication on social media increasingly employs microtargeting strategies to effectively reach and influence specific demographic groups. This study presents a *post-hoc* analysis of microtargeting practices within climate campaigns by leveraging large language models (LLMs) to examine Meta (previously known as Facebook) advertisements. Our analysis focuses on two key aspects: **demographic targeting** and **fairness**. We evaluate the ability of LLMs to accurately predict the intended demographic targets, such as gender and age group. Furthermore, we instruct the LLMs to generate explanations for their classifications, providing transparent reasoning behind each decision. These explanations reveal the specific thematic elements used to engage different demographic segments, highlighting distinct strategies tailored to various audiences. Our findings show that ***young adults*** are primarily targeted through messages emphasizing *activism and environmental consciousness*, while **women** are engaged through themes related to *caregiving roles and social advocacy*. Additionally, we conduct a comprehensive fairness analysis to uncover biases in model predictions. We assess disparities in accuracy and error rates across demographic groups using established fairness metrics such as Demographic Parity, Equal Opportunity, and Predictive Equality. Our findings indicate that while LLMs perform well overall, certain biases exist, particularly in the classification of **male** audiences. The analysis of thematic explanations uncovers recurring patterns in messaging strategies tailored to various demographic groups, while the fairness analysis underscores the need for more inclusive targeting methods. This study provides a valuable framework for future research aimed at enhancing transparency, accountability, and inclusivity in social media-driven climate campaigns.

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MRFD: Multi-Region Fusion Decoding with Self-Consistency for Mitigating Hallucinations in LVLMs
Haonan Ge | Yiwei Wang | Ming-Hsuan Yang | Yujun Cai

Large Vision-Language Models (LVLMs) have shown strong performance across multimodal tasks. However, they often produce hallucinations—text that is inconsistent with visual input, due to the limited ability to verify information in different regions of the image. To address this, we propose **Multi-Region Fusion Decoding (MRFD)**, a training-free decoding method that improves factual grounding by modeling inter-region consistency. MRFD identifies salient regions using cross-attention, generates initial responses for each, and computes reliability weights based on Jensen-Shannon Divergence (JSD) among the responses. These weights guide a consistency-aware fusion of per-region predictions, using region-aware prompts inspired by Chain-of-Thought reasoning. Experiments across multiple LVLMs and benchmarks show that MRFD significantly reduces hallucinations and improves response factuality without requiring model updates.

When does a large language model (LLM) know what it does not know? Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output, and is therefore increasingly recognized as a crucial component of trusted AI systems. Black-box UQ methods do not require access to internal model information from the generating LLM and therefore have numerous real-world advantages, such as robustness to system changes, adaptability to choice of LLM, reduced costs, and computational tractability. In this paper, we investigate the effectiveness of UQ techniques that are primarily but not necessarily entirely black- box, where the consistency between a generated output and other sampled generations is used as a proxy for confidence in its correctness. We propose a high-level non-verbalized similarity-based aggregation framework that subsumes a broad swath of UQ approaches suitable for complex generative tasks, as well as introduce specific novel techniques from the framework that train confidence estimation models using small training sets. Through an empirical study with datasets spanning the diverse tasks of question answering, summarization, and text-to-SQL, we demonstrate that our proposed similarity-based methods can yield better calibrated confidences than baselines.

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Mind the Dialect: NLP Advancements Uncover Fairness Disparities for Arabic Users in Recommendation Systems
Abdulla Alshabanah | Murali Annavaram

Recommendation systems play a critical role in shaping user experiences and access to digital content. However, these systems can exhibit unfair behavior when their performance varies across user groups, especially in linguistically diverse populations. Recent advances in NLP have enabled the identification of user dialects, allowing for more granular analysis of such disparities. In this work, we investigate fairness disparities in recommendation quality among Arabic-speaking users, a population whose dialectal diversity is underrepresented in recommendation system research. By uncovering performance gaps across dialectal variation, we highlight the intersection of NLP and recommendation system and underscore the broader social impact of NLP. Our findings emphasize the importance of interdisciplinary approaches in building fair recommendation systems, particularly for global and local platforms serving diverse Arabic-speaking communities. The source code is available at https://github.com/alshabae/FairArRecSys.

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Hopscotch: Discovering and Skipping Redundancies in Language Models
Mustafa Eyceoz | Nikhil Shivakumar Nayak | Hao Wang | Ligong Han | Akash Srivastava

Modern causal language models stack many attention blocks to improve performance, but not all blocks are necessary for every task. We propose Hopscotch, a simple yet effective method that identifies and skips attention blocks with least contributions to a task and adapts to preserve output quality. Hopscotch jointly optimizes which blocks to skip and how to scale the outputs of the remaining layers. By introducing lightweight, trainable scaling parameters to attention and MLP blocks, it mitigates distribution shifts in hidden states caused by removing attention blocks. Hopscotch does not modify model weights or require access to pretraining or instruction-tuning data, and is compatible with existing model compression techniques. When applied to Llama-3.1-8B and Qwen-2.5-7B, Hopscotch achieves less than a 2% drop in performance even after skipping four attention blocks.

Existing metrics often lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports, resulting in suboptimal evaluation. We introduce a **Cl**inically grounded tabular framework with **E**xpert-curated labels and **A**ttribute-level comparison for **R**adiology report evaluation (**CLEAR**). CLEAR not only examines whether a report can accurately identify the presence or absence of medical conditions, but it also assesses whether the report can precisely describe each positively identified condition across five key attributes: first occurrence, change, severity, descriptive location, and recommendation. Compared with prior works, CLEAR’s multi-dimensional, attribute-level outputs enable a more comprehensive and clinically interpretable evaluation of report quality. Additionally, to measure the clinical alignment of CLEAR, we collaborated with five board-certified radiologists to develop **CLEAR-Bench**, a dataset of 100 chest radiograph reports from MIMIC-CXR, annotated across 6 curated attributes and 13 CheXpert conditions. Our experiments demonstrated that CLEAR achieves high accuracy in extracting clinical attributes and provides automated metrics that are strongly aligned with clinical judgment.

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Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages
Olga Kellert | Nemika Tyagi | Muhammad Imran | Nelvin Licona-Guevara | Carlos Gómez-Rodríguez

Code-switching presents a complex challenge for syntactic analysis, especially in low-resource language settings where annotated data is scarce. While recent work has explored the use of large language models (LLMs) for sequence-level tagging, few approaches systematically investigate how well these models capture syntactic structure in code-switched contexts. Moreover, existing parsers trained on monolingual treebanks often fail to generalize to multilingual and mixed-language input. To address this gap, we introduce the BiLingua Pipeline, an LLM-based annotation pipeline designed to produce Universal Dependencies (UD) annotations for code-switched text. First, we develop a prompt-based framework for Spanish-English and Spanish-Guaraní data, combining few-shot LLM prompting with expert review. Second, we release two annotated datasets, including the first Spanish-Guaraní UD-parsed corpus. Third, we conduct a detailed syntactic analysis of switch points across language pairs and communicative contexts. Experimental results show that BiLingua Pipeline achieves up to 95.29% LAS after expert revision, significantly outperforming prior baselines and multilingual parsers. These results show that LLMs, when carefully guided, can serve as practical tools for bootstrapping syntactic resources in under-resourced, code-switched environments.

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HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering
Runsong Jia | Mengjia Wu | Ying Ding | Jie Lu | Yi Zhang

Academic question answering (QA) in heterogeneous scholarly networks presents unique challenges requiring both structural understanding and interpretable reasoning. While graph neural networks (GNNs) capture structured graph information and large language models (LLMs) demonstrate strong capabilities in semantic comprehension, current approaches lack integration at the reasoning level. We propose HetGCoT, a framework enabling LLMs to effectively leverage and learn information from graphs to reason interpretable academic QA results. Our framework introduces three technical contributions: (1) a framework that transforms heterogeneous graph structural information into LLM-processable reasoning chains, (2) an adaptive metapath selection mechanism identifying relevant subgraphs for specific queries, and (3) a multi-step reasoning strategy systematically incorporating graph contexts into the reasoning process. Experiments on OpenAlex and DBLP datasets show our approach outperforms all sota baselines. The framework demonstrates adaptability across different LLM architectures and applicability to various scholarly question answering tasks.

Increasing test-time compute for LLMs shows promise across domains but remains underexplored in code generation, despite extensive study in math. In this paper, we propose S*, the first hybrid test-time scaling framework that substantially improves the coverage and selection accuracy of generated code. S* augments the existing parallel scaling approach with sequential scaling to further increase the performance. It further leverages a novel selection mechanism that adaptively generates distinguishing inputs for pairwise comparison, combined with execution-grounded information to robustly identify correct solutions.We evaluate S* across 12 Large Language Models and Large Reasoning Models and show that: (1) S* consistently improves performance across model families and sizes, enabling a 3B model to outperform GPT-4o-mini; (2) S* enables non-reasoning models to surpass reasoning models—GPT-4o-mini with S* outperforms o1-preview by 3.7% on LiveCodeBench; (3) S* further boosts state-of-the-art reasoning models—DeepSeek-R1-Distill-Qwen-32B with S* achieves 85.7% on LiveCodeBench, approaching o1 (high) at 88.5%. Codes, model generations and intermediate experiments results are available under Codes, model generations and intermediate ex-periments results are available under https://github.com/NovaSky-AI/SkyThought.

Large reasoning models (LRMs) tackle complex problems by following long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking, and self-validation. However, the training techniques and data requirements to elicit Long CoT remain poorly understood. In this work, we find that language models can effectively learn Long CoT reasoning through data-efficient supervised fine-tuning (SFT) and further parameter-efficient low-rank adaptation (LoRA). Crucially, we find that the structure of Long CoT is critical to the learning process in this data-efficient fine-tuning process. Training on content-incorrect examples, e.g. those lead to incorrect answers or corrupted digits, still leads to significant performance gains. In contrast, training on structurally incorrect examples, e.g., with shuffled or deleted reasoning steps, yield smaller improvements or even degrade performance.

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FSTs vs ICL: Generalisation in LLMs for an under-resourced language
Ximena Gutierrez | Mikel Segura Elizalde | Victor Mijangos

LLMs have been widely adopted to tackle many traditional NLP tasks. Their effectiveness remains uncertain in scenarios where pre-trained models have limited prior knowledge of a language. In this work, we examine LLMs’ generalization in under-resourced settings through the task of orthographic normalization across Otomi language variants. We develop two approaches: a rule-based method using a finite-state transducer (FST) and an in-context learning (ICL) method that provides the model with string transduction examples. We compare the performance of FSTs and neural approaches in low-resource scenarios, providing insights into their potential and limitations. Our results show that while FSTs outperform LLMs in zero-shot settings, ICL enables LLMs to surpass FSTs, stressing the importance of combining linguistic expertise with machine learning in current approaches for low-resource scenarios.

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SRM-LLM: Semantic Relationship Mining with LLMs for Temporal Knowledge Graph Extrapolation
Fu Zhang | Panfeng Zhang | Jingwei Cheng

Temporal knowledge graph (TKG) extrapolation aims to predict future facts by modeling the dynamic evolution of historical facts within TKGs. Existing methods often neglect the complex semantic relationships between relations when modeling their dynamic evolution, leading to incomplete relation representations and affecting the accuracy of reasoning. Inspired by the advancements in large language models (LLMs), we propose Semantic Relationship Mining based on LLMs (SRM-LLM), a novel approach for extracting semantic relationships to achieve TKG extrapolation. By leveraging LLMs to analyze the types of relations, we first identify several common relation types (e.g., causal, synonymous) in TKGs. We then design the LLM-based prompting strategy to capture latent semantic connections between relations, enabling the construction of relational association subgraphs for relation representation learning. In addition, SRM-LLM further enhances reasoning capabilities by incorporating structured logical constraints to guide inference. Experiments on five TKG datasets show significant performance gains and achieve new state of the art (SOTA) results, confirming the effectiveness of our method on TKG extrapolation tasks.

In text-video retrieval, auxiliary captions are often used to enhance video understanding, bridging the gap between the modalities. While recent advances in multi-modal large language models (MLLMs) have enabled strong zero-shot caption generation, we observe that such captions tend to be generic and indistinguishable across visually similar videos, limiting their utility for fine-grained retrieval. Moreover, conventional captioning approaches are typically evaluated using language generation metrics, such as BLEU, which are not typically tailored for retrieval tasks that require making discriminative distinctions between candidates. To address this, we propose CaRe-DPO, a retrieval framework that directly optimizes caption generation using retrieval relevance scores. At its core is Dual-Group Direct Preference Optimization (DG-DPO), a novel learning strategy that supervises captioning by modeling preferences across groups of distinct video and caption pairs. In addition, we present an MLLM-based retrieval model that incorporates role-embeddings to better distinguish between textual inputs with different functional roles, such as an auxiliary caption and a text query. Through extensive experiments, we demonstrate that CaRe-DPO significantly enhances retrieval performance by effectively leveraging auxiliary knowledge to generate fine-grained captions for retrieval. Code is available at https://github.com/mlvlab/CaReDPO.

Recent years have witnessed a growing interest in personalizing the responses of large language models (LLMs). While existing evaluations primarily focus on whether a response aligns with a user’s preferences, we argue that factuality is an equally important yet often overlooked dimension. In the context of personalization, we define a model as robust if its responses are both factually accurate and align with the user preferences. To assess this, we introduce PERG, a scalable framework for evaluating robustness of LLMs in personalization, along with a new dataset, PERGData. We evaluate fourteen models from five different model families using different prompting methods. Our findings show that current LLMs struggle with robust personalization: even the strongest models (GPT-4.1, LLaMA3-70B) fails to maintain correctness in 5% of previously successful cases without personalization, while smaller models (e.g., 7B scale) can fail more than 20% of the time. Further analysis reveals that robustness is significantly affected by the nature of the query and the type of user preference. To mitigate these failures, we propose Pref-Aligner, a two-stage approach that improves robustness by an average of 25% across models. Our work highlights critical gaps in current evaluation practices and introduces tools and metrics to support more reliable, user-aligned LLM deployments.

Meme captioning, the task of generating a sentence that describes the meaning of a meme, is both challenging and important in advancing Computational Meme Understanding (CMU). However, existing research has not explored its decomposition into subtasks or its connections to other CMU tasks. To address this gap, we introduce MemeInterpret, a meme corpus containing meme captions together with corresponding surface messages and relevant background knowledge. Strategically built upon the Facebook Hateful Memes dataset, MemeInterpret is the last piece in a set of corpora that unifies three major categories of CMU tasks for the first time. Extensive experiments on MemeInterpret and connected datasets suggest strong relationships between meme captioning, its two proposed subtasks, and the other two key categories of CMU tasks: classification and explanation. To stimulate further research on CMU, we make our dataset publicly available at https://github.com/npnkhoi/MemeInterpret.

Retrieval-Augmented Generation (RAG) is introduced to enhance Large Language Models (LLMs) by integrating external knowledge. However, conventional RAG approaches treat retrieved documents as independent units, often overlooking their interdependencies. Hybrid-RAG, a recently proposed paradigm that combines textual documents and graph-structured relational information for RAG, mitigates this limitation by collecting entity documents during graph traversal. However, existing methods only retrieve related documents from local neighbors or subgraphs in the knowledge base, which often miss relevant information located further away from a global view. To overcome the above challenges, we propose CoRAG that dynamically chooses whether to retrieve information through direct textual search or explore graph structures in the knowledge base. Our architecture blends different retrieval results, ensuring the potentially correct answer is chosen based on the query context. The textual retrieval components also enable global retrieval by scoring non-neighboring entity documents based on semantic relevance, bypassing the locality constraints of graph traversal. Experiments on semi-structured (relational and textual) knowledge base QA benchmarks demonstrate the outstanding performance of CoRAG.

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Hallucination Detection in Structured Query Generation via LLM Self-Debating
Miaoran Li | Jiangning Chen | Minghua Xu | Xiaolong Wang

Hallucination remains a key challenge in applying large language models (LLMs) to structured query generation, especially for semi-private or domain-specific languages underrepresented in public training data. In this work, we focus on hallucination detection in these low-resource structured language scenarios, using Splunk Search Processing Language (SPL) as a representative case study. We start from analyzing real-world SPL generation to define hallucination in this context and introduce a comprehensive taxonomy. To enhance detection performance, we propose the Self-Debating framework, which prompts an LLM to generate contrastive explanations from opposing perspectives before rendering a final consistency judgment. We also construct a synthetic benchmark, SynSPL, to support systematic evaluation of hallucination detection in SPL generation. Experimental results show that Self-Debating consistently outperforms LLM-as-a-Judge baselines with zero-shot and chain-of-thought (CoT) prompts in SPL hallucination detection across different LLMs, yielding 5–10% relative gains in hallucination F1 scores on both real and synthetic datasets, and up to 260% improvement for LLaMA-3.1–8B. Besides hallucination detection on SPL, Self-Debating also achieves excellent performance on the FaithBench benchmark for summarization hallucination, demonstrating the strong generalization ability of Self-Debating, with OpenAI o1-mini achieving state-of-the-art performance. All these results consistently demonstrate the strong robustness and wide generalizability of Self-Debating.

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Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing
Jongwoo Kim | SeongYeub Chu | Bryan Wong | Mun Yong Yi

Large Language Models (LLMs) have recently emerged as promising tools for knowledge tracing due to their strong reasoning and generalization abilities. While recent LLM-based KT methods have introduced new prompt formats, they struggle to reflect the histories of example learners within a single prompt during in-context learning (ICL), leading to limited scalability and high computational cost under token constraints. In this work, we present LLM-based Option weighted Knowledge Tracing (LOKT), a simple yet effective LLM-based knowledge tracing framework that encodes the interaction histories of example learners in context as textual categorical option weights (TCOW). These are semantic labels (e.g., “inadequate”) assigned to the options selected by learners when answering questions helping understand LLM. Experiments on multiple-choice datasets show that LOKT outperforms existing LLM-based KT models in both warm-start and few-shot settings. Moreover, LOKT enables scalable and cost-efficient inference, performing strongly even under strict token constraints. Our code is available at https://anonymous.4open.science/r/LOKT_model-3233

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Public Data Assisted Differentially Private In-Context Learning
Seongho Joo | Hyukhun Koh | Kyomin Jung

In-context learning (ICL) in Large Language Models (LLMs) has shown remarkable performance across various tasks without requiring fine-tuning. However, recent studies have highlighted the risk of private data leakage through the prompt in ICL, especially when LLMs are exposed to malicious attacks. While differential privacy (DP) provides strong privacy guarantees, it often significantly reduces the utility of in-context learning (ICL). To address this challenge, we incorporate task-related public data into the ICL framework while maintaining the DP guarantee. Based on this approach, we propose a private in-context learning algorithm that effectively balances privacy protection and model utility. Through experiments, we demonstrate that our approach significantly improves the utility of private ICL with the assistance of public data. Additionally, we show that our method is robust against membership inference attacks, demonstrating empirical privacy protection.

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Inducing Argument Facets for Faithful Opinion Summarization
Jian Wang | Yanjie Liang | Yuqing Sun | Bin Gong

Faithful opinion summarization task refers to generating a summary for a set of documents that covers the majority and minority opinions in documents. Inspired by the cognitive science that argument facet is the focus of an opinion, we propose the facets-guided opinion summarization method (FacSum). By inducing the facets, we partition the documents into multiple facet-specific sets. Then key phrases are extracted as the representatives of each set and the number of facets is used for constraining the length of summary, both of which are used to guide large language models (LLMs) to cover different argument facets of opinions while keeping the summary concise. We perform experiments on two representative datasets and the results show that our method outperforms the state-of-the-art (SOTA) methods and multiple LLMs. The ablation studies indicate that the introduced facets contribute to improving model performance by enabling the coverage of minority opinions while preserving the majority ones. The results based on different LLMs demonstrate that our method can improve the performance of LLMs with varying model sizes. We apply FacSum to the summarization of professional paper reviews, and the results confirm its effectiveness in specialty domains as well.

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Scaling Laws Are Unreliable for Downstream Tasks: A Reality Check
Nicholas Lourie | Michael Y. Hu | Kyunghyun Cho

Downstream scaling laws aim to predict task performance at larger scales from the model’s performance at smaller scales. Whether such prediction should be possible is unclear: some works discover clear linear scaling trends after simple transformations of the performance metric, whereas others point out fundamental challenges to downstream scaling laws, such as emergence and inverse scaling. In this work, we conduct a meta-analysis of existing data on downstream scaling laws, and we find that predictable scaling only occurs in a minority of cases: 39% of the time. Moreover, seemingly benign changes to the experimental setting can completely change the scaling behavior. Our analysis underscores the need to understand the conditions under which scaling laws succeed. To accurately model the relationship between pretraining loss and task performance, we must embrace the cases in which scaling behavior deviates from linear trends.

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Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation
Dongwon Jung | Qin Liu | Tenghao Huang | Ben Zhou | Muhao Chen

Retrieval-augmented generation (RAG) improves large language models (LMs) by incorporating non-parametric knowledge through evidence retrieved from external sources. However, it often struggles to cope with inconsistent and irrelevant information that can distract the LM from its tasks, especially when multiple evidence pieces are required. While compressing the retrieved evidence with a compression model aims to address this issue, the compressed evidence may still be unfamiliar to the target model used for downstream tasks, potentially failing to utilize the evidence effectively. We propose FaviComp (Familarity-Aware Evidence Compression), a novel training-free evidence compression technique that makes retrieved evidence more familiar to the target model, while seamlessly integrating parametric knowledge from the model. Experimental results show that FaviComp consistently outperforms the most recent evidence compression baselines across multiple open-domain QA datasets, improving accuracy by up to 28.1% while achieving high compression rates. Additionally, we demonstrate the effective integration of both parametric and non-parametric knowledge during evidence compression.

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O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion
Huu Tuong Tu | Huan Vu | Cuong Tien Nguyen | Dien Hy Ngo | Nguyen Thi Thu Trang

Traditional voice conversion (VC) methods typically attempt to separate speaker identity and linguistic information into distinct representations, which are then combined to reconstruct the audio. However, effectively disentangling these factors remains challenging, often leading to information loss during training. In this paper, we propose a new approach that leverages synthetic speech data generated by a high-quality, pretrained multispeaker text-to-speech (TTS) model. Specifically, synthetic data pairs that share the same linguistic content but differ in speaker identity are used as input-output pairs to train the voice conversion model. This enables the model to learn a direct mapping between source and target voices, effectively capturing speaker-specific characteristics while preserving linguistic content. Additionally, we introduce a flexible training strategy for any-to-any voice conversion that generalizes well to unseen speakers and new languages, enhancing adaptability and performance in zero-shot scenarios. Our experiments show that our proposed method achieves a 16.35% relative reduction in word error rate and a 5.91% improvement in speaker cosine similarity, outperforming several state-of-the-art methods. Voice conversion samples can be accessed at: https://oovc-emnlp-2025.github.io/

Large language models (LLMs) often mislead users with confident hallucinations. Current approaches to detect hallucination require many samples from the LLM generator, which is computationally infeasible as frontier model sizes and generation lengths continue to grow. We present a remarkably simple baseline for detecting hallucinations in long-form LLM generations, with performance comparable to expensive multi-sample approaches while drawing only a single sample from the LLM generator. Our key finding is that LLM hidden states are highly predictive of factuality in long-form natural language generation and that this information can be efficiently extracted at inference time using a lightweight probe. We benchmark a variety of long-form hallucination detection methods across open-weight models up to 405B parameters and demonstrate that our approach achieves competitive performance with up to 100x fewer FLOPs. Furthermore, our probes generalize to out-of-distribution model outputs, evaluated using hidden states of smaller open-source models. Our results demonstrate the promise of hidden state probes in detecting long-form LLM hallucinations.

Sequential Recommendation Systems (SRS) have become essential in many real-world applications. However, existing SRS methods often rely on collaborative filtering signals and fail to capture real-time user preferences, while Conversational Recommendation Systems (CRS) excel at eliciting immediate interests through natural language interactions but neglect historical behavior. To bridge this gap, we propose CESRec, a novel framework that integrates the long-term preference modeling of SRS with the real-time preference elicitation of CRS. We introduce semantic-based pseudo interaction construction, which dynamically updates users’ historical interaction sequences by analyzing conversational feedback, generating a pseudo-interaction sequence that seamlessly combines long-term and real-time preferences. Additionally, we reduce the impact of outliers in historical items that deviate from users’ core preferences by proposing dual alignment outlier items masking, which identifies and masks such items using semantic-collaborative aligned representations. Extensive experiments demonstrate that CESRec achieves state-of-the-art performance by boosting strong SRS models, validating its effectiveness in integrating conversational feedback into SRS.

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TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation
Chengrui Huang | Shen Gao | Zhengliang Shi | Dongsheng Wang | Shuo Shang

Existing tool-learning methods usually rely on supervised fine-tuning, they often overlook fine-grained optimization of internal tool call details, leading to limitations in preference alignment and error discrimination. To overcome these challenges, we propose **T**oken-level **T**ool-use **P**reference **A**lignment Training Framework (TTPA), a training paradigm for constructing token-level tool-use preference datasets that align LLMs with fine-grained preferences using a novel error-oriented scoring mechanism. TTPA first introduces reversed dataset construction, a method for creating high-quality, multi-turn tool-use datasets by reversing the generation flow. Additionally, we propose _Preference Oriented Tool-use Dataset Construction_ to capture fine-grained preferences by modeling token-level differences during generation. To address biases in scoring, we introduce the _Error-oriented Scoring Mechanism_, which quantifies tool-call errors and can be used as a training signal. Extensive experiments on three diverse benchmark datasets demonstrate that TTPA significantly improves tool-using performance while showing strong generalization ability across models and datasets.

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Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition
Yi Liu | Xiangrong Zhu | Xiangyu Liu | Wei Wei | Wei Hu

In a rapidly evolving world where information updates swiftly, knowledge in large language models (LLMs) becomes outdated quickly. Retraining LLMs is not a cost-effective option, making knowledge editing (KE) without modifying parameters particularly necessary. We find that although existing retrieval-augmented generation (RAG)-based KE methods excel at editing simple knowledge, they struggle with KE in multi-hop question answering due to the issue of ”edit skipping”, which refers to skipping the relevant edited fact in inference. In addition to the diversity of natural language expressions of knowledge, edit skipping also arises from the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory. To address this issue, we propose a novel Iterative Retrieval-Augmented Knowledge Editing method with guided decomposition (IRAKE) through the guidance from single edited facts and entire edited cases. Experimental results demonstrate that IRAKE mitigates the failure of editing caused by edit skipping and outperforms state-of-the-art methods for KE in multi-hop question answering.

Large Language Models (LLMs) have shown significant limitations in understanding creative content, as demonstrated by Hessel et al. (2023)’s influential work on the New Yorker Cartoon Caption Contest (NYCCC). Their study exposed a substantial gap between LLMs and humans in humor comprehension, establishing that understanding and evaluating creative content is key challenge in AI development. We revisit this challenge by decomposing humor understanding into three components and systematically improve each: enhancing visual understanding through improved annotation, utilizing LLM-generated humor reasoning and explanations, and implementing targeted alignment with human preference data. Our refined approach achieves 82.4% accuracy in caption ranking, significantly improving upon the previous 67% benchmark and matching the performance of world-renowned human experts in this domain. Notably, while attempts to mimic subgroup preferences through various persona prompts showed minimal impact, model finetuning with crowd preferences proved remarkably effective. These findings reveal that LLM limitations in creative judgment can be effectively addressed through focused alignment to specific subgroups and individuals. Lastly, we propose the position that achieving artificial general intelligence necessitates systematic collection of human preference data across creative domains. We advocate that just as human creativity is deeply influenced by individual and cultural preferences, training LLMs with diverse human preference data may be essential for developing true creative understanding.

We present SMARTMiner, a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching (HC) notes. Developed in response to challenges observed during a clinical trial, the SMARTMiner achieves two tasks: (i) extracting behavior change goal spans and (ii) categorizing their SMARTness. We also introduce SMARTSpan, the first publicly available dataset of 173 HC notes annotated with 266 goals and SMART attributes. SMARTMiner incorporates an extractive goal retriever with a component-wise SMARTness classifier. Experiment results show that extractive models significantly outperformed their generative counterparts in low-resource settings, and that two-stage fine-tuning substantially boosted performance. The SMARTness classifier achieved up to 0.91 SMART F1 score, while the full SMARTMiner maintained high end-to-end accuracy. This work bridges healthcare, behavioral science, and natural language processing to support health coaches and clients with structured goal tracking - paving way for automated weekly goal reviews between human-led HC sessions. Both the code and the dataset are available at: https://github.com/IvaBojic/SMARTMiner.

Retrieval-Augmented Generation (RAG) has significantly mitigated the hallucinations of Large Language Models (LLMs) by grounding the generation with external knowledge. Recent extensions of RAG to graph-based retrieval offer a promising direction, leveraging the structural knowledge for multi-hop reasoning. However, existing graph RAG typically decouples retrieval and reasoning processes, which prevents the retriever from adapting to the reasoning needs of the LLM. They also struggle with scalability when performing multi-hop expansion over large-scale graphs, or depend heavily on annotated ground-truth entities, which are often unavailable in open-domain settings. To address these challenges, we propose a novel graph retriever trained end-to-end with LLM, which features an attention-based growing and pruning mechanism, adaptively navigating multi-hop relevant entities while filtering out noise. Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together, thereby enhancing its reasoning capability and facilitating interactive joint training of the graph retriever and the LLM reasoner. Experimental results across three QA benchmarks show that our approach consistently achieves state-of-the-art performance, validating the strength of joint graph–LLM optimization for complex reasoning tasks. Notably, our framework eliminates the need for predefined ground-truth entities by directly optimizing the retriever using LLM logits as implicit feedback, making it especially effective in open-domain settings.

Deductive and inductive reasoning are fundamental components of human cognition, and in daily life, people often apply these types of reasoning unconsciously. While previous studies have extensively examined the deductive and inductive reasoning abilities of Large Language Models (LLMs) in rule-based and math-related tasks, little attention has been given to their role in procedural planning——an area that holds considerable relevance for real-world applications. To fill this gap, we present DIRPP (Deductive and Inductive Reasoning in Procedural Planning) in this paper, a benchmark designed to assess the deductive and inductive reasoning abilities of various LLMs within the context of procedural planning. Based on the benchmark, we initially observe that LLMs demonstrate excellent deductive reasoning capabilities in procedural planning but show suboptimal performance in inductive reasoning. To enhance their inductive reasoning abilities, we further propose a novel and effective method called IMSE (Induction through Multiple Similar Examples), which enables LLMs to generate multiple similar procedural plans and then perform inductive reasoning based on these examples. Through various experiments, we find that the proposed method can significantly improve the inductive reasoning capabilities of LLMs.

Socratic teaching, known for its emphasis on heuristic questioning and deep thinking, has demonstrated significant advantages in promoting students’ cognitive development. However, traditional Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings. Recent breakthroughs in large language models (LLMs) in natural language generation and dialogue comprehension offer the potential for automated Socratic teaching. In this paper, we propose Knowledge-Enlightened Learning Enhanced by LLMs (KELE), a novel multi-agent framework for structured Socratic teaching with LLMs. KELE constructs a structured Socratic teaching rule system (SocRule) and a “consultant–teacher” multi-agent collaborative teaching mechanism, in which two LLMs respectively take charge of teaching planning and execution, ensuring a logically coherent and hierarchically structured Socratic teaching process. We also construct SocratDataset, a structured Socratic teaching dataset covering 34 teaching strategies and over 42,000 dialogue turns, and train SocratTeachLLM, a specialized LLM for Socratic teaching tasks. Additionally, we build a comprehensive Socratic teaching quality evaluation system for LLMs, covering 9 dimensions from single-turn dialogue to multi-turn teaching processes. Experimental results show that SocratTeachLLM significantly outperforms GPT-4o, which has a much larger parameter size, across all Socratic teaching capabilities.

Generating logically coherent video from text (T2V) for reasoning-intensive tasks like mathematical problem-solving presents a significant challenge for Vision-Language Models (VLMs). Therefore, we introduce VisualEDU, a benchmark based on Manim package to rigorously evaluate VLM capabilities in producing coherent, step-by-step video solutions for educational purposes, with a framework that integrates meta-prompt learning, visual and code feedback, and a modular drawing toolkit to enhance output quality. Novel metrics for temporal consistency, logical correctness, and visual clarity are proposed, and extensive experiments across nine VLMs reveal that while advanced proprietary models show promise, all struggle significantly with increasing task complexity (e.g., the performances of Claude-3.7-Sonnet and GPT-4o are below 56% on difficult tasks ), highlighting limitations in code generation, visual feedback correction and precise tool invocation. VisualEDU offers a robust platform for systematic T2V assessment in reasoning-intensive domains and guides future VLM improvements in this area.

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OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Question Answering
Yulong Hui | Yihao Liu | Yao Lu | Huanchen Zhang

Large Language Models (LLMs) encounter challenges in efficiently answering long-text questions, as seen in applications like enterprise document analysis and financial report comprehension. While conventional solutions employ long-context processing or Retrieval-Augmented Generation (RAG), they suffer from prohibitive input expenses or incomplete information. Recent advancements adopt context compression and dynamic retrieval loops, but still sacrifice critical details or incur iterative costs. To address these limitations, we propose OkraLong, a novel framework that flexibly optimizes the entire processing workflow. Unlike prior static or coarse-grained adaptive strategies, OkraLong adopts fine-grained orchestration through three synergistic components: analyzer, organizer and executor. The analyzer characterizes the task states, which guide the organizer in dynamically scheduling the workflow. The executor carries out the execution and generates the final answer. Experimental results demonstrate that OkraLong not only enhances answer accuracy by 5.7%-41.2%, but also achieves cost savings of 1.3x-4.7x.

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VerifiAgent: a Unified Verification Agent in Language Model Reasoning
Jiuzhou Han | Wray Buntine | Ehsan Shareghi

Large language models demonstrate remarkable reasoning capabilities but often produce unreliable or incorrect responses. Existing verification methods are typically model-specific or domain-restricted, requiring significant computational resources and lacking scalability across diverse reasoning tasks. To address these limitations, we propose VerifiAgent, a unified verification agent that integrates two levels of verification: meta-verification, which assesses completeness and consistency in model responses, and tool-based adaptive verification, where VerifiAgent autonomously selects appropriate verification tools based on the reasoning type, including mathematical, logical, or commonsense reasoning. This adaptive approach ensures both efficiency and robustness across different verification scenarios. Experimental results show that VerifiAgent outperforms baseline verification methods (e.g., deductive verifier, backward verifier) among all reasoning tasks. Additionally, it can further enhance reasoning accuracy by leveraging feedback from verification results. VerifiAgent can also be effectively applied to inference scaling, achieving better results with fewer generated samples and costs compared to existing process reward models in the mathematical reasoning domain.

Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information. Recently, generative large language models (LLMs) have been increasingly employed for graph tasks. However, current approaches typically encode graph context in textual form, which fails to fully exploit the potential of LLMs for perceiving and reasoning about graph structures. To address this limitation, we propose DrKGC (Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion). DrKGC employs a flexible lightweight model training strategy to learn structural embeddings and logical rules within the KG. It then leverages a novel bottom-up graph retrieval method to extract a subgraph for each query guided by the learned rules. Finally, a graph convolutional network (GCN) adapter uses the retrieved subgraph to enhance the structural embeddings, which are then integrated into the prompt for effective LLM fine-tuning. Experimental results on two general domain benchmark datasets and two biomedical datasets demonstrate the superior performance of DrKGC. Furthermore, a realistic case study in the biomedical domain highlights its interpretability and practical utility.

Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem-solving. Investigating the internal reasoning mechanisms of these models can help us design better model architectures and training strategies, ultimately enhancing their reasoning capability. In this study, we constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning. We introduced the concept of buffer mechanism: the model stores various information in distinct buffers and selectively extracts it through the query-key matrix. We proposed a random matrix-based algorithm to enhance the model’s reasoning ability. This algorithm introduces only 132 trainable parameters, yet leads to significant performance improvements on 7 multi-step reasoning datasets, including PrOntoQA, LogicAsker, and LogicInference. These findings provide new insights into understanding the large language models.

Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes. However, this enhanced reasoning capability results in an increased number of output tokens during inference, leading to higher computational costs. To address this challenge, we propose TwT (Thinking without Tokens), a method that reduces inference-time costs through habitual reasoning distillation with multi-teachers’ guidance, while maintaining high performance. Our approach introduces a Habitual Reasoning Distillation method, which internalizes explicit reasoning into the model’s habitual behavior through a Teacher-Guided compression strategy inspired by human cognition. Additionally, we propose Dual-Criteria Rejection Sampling (DCRS), a technique that generates a high-quality and diverse distillation dataset using multiple teacher models, making our method suitable for unsupervised scenarios. Experimental results demonstrate that TwT effectively reduces inference costs while preserving superior performance, achieving up to a 13.6% improvement in accuracy with fewer output tokens compared to other distillation methods, offering a highly practical solution for efficient LLM deployment.

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DAVIS: Planning Agent with Knowledge Graph-Powered Inner Monologue
Minh Pham Dinh | Michael G Yankoski | Munira Syed | Trenton W. Ford

Designing a generalist scientific agent capable of performing tasks in laboratory settings to assist researchers has become a key goal in recent Artificial Intelligence (AI) research. Unlike everyday tasks, scientific tasks are inherently more delicate and complex, requiring agents to possess a higher level of reasoning ability, structured and temporal understanding of their environment, and a strong emphasis on safety. Existing approaches often fail to address these multifaceted requirements. To tackle these challenges, we present DAVIS. Unlike traditional retrieval-augmented generation (RAG) approaches, DAVIS incorporates structured and temporal memory, which enables model-based planning. Additionally, DAVIS implements an agentic, multi-turn retrieval system, similar to a human’s inner monologue, allowing for a greater degree of reasoning over past experiences. DAVIS demonstrates substantially improved performance on the ScienceWorld benchmark comparing to previous approaches on 8 out of 9 elementary science subjects. In addition, DAVIS’s World Model demonstrates competitive performance on the famous HotpotQA and MusiqueQA dataset for multi-hop question answering. To the best of our knowledge, DAVIS is the first RAG agent to employ an interactive retrieval method in a RAG pipeline.

As large language models (LLMs) are increasingly applied to real-world scenarios, it becomes crucial to understand their ability to follow multiple instructions simultaneously. To systematically evaluate these capabilities, we introduce two specialized benchmarks for fundamental domains where multiple instructions following is important: Many Instruction-Following Eval (ManyIFEval) for text generation with up to ten instructions, and Style-aware Mostly Basic Programming Problems (StyleMBPP) for code generation with up to six instructions. Our experiments with the created benchmarks across ten LLMs reveal that performance consistently degrades as the number of instructions increases. Furthermore, given the fact that evaluating all the possible combinations of multiple instructions is computationally impractical in actual use cases, we developed three types of regression models that can estimate performance on both unseen instruction combinations and different numbers of instructions which are not used during training. We demonstrate that a logistic regression model using instruction count as an explanatory variable can predict performance of following multiple instructions with approximately 10% error, even for unseen instruction combinations. We show that relatively modest sample sizes (500 for ManyIFEval and 300 for StyleMBPP) are sufficient for performance estimation, enabling efficient evaluation of LLMs under various instruction combinations.

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FormosanBench: Benchmarking Low-Resource Austronesian Languages in the Era of Large Language Models
Kaiying Kevin Lin | Hsi-Yu Chen | Haopeng Zhang

While large language models (LLMs) have demonstrated impressive performance across a wide range of natural language processing (NLP) tasks in high-resource languages, their capabilities in low-resource and minority languages remain significantly underexplored. Formosan languages—a subgroup of Austronesian languages spoken in Taiwan—are both linguistically rich and endangered, largely due to the sociolinguistic dominance of Mandarin. In this work, we introduce FormosanBench, the first benchmark for evaluating LLMs on low-resource Austronesian languages. It covers three endangered Formosan languages: Atayal, Amis, and Paiwan, across three core NLP tasks: machine translation, automatic speech recognition (ASR), and text summarization. We assess model performance in zero-shot, 10-shot, and fine-tuned settings using FormosanBench. Our results reveal a substantial performance gap between high-resource and Formosan languages. Existing LLMs consistently underperform across all tasks, with 10-shot learning and fine-tuning offering only limited improvements. These findings underscore the urgent need for more inclusive NLP technologies that can effectively support endangered and underrepresented languages. We release our datasets and code to facilitate future research in this direction :https://anonymous.4open.science/r/FormosanBench-DB43/

Existing alignment methods for preference optimization of large language models (LLMs) aim to enhance model performance by utilizing pairs of positive and negative samples. However, due to the limited capacity of models in scoring or generating responses, the quality of positive and negative samples may become similar during training, which complicates optimization for preference learning. To address this issue, we introduce SeaPO, a Strategic Error Amplification method that leverages three error types commonly occurring in LLMs to introduce specific error patterns into the model Preference Optimization. This strategy ensures that negative samples are more erroneous than positive samples and preference-based training is employed to mitigate the occurrence of these errors, thereby enhancing model performance. Evaluations across five capability dimensions and different model scales (1.5B to 14B) demonstrate that the generated data significantly improved overall model performance, particularly in terms of truthfulness, with improvements of 5–10 percentage points observed. Further analysis reveals that task performance varies depending on the error types introduced. Injecting the most common error types improves performance in related tasks, while a mix of error types leads to a broader performance enhancement: most tasks show stable improvements, while a few tasks exhibit significant gains.

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FigEx: Aligned Extraction of Scientific Figures and Captions
Jifeng Song | Arun Das | Ge Cui | Yufei Huang

Automatic understanding of figures in scientific papers is challenging since they often contain subfigures and subcaptions in complex layouts. In this paper, we propose FigEx, a vision-language model to extract aligned pairs of subfigures and subcaptions from scientific papers. We also release BioSci-Fig, a curated dataset of 7,174 compound figures with annotated subfigure bounding boxes and aligned subcaptions. On BioSci-Fig, FigEx improves subfigure detection AP^b over Grounding DINO by 0.023 and boosts caption separation BLEU over Llama-2-13B by 0.465. The source code is available at: https://github.com/Huang-AI4Medicine-Lab/FigEx.

Text Image Machine Translation (TIMT) aims to translate texts embedded within an image into another language. Current TIMT studies primarily focus on providing translations for all the text within an image, while neglecting to provide bounding boxes and covering limited scenarios. In this work, we extend traditional TIMT into position-aware TIMT (PATIMT), aiming to support fine-grained and layout-preserving translation, which holds great practical value but remains largely unexplored. This task comprises two key sub-tasks: region-specific translation and full-image translation with grounding. To support existing models on PATIMT and conduct fair evaluation, we construct the PATIMT benchmark (PATIMT-Bench), which consists of 10 diverse real-world scenarios. Specifically, we introduce an Adaptive Image OCR Refinement Pipeline, which adaptively selects appropriate OCR tools based on scenario and refines the results of text-rich images. To ensure evaluation reliability, we further construct a test set, which contains 1,200 high-quality instances manually annotated and reviewed by human experts. After fine-tuning on our data, compact Large Vision-Language Models (LVLMs) achieve state-of-the-art performance on both sub-tasks. Experimental results also highlight the scalability and generalizability of our training data.

Fine-tuning large language models (LLMs) for downstream tasks often leads to catastrophic forgetting, notably degrading the safety of originally aligned models. While some existing methods attempt to restore safety by incorporating additional safety data, the quality of such data typically falls short of that used in the original alignment process. Moreover, these high-quality safety datasets are generally inaccessible, making it difficult to fully recover the model’s original safety. We ask: How can we preserve safety while improving downstream task performance without additional safety data? We show that simply merging the weights of pre- and post-fine-tuned models effectively mitigates safety degradation while enhancing performance. Experiments across different downstream tasks and models validate the method’s practicality and effectiveness.

Although Large Language Models (LLMs) perform well in general fields, they exhibit a **confidence distortion problem** on multi-choice question-answering (MCQA), particularly as the number of answer choices increases. Specifically, on MCQA with many choices, LLMs suffer from under-confidence in correct predictions and over-confidence in incorrect ones, leading to a substantially degraded performance. To solve this problem, we propose Self-Ensemble in this work. Our method splits the choices into several groups and ensembles LLM predictions across these groups to reach a final decision. The advantage of Self-Ensemble is its plug-and-play nature, where it can be integrated into existing LLM architecture based on a designed attention mask and positional encoding, without requiring labeled datasets for parameter tuning. Experimental results on three LLMs and datasets demonstrate that Self-Ensemble comprehensively addresses the confidence distortion problem of LLMs, outperforming standard inference as well as baseline methods.

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Annotation-Efficient Language Model Alignment via Diverse and Representative Response Texts
Yuu Jinnai | Ukyo Honda

Preference optimization is a standard approach to fine-tuning large language models to align with human preferences. The quantity, diversity, and representativeness of the preference dataset are critical to the effectiveness of preference optimization. However, obtaining a large amount of preference annotations is difficult in many applications. This raises the question of how to use the limited annotation budget to create an effective preference dataset. To this end, we propose Annotation-Efficient Preference Optimization (AEPO). Instead of exhaustively annotating preference over all available response texts, AEPO selects a subset of responses that maximizes diversity and representativeness from the available responses and then annotates preference over the selected ones. In this way, AEPO focuses the annotation budget on labeling preferences over a smaller but informative subset of responses. We evaluate the performance of preference learning using AEPO on three datasets and show that it outperforms the baselines with the same annotation budget.

Recent advances in chain-of-thought (CoT) prompting have demonstrated the ability of large language models (LLMs) to perform multi-step reasoning. While prior work focuses on improving CoT generation quality or attributing token-level importance, we propose a novel framework to structurally analyze the latent dynamics of CoT trajectories for interpretability. Our method segments generated CoT into discrete reasoning steps, abstracts each step into a spectral embedding based on the eigenvalues of token-level Gram matrices, and clusters these embeddings into semantically meaningful latent states. We model the global evolution of reasoning as a first-order Markov chain over latent clusters, yielding interpretable transition structures. Through t-SNE visualizations and Monte Carlo rollouts, we uncover consistent trajectories across tasks and models, supporting the hypothesis that LLM reasoning follows globally coherent yet abstract paths.

In high-stakes domains such as healthcare and finance, effective decision-making demands not just accurate outcomes but transparent and explainable reasoning. However, current language models often lack the structured deliberation needed for such tasks, instead generating decisions and justifications in a disconnected, post-hoc manner. To address this, we propose DecisionFlow, a novel decision modeling framework that guides models to reason over structured representations of actions, attributes, and constraints. Rather than predicting answers directly from prompts, DecisionFlow builds a semantically grounded decision space and infers a latent utility function to evaluate trade-offs in a transparent, utility-driven manner. This process produces decisions tightly coupled with interpretable rationales reflecting the model’s reasoning. Empirical results on two high-stakes benchmarks show that DecisionFlow not only achieves up to 30% accuracy gains over strong prompting baselines but also enhances alignment in outcomes. Our work is a critical step toward integrating symbolic reasoning with LLMs, enabling more accountable, explainable, and reliable LLM decision support systems. Code and data are at https://github.com/xiusic/DecisionFlow.

With the widespread application of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), enhancing their performance has become a research hotspot. This paper presents a novel multi-prompt ensemble decoding approach designed to bolster the generation quality of LLMs by leveraging the aggregation of outcomes from multiple prompts. Given a unique input X, we submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions. For each token prediction, we calculate the ensemble probability by averaging the n probability distributions within the batch, utilizing this aggregated probability to generate the token. This technique is dubbed Inner-Batch Ensemble. To facilitate efficient batch inference, we implement a Left-Padding strategy to maintain uniform input lengths across the n prompts. Through extensive experimentation on diverse NLP tasks, including code generation, text simplification and machine translation, we demonstrate the efficacy of our method in enhancing LLM performance. The results show substantial improvements in pass@k rates, LENS metrics and BLEU scores over conventional methods.

The emergence of the tool agent paradigm has broadened the capability boundaries of the Large Language Model (LLM), enabling it to complete more complex tasks. However, the effectiveness of this paradigm is limited due to the issue of parameter failure during its execution. To explore this phenomenon and propose corresponding suggestions, we first construct a parameter failure taxonomy in this paper. We derive five failure categories from the invocation chain of a mainstream tool agent. Then, we explore the correlation between three different input sources and failure categories by applying 15 input perturbation methods to the input. Experimental results show that parameter name hallucination failure primarily stems from inherent LLM limitations, while issues with input sources mainly cause other failure patterns. To improve the reliability and effectiveness of tool-agent interactions, we propose corresponding improvement suggestions, including standardizing tool return formats, improving error feedback mechanisms, and ensuring parameter consistency.

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FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering
Yitao Long | Tiansheng Hu | Yilun Zhao | Arman Cohan | Chen Zhao

Large Language Models (LLMs) frequently hallucinate to long-form questions, producing plausible yet factually incorrect answers. A common mitigation strategy is to provide attribution to LLM outputs. However, existing benchmarks primarily focus on simple attribution that retrieves supporting textual evidence as references. We argue that in real-world scenarios such as financial applications, attribution goes beyond reference retrieval.We introduce FinLFQA, a benchmark designed to evaluate the ability of LLMs to generate long-form answers to complex financial questions with reliable and nuanced attributions. FinLFQA evaluates three critical aspects of attribution through human annotations: (1) supporting evidence extracted from financial reports, (2) intermediate numerical reasoning steps, and (3) domain-specific financial knowledge that informs the reasoning process.We further provide an automatic evaluation framework covering both answer quality and attribution quality. Through extensive experiments on eight LLMs across multiple attribution-generation paradigms, we find that fine-grained metrics are important to distinguish model capabilities, that end-to-end generation achieves comparable performance to post-hoc approaches, and that iterative refinement only helps when guided by external feedback.

Existing multilingual benchmarks focus primarily on language understanding tasks. There is a lack of benchmarks to measure comprehensive critical capabilities of large language models (LLMs) across diverse languages, including instruction following, reasoning, code generation, and long context understanding. To bridge this gap, we develop BenchMAX, a multi-way multilingual benchmark that covers 10 diverse tasks, to evaluate LLMs’ general abilities across many languages. To ensure high data quality, each sample is post-edited by three native annotators after machine-translating from English into 16 languages. Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve. BenchMAX serves as a comprehensive multilingual evaluation platform, providing a promising test bed to promote the development of multilingual language models. The dataset and code are publicly accessible.

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Assessing the Sensitivity and Alignment of FOL Closeness Metrics
Ramya Keerthy Thatikonda | Wray Buntine | Ehsan Shareghi

The recent successful paradigm of solving logical reasoning problems with tool-augmented large language models (LLMs) leverages translation of natural language (NL) statements into First-Order Logic (FOL) and external theorem provers. However, the correctness of FOL statements, comprising operators and text, often go unverified due to the lack of a reliable evaluation metric for comparing generated and ground-truth FOLs. In this paper, we conduct a comprehensive study on the sensitivity of existing metrics—NL, FOL, and graph-based— and their alignment with LLM as a judge on FOL evaluation to measure robustness. We introduce operator and text-based perturbations to ground-truth FOL statements to assess metric sensitivity. We then evaluate metric robustness by comparing them against LLMs judgement. Our empirical findings highlight a clear oversensitivity in the n-gram metric BLEU for text perturbations. The operator perturbation affects the semantic graph metric Smatch++ for structural changes, and the FOL metric for specific operator changes. We observe a closer alignment between BertScore and LLM judgement, proving the importance of semantic evaluation. Additionally, we show that combining metrics enhances both robustness and sensitivity compared to using individual metrics.

Food safety demands timely detection, regulation, and public communication, yet the lack of structured datasets hinders Natural Language Processing (NLP) research. We present and release a new dataset of human-written and Large Language Model (LLM)-generated summaries of food safety documents, plus food safety related metadata. We evaluate its utility on three NLP tasks directly reflecting food safety practices: multilabel classification for organizing documents into domain-specific categories; document retrieval for accessing regulatory and scientific evidence; and question answering via retrieval-augmented generation that improves factual accuracy.We show that LLM summaries perform comparably or better than human ones across tasks. We also demonstrate clustering of summaries for event tracking and compliance monitoring. This dataset enables NLP applications that support core food safety practices, including the organization of regulatory and scientific evidence, monitoring of compliance issues, and communication of risks to the public.

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Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios
Jingen Qu | Lijun Li | Bo Zhang | Yichen Yan | Jing Shao

Multimodal large language models (MLLMs) are rapidly evolving, presenting increasingly complex safety challenges. However, current dataset construction methods, which are risk-oriented, fail to cover the growing complexity of real-world multimodal safety scenarios (RMS). And due to the lack of a unified evaluation metric, their overall effectiveness remains unproven. This paper introduces a novel image-oriented self-adaptive dataset construction method for RMS, which starts with images and end constructing paired text and guidance responses. Using the image-oriented method, we automatically generate an RMS dataset comprising 35,610 image–text pairs with guidance responses. Additionally, we introduce a standardized safety dataset evaluation metric: fine-tuning a safety judge model and evaluating its capabilities on other safety datasets. Extensive experiments on various tasks demonstrate the effectiveness of the proposed image-oriented pipeline. The results confirm the scalability and effectiveness of the image-oriented approach, offering a new perspective for the construction of real-world multimodal safety datasets.

The diversity of human language, shaped by social, cultural, and regional influences, presents significant challenges for natural language processing (NLP) systems. Existing benchmarks often overlook intra-language variations, leaving speakers of non-standard dialects underserved. To address this gap, we introduce EnDive (English Diversity), a benchmark that evaluates seven state-of-the-art (SOTA) large language models (LLMs) across tasks in language understanding, algorithmic reasoning, mathematics, and logic. Our framework translates Standard American English datasets into five underrepresented dialects using few-shot prompting with verified examples from native speakers, and compares these translations against rule-based methods via fluency assessments, preference tests, and semantic similarity metrics. Human evaluations confirm high translation quality, with average scores of at least 6.02/7 for faithfulness, fluency, and formality. By filtering out near-identical translations, we create a challenging dataset that reveals significant performance disparities—models consistently underperform on dialectal inputs compared to Standard American English (SAE). EnDive thus advances dialect-aware NLP by uncovering model biases and promoting more equitable language technologies.

The efficacy of Large Language Models (LLMs) in long-context tasks is often hampered by the substantial memory footprint and computational demands of the Key-Value (KV) cache. Current compression strategies, including token eviction and learned projections, frequently lead to biased representations—either by overemphasizing recent/high-attention tokens or by repeatedly degrading information from earlier context—and may require costly model retraining. We present FAEDKV (Frequency-Adaptive Infinite-Window for KV cache), a novel, training-free KV cache compression framework that ensures unbiased information retention. FAEDKV operates by transforming the KV cache into the frequency domain using a proposed Infinite-Window Fourier Transform (IWDFT). This approach allows for the equalized contribution of all tokens to the compressed representation, effectively preserving both early and recent contextual information. A preliminary frequency ablation study identifies critical spectral components for layer-wise, targeted compression. Experiments on LongBench benchmark demonstrate FAEDKV’s superiority over existing methods by up to 22%. In addition, our method shows superior, position-agnostic retrieval accuracy on the Needle-In-A-Haystack task compared to compression based approaches.

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Dynamic Injection of Entity Knowledge into Dense Retrievers
Ikuya Yamada | Ryokan Ri | Takeshi Kojima | Yusuke Iwasawa | Yutaka Matsuo

Dense retrievers often struggle with queries involving less-frequent entities due to their limited entity knowledge. We propose the Knowledgeable Passage Retriever (KPR), a BERT-based retriever enhanced with a context-entity attention layer and dynamically updatable entity embeddings. This design enables KPR to incorporate external entity knowledge without retraining. Experiments on three datasets demonstrate that KPR consistently improves retrieval accuracy, with particularly large gains on the EntityQuestions dataset. When built on the off-the-shelf bge-base retriever, KPR achieves state-of-the-art performance among similarly sized models on two datasets. Models and code are released at https://github.com/knowledgeable-embedding/knowledgeable-embedding.

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When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning
Yijiang River Dong | Tiancheng Hu | Yinhong Liu | Ahmet Üstün | Nigel Collier

While Reinforcement Learning from Human Feedback (RLHF) is widely used to align Large Language Models (LLMs) with human preferences, it typically assumes homogeneous preferences across users, overlooking diverse human values and minority viewpoints.Although personalized preference learning addresses this by tailoring separate preferences for individual users, the field lacks standardized methods to assess its effectiveness. We present a multi-faceted evaluation framework that measures not only performance but also fairness, unintended effects, and adaptability across varying levels of preference divergence. Through extensive experiments comparing eight personalization methods across three preference datasets, we demonstrate that performance differences between methods could reach 36% when users strongly disagree, and personalization can introduce up to 20% safety misalignment. These findings highlight the critical need for holistic evaluation approaches to advance the development of more effective and inclusive preference learning systems.

Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains due to their specialized agentic roles and collaborative interactions. However, this also amplifies the severity of security risks under MAS attacks. To address this, we introduce MASTER, a novel security research framework for MAS, focusing on diverse Role configurations and Topological structures across various scenarios. MASTER offers an automated construction process for different MAS setups and an information-flow-based interaction paradigm. To tackle MAS security challenges in varied scenarios, we design a scenario-adaptive, extensible attack strategy utilizing role and topological information, which dynamically allocates targeted, domain-specific attack tasks for collaborative agent execution. Our experiments demonstrate that such an attack, leveraging role and topological information, exhibits significant destructive potential across most models. Additionally, we propose corresponding defense strategies, substantially enhancing MAS resilience across diverse scenarios. We anticipate that our framework and findings will provide valuable insights for future research into MAS security challenges.

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MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices
Patara Trirat | Jae-Gil Lee

The growing use of smartphones and IoT devices necessitates efficient time-series analysis on resource-constrained hardware, which is critical for sensing applications such as human activity recognition and air quality prediction. Recent efforts in hardware-aware neural architecture search (NAS) automate architecture discovery for specific platforms; however, none focus on general time-series analysis with edge deployment. Leveraging the problem-solving and reasoning capabilities of large language models (LLM), we propose ***MONAQ***, a novel framework that reformulates NAS into ***M***ulti-***O***bjective ***N***eural ***A***rchitecture ***Q***uerying tasks. *MONAQ* is equipped with *multimodal query generation* for processing multimodal time-series inputs and hardware constraints, alongside an *LLM agent-based multi-objective search* to achieve deployment-ready models via code generation. By integrating numerical data, time-series images, and textual descriptions, *MONAQ* improves an LLM’s understanding of time-series data. Experiments on fifteen datasets demonstrate that *MONAQ*-discovered models outperform both handcrafted models and NAS baselines while being more efficient.

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StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos
Valentin Barriere | Nahuel Gomez | Léo Hemamou | Sofia Callejas | Brian Ravenet

Aiming towards improving current computational models of humor detection, we propose a new multimodal dataset of stand-up comedies, in seven languages: English, French, Spanish, Italian, Portuguese, Hungarian and Czech. Our dataset of more than 330 hours %, is at the time of writing the biggest available for this type of task, and the most diverse. The whole dataset is automatically annotated in laughter (from the audience), and the subpart left for model validation is manually annotated.% Contrary to contemporary approaches, we do not frame the task of humor detection as a binary sequence classification, but as word-level sequence labeling, in order to take into account all the context of the sequence and to capture the continuous joke tagging mechanism typically occurring in natural conversations. As par with unimodal baselines results, we propose a method for e propose a method to enhance the automatic laughter detection based on Audio Speech Recognition errors. Our code and data are available online: https://tinyurl.com/EMNLPHumourStandUpAnonym

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Does Visual Grounding Enhance the Understanding of Embodied Knowledge in Large Language Models?
Zhihui Yang | Yupei Wang | Kaijie Mo | Zhe Zhao | Renfen Hu

Despite significant progress in multimodal language models (LMs), it remains unclear whether visual grounding enhances their understanding of embodied knowledge compared to text-only models. To address this question, we propose a novel embodied knowledge understanding benchmark based on the perceptual theory from psychology, encompassing visual, auditory, tactile, gustatory, olfactory external senses, and interoception. The benchmark assesses the models’ perceptual abilities across different sensory modalities through vector comparison and question-answering tasks with over 1,700 questions. By comparing 30 state-of-the-art LMs, we surprisingly find that vision-language models (VLMs) do not outperform text-only models in either task. Moreover, the models perform significantly worse in the visual dimension compared to other sensory dimensions. Further analysis reveals that the vector representations are easily influenced by word form and frequency, and the models struggle to answer questions involving spatial perception and reasoning. Our findings underscore the need for more effective integration of embodied knowledge in LMs to enhance their understanding of the physical world.

Retrieval-Augmented Generation (RAG) plays a critical role in mitigating hallucinations and improving factual accuracy for Large Language Models (LLMs). While dynamic retrieval techniques aim to determine retrieval timing and content based on model intrinsic needs, existing approaches struggle to generalize effectively in black-box model scenarios. To address this limitation, we propose the Semantic Contribution-Aware Adaptive Retrieval (SCAAR) framework. SCAAR iteratively leverages the semantic importance of words in upcoming sentences to dynamically adjust retrieval thresholds and filter information, retaining the top-𝛼% most semantically significant words for constructing retrieval queries. We comprehensively evaluate SCAAR against baseline methods across four long-form, knowledge-intensive generation datasets using four models. Our method achieved the highest score on each dataset with GPT-4o. Extensive experiments also analyze the impact of various hyperparameters within the framework. Our results demonstrate SCAAR’s superior or competitive performance, showcasing its ability to effectively detect model retrieval needs and construct efficient retrieval queries for relevant knowledge about problem-solving in black-box scenarios. Our code is available on https://github.com/linqinhong/SAC.

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On Guardrail Models’ Robustness to Mutations and Adversarial Attacks
Elias Bassani | Ignacio Sanchez

The risk of generative AI systems providing unsafe information has raised significant concerns, emphasizing the need for safety guardrails. To mitigate this risk, guardrail models are increasingly used to detect unsafe content in human-AI interactions, complementing the safety alignment of Large Language Models. Despite recent efforts to evaluate those models’ effectiveness, their robustness to input mutations and adversarial attacks remains largely unexplored. In this paper, we present a comprehensive evaluation of 15 state-of-the-art guardrail models, assessing their robustness to: a) input mutations, such as typos, keywords camouflage, ciphers, and veiled expressions, and b) adversarial attacks designed to bypass models’ safety alignment. Those attacks exploit LLMs capabilities like instruction-following, role-playing, personification, reasoning, and coding, or introduce adversarial tokens to induce model misbehavior. Our results reveal that most guardrail models can be evaded with simple input mutations and are vulnerable to adversarial attacks. For instance, a single adversarial token can deceive them 44.5% of the time on average. The limitations of the current generation of guardrail models highlight the need for more robust safety guardrails.

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IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data
Bo Peng | Zhiheng Wang | Heyang Gong | Chaochao Lu

In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial. However, the scarcity of high-quality data remains a fundamental challenge to evaluating and improving this capability. Traditional dataset construction methods are labor-intensive, resource-demanding, and raise privacy concerns. To address these issues, we propose a novel approach for automatic synthetic data generation and introduce the **I**mplicit **P**ersonalized **Dialog**ue (**IP-Dialog**) benchmark along with a training dataset, covering 10 tasks and 12 user attribute types. Additionally, we develop a systematic evaluation framework with four metrics to assess both attribute awareness and reasoning capabilities. We further propose five causal graphs to elucidate models’ reasoning pathways during implicit personalization. Extensive experiments yield insightful observations and prove the reliability of our dataset.

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Zero-shot Graph Reasoning via Retrieval Augmented Framework with LLMs
Hanqing Li | Sharika Mahadevan | Kiran Jyothi Sheena | Henry Liang | Diego Klabjan

We propose a new, training-free method, Graph Reasoning via Retrieval Augmented Framework (GRRAF), that harnesses retrieval-augmented generation (RAG) alongside the code-generation capabilities of large language models (LLMs) to address a wide range of graph reasoning tasks. In GRRAF, the target graph is stored in a graph database, and the LLM is prompted to generate executable code queries that retrieve the necessary information. This approach circumvents the limitations of existing methods that require extensive finetuning or depend on predefined algorithms, and it incorporates an error feedback loop with a time-out mechanism to ensure both correctness and efficiency. Experimental evaluations on the GraphInstruct dataset reveal that GRRAF achieves 100% accuracy on most graph reasoning tasks, including cycle detection, bipartite graph checks, shortest path computation, and maximum flow, while maintaining consistent token costs regardless of graph sizes. Imperfect but still very high performance is observed on subgraph matching. Notably, GRRAF scales effectively to large graphs with up to 10,000 nodes.

The increasing autonomy of LLM agents in handling sensitive communications, accelerated by Model Context Protocol (MCP) and Agent-to-Agent (A2A) frameworks, creates urgent privacy challenges. While recent work reveals significant gaps between LLMs’ privacy Q&A performance and their agent behavior, existing benchmarks remain limited to static, simplified scenarios. We present PrivacyChecker, a model-agnostic, contextual integrity based mitigation approach that effectively reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o, all while preserving task helpfulness. We also introduce PrivacyLens-Live, transforming static benchmarks into dynamic MCP and A2A environments that reveal substantially higher privacy risks in practical. Our modular mitigation approach integrates seamlessly into agent protocols through three deployment strategies, providing practical privacy protection for the emerging agentic ecosystem. Our data and code will be made available at https://aka.ms/privacy_in_action.

Logical reasoning is a core capability for large language models (LLMs), yet existing benchmarks that rely solely on final-answer accuracy fail to capture the quality of the reasoning process. To address this, we introduce FineLogic, a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. Leveraging this framework, we conduct a comprehensive study on how different supervision formats in fine-tuning shape reasoning abilities. We fine-tune LLMs on four supervision styles—one in natural language and three symbolic variants—and find a key trade-off: natural language supervision excels at generalization to out-of-distribution and long-chain problems, whereas symbolic supervision is superior at instilling structurally sound, atomic reasoning steps. Furthermore, our probing analysis indicates that fine-tuning primarily refines the model’s step-by-step generation process, rather than improving its ability to converge on an answer early. Together, our framework and analysis provide a more rigorous lens for evaluating and improving logical reasoning in LLMs. The code is available at https://github.com/YujunZhou/FineLogic.

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ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models
Razvan-Gabriel Dumitru | Darius Peteleaza | Vikas Yadav | Liangming Pan

Large language models excel at complex tasks by breaking down problems into structured reasoning steps. However, reasoning traces often extend beyond reaching a correct answer, causing wasted computation, reduced readability, and hallucinations. To address this, we introduce a novel hyperparameter-free conciseness score used as a reward signal within a reinforcement learning framework to guide models toward generating correct and concise reasoning traces. This score is evaluated by a large language model acting as a judge, enabling dynamic, context-aware feedback beyond simple token length. Our method achieves state-of-the-art efficiency–accuracy trade-offs on the MATH dataset, reducing token usage by up to 31x on simple problems while improving accuracy by 7%, and on the hardest problems, it outperforms full reasoning by +7.5% accuracy with up to 3.6x fewer tokens. On TheoremQA, our method improves accuracy by +2.2% using 12.5x fewer tokens. We also conduct ablation studies on the judge model, reward composition, and problem difficulty, showing that our method dynamically adapts reasoning length based on problem difficulty and benefits significantly from stronger judges. The code, model weights, and datasets are open-sourced at https://github.com/RazvanDu/ConciseRL.

Test-Time Scaling (TTS) has proven effective in improving the performance of Large Language Models (LLMs) during inference. However, existing research has overlooked the efficiency of TTS from a latency-sensitive perspective. Through a latency-aware evaluation of representative TTS methods, we demonstrate that a compute-optimal TTS does not always result in the lowest latency in scenarios where latency is critical. To address this gap and achieve latency-optimal TTS, we propose two key approaches by optimizing the concurrency configurations: (1) branch-wise parallelism, which leverages multiple concurrent inference branches, and (2) sequence-wise parallelism, enabled by speculative decoding. By integrating these two approaches and allocating computational resources properly to each, our latency-optimal TTS enables a 32B model to reach 82.3% accuracy on MATH-500 within 1 minute and a smaller 3B model to achieve 72.4% within 10 seconds. Our work emphasizes the importance of latency-aware TTS and demonstrates its ability to deliver both speed and accuracy in latency-sensitive scenarios.

Multi-turn jailbreak attacks simulate real-world human interactions by engaging large language models (LLMs) in iterative dialogues, exposing critical safety vulnerabilities. However, existing methods often struggle to balance semantic coherence with attack effectiveness, resulting in either benign semantic drift or ineffective detection evasion. To address this challenge, we propose Reasoning-Augmented Conversation (RACE), a novel multi-turn jailbreak framework that reformulates harmful queries into benign reasoning tasks and leverages LLMs’ strong reasoning capabilities to compromise safety alignment. Specifically, we introduce an attack state machine framework to systematically model problem translation and iterative reasoning, ensuring coherent query generation across multiple turns. Building on this framework, we design gain-guided exploration, self-play, and rejection feedback modules to preserve attack semantics, enhance effectiveness, and sustain reasoning-driven attack progression. Extensive experiments on multiple LLMs demonstrate that RACE achieves state-of-the-art attack effectiveness in complex conversational scenarios, with attack success rates (ASRs) increasing by up to 96%. Notably, our approach achieves average ASR of 83.3% against leading commercial models, including Gemini 2.0 Flashing Thinking and OpenAI o1, underscoring its potency.

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Distilling Many-Shot In-Context Learning into a Cheat Sheet
Ukyo Honda | Soichiro Murakami | Peinan Zhang

Recent advances in large language models (LLMs) enable effective in-context learning (ICL) with many-shot examples, but at the cost of high computational demand due to longer input tokens. To address this, we propose cheat-sheet ICL, which distills the information from many-shot ICL into a concise textual summary (cheat sheet) used as the context at inference time. Experiments on challenging reasoning tasks show that cheat-sheet ICL achieves comparable or better performance than many-shot ICL with far fewer tokens, and matches retrieval-based ICL without requiring test-time retrieval. These findings demonstrate that cheat-sheet ICL is a practical alternative for leveraging LLMs in downstream tasks.

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Tracing Training Footprints: A Calibration Approach for Membership Inference Attacks Against Multimodal Large Language Models
Xiaofan Zheng | Huixuan Zhang | Xiaojun Wan

With the increasing scale of training data for Multimodal Large Language Models (MLLMs) and the lack of data details, there is growing concern about privacy breaches and data security issues. Under black-box access, exploring effective Membership Inference Attacks (MIA) has garnered increasing attention. In real-world applications, where most samples are non-members, the issue of non-members being over-represented in the data manifold, leading to misclassification as member samples, becomes more prominent. This has motivated recent work to focus on developing effective difficulty calibration strategies, producing promising results. However, these methods only consider text-only input during calibration, and their effectiveness is diminished when migrated to MLLMs due to the presence of visual embeddings. To address the above problem, we propose PC-MMIA, focusing on visual instruction fine-tuning data. PC-MMIA is based on the idea that tokens located in poorly generalized local manifolds can better reflect traces of member samples that have been trained. By employing bidirectional perturbation of image embeddings to capture tokens critical to MIA and assigning them different weights, we achieve difficulty calibration. Experimental results demonstrate that our proposed method surpasses existing methods.

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PolBiX: Detecting LLMs’ Political Bias in Fact-Checking through X-phemisms
Charlott Jakob | David Harbecke | Patrick Parschan | Pia Wenzel Neves | Vera Schmitt

Large Language Models are increasingly used in applications requiring objective assessment, which could be compromised by political bias. Many studies found preferences for left-leaning positions in LLMs, but downstream effects on tasks like fact-checking remain underexplored. In this study, we systematically investigate political bias through exchanging words with euphemisms or dysphemisms in German claims. We construct minimal pairs of factually equivalent claims that differ in political connotation, to assess the consistency of LLMs in classifying them as true or false. We evaluate six LLMs and find that, more than political leaning, the presence of judgmental words significantly influences truthfulness assessment. While a few models show tendencies of political bias, this is not mitigated by explicitly calling for objectivism in prompts. Warning: This paper contains content that may be offensive or upsetting.

Recent advances in large language models (LLMs) have driven significant progress in end-to-end spoken dialogue models (SDMs). In contrast to text-based LLMs, the evaluation framework for SDMs should encompass both cognitive dimensions (e.g., logical reasoning, knowledge) and speech-related aspects (e.g., paralinguistic cues, audio quality). However, there is still a lack of comprehensive evaluations for SDMs in speech-to-speech (S2S) scenarios. To address this gap, we propose **URO-Bench**, an extensive benchmark for SDMs. Notably, URO-Bench is the first S2S benchmark that covers evaluations about multilingualism, multi-round dialogues, and paralinguistics. Our benchmark is divided into two difficulty levels: basic track and pro track, each comprising 20 test sets, evaluating the spoken dialogue model’s abilities in **U**nderstanding, **R**easoning, and **O**ral conversation. Evaluations on our proposed benchmark reveal that current open-source SDMs perform rather well in daily QA tasks, but lag behind their backbone LLMs in terms of instruction-following ability and also suffer from catastrophic forgetting. Their performance in advanced evaluations of paralinguistic information and audio understanding remains subpar, highlighting the need for further research in this direction. We hope that URO-Bench can facilitate the development of spoken dialogue models by providing a multifaceted evaluation of existing models and helping to track progress in this area.

Large Language Models (LLMs) have shown promising results in coreference resolution, especially after fine-tuning. However, recent generative approaches face a critical issue: hallucinations—where the model generates content not present in the original input. These hallucinations make evaluation difficult and decrease overall performance. To address this issue, we analyze the underlying causes of hallucinations and propose a low-hallucination and efficient solution. Specifically, we introduce Efficient Constrained Decoding for Coreference Resolution, which maintains strong robustness while significantly improving computational efficiency. On the English OntoNotes development set, our approach achieved slightly better performance than previous state-of-the-art methods, while requiring substantially fewer parameters.

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Your Mileage May Vary: How Empathy and Demographics Shape Human Preferences in LLM Responses
Yishan Wang | Amanda Cercas Curry | Flor Miriam Plaza-del-Arco

As large language models (LLMs) increasingly assist in subjective decision-making (e.g., moral reasoning, advice), it is critical to understand whose preferences they align with—and why. While prior work uses aggregate human judgments, demographic variation and its linguistic drivers remain underexplored. We present a comprehensive analysis of how demographic background and empathy level correlate with preferences for LLM-generated dilemma responses, alongside a systematic study of predictive linguistic features (e.g., agency, emotional tone). Our findings reveal significant demographic divides and identify markers (e.g., power verbs, tentative phrasing) that predict group-level differences. These results underscore the need for demographically informed LLM evaluation.

Object hallucinations in Large Vision-Language Models (LVLMs) significantly impede their real-world applicability. As the primary component for accurately interpreting visual information, the choice of visual encoder is pivotal. We hypothesize that the diverse training paradigms employed by different visual encoders instill them with distinct inductive biases, which leads to their diverse hallucination performances. Existing benchmarks typically focus on coarse-grained hallucination detection and fail to capture the diverse hallucinations elaborated in our hypothesis. To systematically analyze these effects, we introduce VHBench-10, a comprehensive benchmark for evaluating LVLMs across ten fine-grained hallucination categories. Our evaluations confirm encoders exhibit unique hallucination characteristics. Building on these insights and the suboptimality of simple feature fusion, we propose VisionWeaver, a novel Context-Aware Routing Network. It employs global visual features to generate routing signals, dynamically aggregating visual features from multiple specialized experts. Comprehensive experiments confirm the effectiveness of VisionWeaver in significantly reducing hallucinations and improving overall model performance. Our code and benchmark are available at https://github.com/whwangovo/VisionWeaver.

Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in diverse reasoning tasks, yet their application to complex physics reasoning remains underexplored. Physics reasoning presents unique challenges, requiring grounding in physical conditions and the interpretation of multimodal information. Current physics benchmarks are limited, often focusing on text-only inputs or solely on problem-solving, thereby overlooking the critical intermediate steps of variable identification and process formulation. To address these limitations, we introduce **PhysicsArena, the first multimodal physics reasoning benchmark designed to holistically evaluate MLLMs across three critical dimensions: variable identification, physical process formulation, and solution derivation.** PhysicsArena aims to provide a comprehensive platform for assessing and advancing the multimodal physics reasoning abilities of MLLMs.

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Ko-LongRAG: A Korean Long-Context RAG Benchmark Built with a Retrieval-Free Approach
Yongil Kim | Heuiyeen Yeen | Hyeongu Yun | Jinsik Lee

The rapid advancement of large language models (LLMs) significantly enhances long-context Retrieval-Augmented Generation (RAG), yet existing benchmarks focus primarily on English. This leaves low-resource languages without comprehensive evaluation frameworks, limiting their progress in retrieval-based tasks. To bridge this gap, we introduce Ko-LongRAG, the first Korean long-context RAG benchmark. Unlike conventional benchmarks that depend on external retrievers, Ko-LongRAG adopts a retrieval-free approach designed around Specialized Content Knowledge (SCK), enabling controlled and high-quality QA pair generation without the need for an extensive retrieval infrastructure. Our evaluation shows that o1 model achieves the highest performance among proprietary models, while EXAONE 3.5 leads among open-sourced models. Additionally, various findings confirm Ko-LongRAG as a reliable benchmark for assessing Korean long-context RAG capabilities and highlight its potential for advancing multilingual RAG research. The dataset and source code will be released publicly.

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Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI
Annika Bush | Meltem Aksoy | Markus Pauly | Greta Ontrup

As organizations increasingly rely on AI systems for decision support in sustainability contexts, it becomes critical to understand the inherent biases and perspectives embedded in Large Language Models (LLMs). This study systematically investigates how five state-of-the-art LLMs – Claude, DeepSeek, GPT, LLaMA, and Mistral – conceptualize sustainability and its relationship with AI. We administered validated, psychometric sustainability-related questionnaires – each 100 times per model – to capture response patterns and variability. Our findings revealed significant inter-model differences: For example, GPT exhibited skepticism about the compatibility of AI and sustainability, whereas LLaMA demonstrated extreme techno-optimism with perfect scores for several Sustainable Development Goals (SDGs). Models also diverged in attributing institutional responsibility for AI and sustainability integration, a results that holds implications for technology governance approaches. Our results demonstrate that model selection could substantially influence organizational sustainability strategies, highlighting the need for awareness of model-specific biases when deploying LLMs for sustainability-related decision-making.

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Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics
Yuxuan Ye | Raul Santos-Rodriguez | Edwin Simpson

Reinforcement learning with evaluation metrics as rewards is widely used to enhance specific capabilities of language models. However, for tasks such as factually consistent summarisation, existing metrics remain underdeveloped, limiting their effectiveness as signals for shaping model behaviour.While individual factuality metrics are unreliable, their combination can more effectively capture diverse factual errors. We leverage this insight to introduce an automated training pipeline that improves factual consistency in summaries by aggregating scores from different weak metrics. Our approach avoids the need for complex reward shaping by mapping scores to preferences and filtering out cases with high disagreement between metrics. For each source document, we generate lexically similar summary pairs by varying decoding strategies, enabling the model to learn from factual differences caused by subtle lexical differences. This approach constructs a high-quality preference dataset using only source documents.Experiments demonstrate consistent factuality gains across models, ranging from early encoder-decoder architectures to modern large language models, with smaller models reaching comparable factuality to larger ones.

LLM-as-Judge has emerged as a scalable alternative to human evaluation, enabling large language models (LLMs) to provide reward signals in trainings. While recent work has explored multi-agent extensions such as multi-agent debate and meta-judging to enhance evaluation quality, the question of how intrinsic biases manifest in these settings remains underexplored. In this study, we conduct a systematic analysis of four diverse bias types: position bias, verbosity bias, chain-of-thought bias, and bandwagon bias. We evaluate these biases across two widely adopted multi-agent LLM-as-Judge frameworks: Multi-Agent-Debate and LLM-as-Meta-Judge. Our results show that debate framework amplifies biases sharply after the initial debate, and this increased bias is sustained in subsequent rounds, while meta-judge approaches exhibit greater resistance. We further investigate the incorporation of PINE, a leading single-agent debiasing method, as a bias-free agent within these systems. The results reveal that this bias-free agent effectively reduces biases in debate settings but provides less benefit in meta-judge scenarios. Our work provides a comprehensive study of bias behavior in multi-agent LLM-as-Judge systems and highlights the need for targeted bias mitigation strategies in collaborative evaluation settings.

Metaphorical language is prevalent in everyday communication, often used unconsciously, as in “rising crime.” While LLMs excel at identifying metaphors in text, they struggle with downstream tasks that implicitly require correct metaphor interpretation, such as natural language inference (NLI). This work explores how LLMs perform on NLI with metaphorical input. Particularly, we investigate whether incorporating conceptual metaphors (source and target domains) enhances performance in zero-shot and few-shot settings. Our contributions are two-fold: (1) we extend metaphorical texts in an existing NLI dataset by source and target domains, and (2) we conduct an ablation study using Shapley values and interactions to assess the extent to which LLMs interpret metaphorical language correctly in NLI. Our results indicate that incorporating conceptual metaphors often improves task performance.

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KurTail : Kurtosis-based LLM Quantization
Mohammad Sadegh Akhondzadeh | Aleksandar Bojchevski | Evangelos Eleftheriou | Martino Dazzi

One challenge of quantizing a large language model (LLM) is the presence of outliers. Outliers often make uniform quantization schemes less effective, particularly in extreme cases such as 4-bit quantization. We introduce KurTail, a new post-training quantization (PTQ) scheme that leverages Kurtosis-based rotation to mitigate outliers in the activations of LLMs. Our method optimizes Kurtosis as a measure of tailedness. This approach enables the quantization of weights, activations, and the KV cache in 4 bits. We utilize layer-wise optimization, ensuring memory efficiency. KurTail outperforms existing quantization methods, offering a 13.3% boost in MMLU accuracy and a 15.5% boost in Wiki perplexity compared to QuaRot. It also outperforms SpinQuant with a 2.6% MMLU gain and reduces perplexity by 2.9%, all while reducing the training cost. For comparison, learning the rotation using SpinQuant for Llama3-70B requires at least four NVIDIA H100 80GB GPUs, whereas our method requires only a single GPU, making it more accessible.

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VIVA+: Human-Centered Situational Decision-Making
Zhe Hu | Yixiao Ren | Guanzhong Liu | Jing Li | Yu Yin

Multimodal Large Language Models (MLLMs) show promising results for embodied agents in operating meaningfully in complex, human-centered environments. Yet, evaluating their capacity for nuanced, human-like reasoning and decision-making remains challenging. In this work, we introduce VIVA+, a cognitively grounded benchmark for evaluating the reasoning and decision-making of MLLMs in human-centered situations. VIVA+ consists of 1,317 real-world situations paired with 6,373 multiple-choice questions, targeting three core abilities for decision-making: (1) Foundational Situation Comprehension, (2) Context-Driven Action Justification, and (3) Reflective Reasoning. Together, these dimensions provide a systematic framework for assessing a model’s ability to perceive, reason, and act in socially meaningful ways. We evaluate the latest commercial and open-source models on VIVA+, where we reveal distinct performance patterns and highlight significant challenges. We further explore targeted training and multi-step reasoning strategies, which yield consistent performance improvements. Finally, our in-depth analysis highlights current model limitations and provides actionable insights for advancing MLLMs toward more robust, context-aware, and socially adept decision-making in real-world settings.

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QuantAgents: Towards Multi-agent Financial System via Simulated Trading
Xiangyu Li | Yawen Zeng | Xiaofen Xing | Jin Xu | Xiangmin Xu

In this paper, our objective is to develop a multi-agent financial system that incorporates simulated trading, a technique extensively utilized by financial professionals. While current LLM-based agent models demonstrate competitive performance, they still exhibit significant deviations from real-world fund companies. A critical distinction lies in the agents’ reliance on “post-reflection”, particularly in response to adverse outcomes, but lack a distinctly human capability: long-term prediction of future trends. Therefore, we introduce QuantAgents, a multi-agent system integrating simulated trading, to comprehensively evaluate various investment strategies and market scenarios without assuming actual risks. Specifically, QuantAgents comprises four agents: a simulated trading analyst, a risk control analyst, a market news analyst, and a manager, who collaborate through several meetings. Moreover, our system incentivizes agents to receive feedback on two fronts: performance in real-world markets and predictive accuracy in simulated trading. Extensive experiments demonstrate that our framework excels across all metrics, yielding an overall return of nearly 300% over the three years (https://quantagents.github.io).

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LLMs Reproduce Stereotypes of Sexual and Gender Minorities
Ruby Ostrow | Adam Lopez

A large body of research has found substantial gender bias in NLP systems. Most of this research takes a binary, essentialist view of gender: limiting its variation to the categories _men_ and _women_, conflating gender with sex, and ignoring different sexual identities. But gender and sexuality exist on a spectrum, so in this paper we study the biases of large language models (LLMs) towards sexual and gender minorities beyond binary categories. Grounding our study in a widely used social psychology model—the Stereotype Content Model—we demonstrate that English-language survey questions about social perceptions elicit more negative stereotypes of sexual and gender minorities from both humans and LLMs. We then extend this framework to a more realistic use case: text generation. Our analysis shows that LLMs generate stereotyped representations of sexual and gender minorities in this setting, showing that they amplify representational harms in creative writing, a widely advertised use for LLMs.

Large Language Models (LLMs) are increasingly employed in high-stakes decision-making tasks, such as loan approvals. While their applications expand across domains, LLMs struggle to process tabular data, ensuring fairness and delivering reliable predictions. In this work, we assess the performance and fairness of LLMs on serialized loan approval datasets from three geographically distinct regions: Ghana, Germany, and the United States. Our evaluation focuses on the model’s zero-shot and in-context learning (ICL) capabilities. Our results reveal that the choice of serialization format significantly affects both performance and fairness in LLMs, with certain formats such as GReaT and LIFT yielding higher F1 scores but exacerbating fairness disparities. Notably, while ICL improved model performance by 4.9-59.6% relative to zero-shot baselines, its effect on fairness varied considerably across datasets. Our work underscores the importance of effective tabular data representation methods and fairness-aware models to improve the reliability of LLMs in financial decision-making.

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Transfer-Aware Data Selection for Domain Adaptation in Text Retrieval
Linzhu Yu | Huan Li | Ke Chen | Lidan Shou

Domain adaptation is widely adopted in text retrieval scenarios where large labeled data is unavailable. To improve model adaptability, existing methods try to expand more source datasets. However, we found from experiments that indiscriminately using a large amount of source data from various text tasks does not guarantee improved adaptability, but may negatively impact model performance. To tackle this issue, we propose Trait, a framework that can effectively improve model adaptability by selecting beneficial data without evaluating all source data. Specifically, we first divide multiple source datasets into data chunks of the same size as the minimum selection unit to form the whole selection space. Then we devise an iterative process that includes Bayesian optimization-based selection and transfer-aware chunk evaluation to incrementally select beneficial chunks. To reduce unnecessary evaluation costs, we also design backtracking and pruning actions to adjust the selection subspace. Extensive experimental results show that Trait not only achieves average state-of-the-art for few-shot on nine target datasets by evaluating only 4% of BERRI source data, but also is very competitive for zero-shot compared with LLM-based rankers.

Recent advancements in large language models (LLMs) have enabled their successful application to a broad range of tasks. However, in information-intensive tasks, the prompt length can grow fast, leading to increased computational requirements, performance degradation, and induced biases from irrelevant or redundant information. Recently, various prompt compression techniques have been introduced to optimize the trade-off between reducing input length and retaining performance. We propose a holistic evaluation framework that allows for in-depth analysis of prompt compression methods. We focus on three key aspects, besides compression ratio: (i) downstream task performance, (ii) grounding in the input context, and (iii) information preservation. Using our framework, we analyze state-of-the-art soft and hard compression methods and show that some fail to preserve key details from the original prompt, limiting performance on complex tasks. By identifying these limitations, we are able to improve one soft prompting method by controlling compression granularity, achieving up to +23% in downstream performance, +8 BERTScore points in grounding, and 2.7× more entities preserved in compression. Ultimately, we find that the best effectiveness/compression rate trade-off is achieved with soft prompting combined with sequence-level training.

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A Benchmark for Hindi Verb-Argument Structure Alternations
Kanishka Jain | Ashwini Vaidya

In this paper we introduce a Hindi verb alternations benchmark to investigate whether pretrained large language models (LLMs) can infer the frame-selectional properties of Hindi verbs. Our benchmark consists of minimal pairs such as ‘Tina cut the wood’/*‘Tina disappeared the wood’. We create four variants of these alternations for Hindi to test knowledge of verbal morphology and argument case-marking. Our results show that a masked monolingual model performs the best, while causal models fare poorly. We further test the quality of the predictions using a cloze-style sentence completion task. While the models appear to infer the right mapping between verbal morphology and valency in the acceptability task, they do not generate the right verbal morphology in the cloze task. The model completions also lack pragmatic and world knowledge, crucial for making generalizations about verbal alternations. Our work points towards the need for more cross-linguistic research of verbal alternations.

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Beyond Binary Preferences: Semi-Online Label-Free GRACE-KTO with Group-Wise Adaptive Calibration for High-Quality Long-Text Generation
Jingyang Deng | Ran Chen | Jo-Ku Cheng | Jinwen Ma

Generating high-quality long-text remains challenging for Large Language Models (LLMs), as conventional supervised fine-tuning fails to ensure overall quality due to its teacher-forcing nature. Kahneman-Tversky Optimization (KTO), as a model alignment method that can holistically optimize generation quality, overcomes the need for paired preference data required by previous methods. However, it still suffers from binary supervision that inadequately reflects varying quality degrees. To address this, we propose GRACE-KTO, a semi-online framework that transforms KTO’s binary signals into dynamically calibrated intra-group rewards. Specifically, GRACE-KTO aggregates responses to identical queries into groups, computes rank-sum scores across multiple linguistic quality dimensions, and applies group-wise and global normalization to adaptively redistribute sample importance. We adopt a semi-online training strategy to reduce costly online sampling while outperforming offline variants. By leveraging query generation with seed data, we minimize labeled data dependency, using the model’s own knowledge to enhance its long-text generation capabilities. Additionally, we extend the context window to 32k tokens using YaRN during inference, enabling the model to generate longer texts while maintaining perplexities. Experiments demonstrate GRACE-KTO’s superiority over vanilla KTO on both automatic metrics and LLM-as-a-Judge evaluations, advancing long-text generation through group-wise adaptive calibration.

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Representation-based Broad Hallucination Detectors Fail to Generalize Out of Distribution
Zuzanna Dubanowska | Maciej Żelaszczyk | Michał Brzozowski | Paolo Mandica | Michal P. Karpowicz

We critically assess the efficacy of the current SOTA in hallucination detection and find that its performance on the RAGTruth dataset is largely driven by a spurious correlation with data. Controlling for this effect, state-of-the-art performs no better than supervised linear probes, while requiring extensive hyperparameter tuning across datasets. Out-of-distribution generalization is currently out of reach, with all of the analyzed methods performing close to random. We propose a set of guidelines for hallucination detection and its evaluation.

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MAFMO: Multi-modal Adaptive Fusion with Meta-template Optimization for Vision-Language Models
Mingrui Xie | Lulu Xu | Junliang Du

Vision-language models like CLIP demonstrate exceptional generalization capabilities but face significant adaptation challenges due to parameter scale, prompt sensitivity, and cross-modal alignment difficulties. Existing approaches primarily focus on single-modality adjustments, leading to suboptimal alignment and limited generalization. We introduce MAFMO, a plug-and-play framework comprising: (1) a Harmonic Cross-Modal Adapter enabling efficient cross-modal knowledge transfer; (2) a Meta-Template Optimization module dynamically generating input-dependent templates; and (3) a Cross-Modal Knowledge Synthesis mechanism preserving critical structural relationships during adaptation. Extensive experiments across multiple fine-grained visual recognition benchmarks demonstrate MAFMO consistently improves existing methods’ performance on both novel classes and harmonic mean, while maintaining robustness under various challenging conditions with minimal computational overhead.

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Multimodal UNcommonsense: From Odd to Ordinary and Ordinary to Odd
Yejin Son | Saejin Kim | Dongjun Min | Youngjae Yu

Commonsense reasoning in multimodal contexts remains a foundational challenge in artificial intelligence. We introduce Multimodal UNcommonsense (MUN), a benchmark designed to evaluate models’ ability to handle scenarios that deviate from typical visual or contextual expectations. MUN pairs visual scenes with surprising or unlikely outcomes described in natural language, prompting models to either rationalize seemingly odd images using everyday logic or uncover unexpected interpretations in ordinary scenes. To support this task, we propose a retrieval-based in-context learning (R-ICL) framework that transfers reasoning capabilities from larger models to smaller ones without additional training. Leveraging a novel Multimodal Ensemble Retriever (MER), our method identifies semantically relevant exemplars even when image and text pairs are deliberately discordant. Experiments show an average improvement of 8.3% over baseline ICL methods, highlighting the effectiveness of R-ICL in low-frequency, atypical settings. MUN opens new directions for evaluating and improving visual-language models’ robustness and adaptability in real-world, culturally diverse, and non-prototypical scenarios.

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Analyzing Gambling Addictions: A Spanish Corpus for Understanding Pathological Behavior
Manuel Couto | Marcos Fernández-Pichel | Mario Ezra Aragon | David E. Losada

This work fosters research on the interaction between natural language use and gambling disorders. We have built a new Spanish corpus for screening standardized gambling symptoms. We employ search methods to find on-topic sentences, top-k pooling to form the assessment pools of sentences, and thorough annotation guidelines. The labeling task is challenging, given the need to identify topic relevance and explicit evidence about the symptoms. Additionally, we explore using state-of-the-art LLMs for annotation and compare different sentence search models.

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Beyond Surface Alignment: Rebuilding LLMs Safety Mechanism via Probabilistically Ablating Refusal Direction
Yuanbo Xie | Yingjie Zhang | Tianyun Liu | Duohe Ma | Tingwen Liu

Jailbreak attacks pose persistent threats to large language models (LLMs). Current safety alignment methods have attempted to address these issues, but they experience two significant limitations: insufficient safety alignment depth and unrobust internal defense mechanisms. These limitations make them vulnerable to adversarial attacks such as prefilling and refusal direction manipulation. We introduce DeepRefusal, a robust safety alignment framework that overcomes these issues. DeepRefusal forces the model to dynamically rebuild its refusal mechanisms from jailbreak states. This is achieved by probabilistically ablating the refusal direction across layers and token depths during fine-tuning. Our method not only defends against prefilling and refusal direction attacks but also demonstrates strong resilience against other unseen jailbreak strategies. Extensive evaluations on four open-source LLM families and six representative attacks show that DeepRefusal reduces attack success rates by approximately 95%, while maintaining model capabilities with minimal performance degradation.

Large language models are reshaping internet services. Serving these models is often costly, as it requires multiple high-end GPUs. Consumer-grade GPUs offer cheaper computational power, providing an opportunity for more cost-efficient LLM serving.Prior efforts have explored distributed serving at scale, primarily focusing on model deployment strategies. However, communication efficiency has emerged as a challenge due to the imbalance in data transfer volumes between the two phases of inference: prefill and decode. Prefill requests can involve transmitting up to 1000 times more data than decode requests, leading to decode requests being delayed. Consequently, servers are underutilized while waiting for decode requests. In this paper, we present MoLink, an efficient distributed LLM serving system. It splits the prolonged transmission volume of prefill requests into smaller chunks and carefully scheduling their transmission. It consists of two parts: (i) a transmission scheduling algorithm that fairly determines whether to transmit prefill or decode requests, and (ii) a chunking determination algorithm that determines the transmit volume for prefill requests just-in-time. Our evaluation demonstrates that MoLink reduces TTFT, TPOT, and latency compared to the state-of-the-art distributed LLM serving system, with a maximum reduction of up to 46%.

Large language models (LLMs) have exhibited great performance in autonomously calling various tools in external environments, leading to better problems solving and task automation capabilities. However, these external tools also amplify potential risks such as financial loss or privacy leaking with ambiguous or malicious user instructions. Compared to previous studies, which mainly assess the safety awareness of LLMs after obtaining the tool execution results (i.e., retrospective evaluation), this paper focuses on prospective ways to assess the safety of LLM tool utilization, aiming to avoid irreversible harm caused by directly executing tools. To this end, we propose SafeToolBench, the first benchmark to comprehensively assess tool utilization security in a prospective manner, covering malicious user instructions and diverse practical toolsets. Additionally, we propose a novel framework, SafeInstructTool, which aims to enhance LLMs’ awareness of tool utilization security through three perspectives (i.e., User Instruction, Tool Itself, and Joint Instruction-Tool), leading to nine detailed dimensions in total. We experiment with four LLMs using different methods, revealing that existing approaches fail to fully capture all risks in tool utilization. In contrast, our framework significantly enhances LLMs’ self-awareness, enabling a more safer and trustworthy tool utilization.

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Sparsifying Mamba
An Wang | Ruobing Xie | Shuaipeng Li | Xingwu Sun | Zhanhui Kang

The Transformer architecture has long dominated the development of large language models, but its quadratic complexity in sequence length presents scalability challenges. Recent advances in State Space Models, particularly Mamba series, offer a promising alternative with linear-time inference and competitive performance. While scaling model capacity via sparsification, exemplified by Mixture-of-Experts, has proven effective in reducing computation while expanding knowledge capacity, the integration of sparsification with Mamba remains largely unexplored. Existing attempts typically apply naive block-level stacking, failing to leverage Mamba’s internal structure for fine-grained sparsification. In this work, we mainly explore how to sparsify the parameters inside Mamba. We found that the effects of using sparsification strategies on parameters related to various mechanisms inside mamba are significantly different. Our proposed Mamba-MoZ framework introduces a flexible and effective sparsification mechanism inside Mamba, which can independently achieve parameter scalability and has stronger performance.

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Beneath the Facade: Probing Safety Vulnerabilities in LLMs via Auto-Generated Jailbreak Prompts
Heehyeon Kim | Kyeongryul Lee | Joyce Jiyoung Whang

The rapid proliferation of large language models and multimodal generative models has raised concerns about their potential vulnerabilities to a wide range of real-world safety risks. However, a critical gap persists in systematic assessment, alongside the lack of evaluation frameworks to keep pace with the breadth and variability of real-world risk factors. In this paper, we introduce TroGEN, an automated jailbreak prompt generation framework that assesses these vulnerabilities by deriving scenario-driven jailbreak prompts using an adversarial agent. Moving beyond labor-intensive dataset construction, TroGEN features an extensible design that covers broad range of risks, supports plug-and-play jailbreak strategies, and adapts seamlessly to multimodal settings. Experimental results demonstrate that TroGEN effectively uncovers safety weaknesses, revealing susceptibilities to adversarial attacks that conceal malicious intent beneath an apparently benign facade, like a Trojan horse. Furthermore, such stealthy attacks exhibit resilience even against existing jailbreak defense methods.

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ET-MIER: Entity Type-guided Key Mention Identification and Evidence Retrieval for Document-level Relation Extraction
Xin Li | Huangming Xu | Fu Zhang | Jingwei Cheng

Document-level relation extraction (DocRE) task aims to identify relations between entities in a document. In DocRE, an entity may appear in multiple sentences of a document in the form of mentions. In addition, relation inference requires the use of evidence sentences that can provide key clues to entity pairs. These make DocRE more challenging than sentencelevel relation extraction. Existing work does not fully distinguish the contribution of different mentions to entity representation and the importance of mentions in evidence sentences. To address these issues, we observe that entity types can provide consistent semantic constraints for entities of the same type and implicitly preclude impossible relations between entities, which may help the model better understand both intra- and inter-entity mentions. Therefore, we propose a novel model ET-MIER, which for the first time leverages **E**ntity **T**ypes to guide key **M**ention **I**dentification and **E**vidence **R**etrieval. In this way, entity types not only help learn better entity representation but also enhance evidence retrieval, both of which are crucial for DocRE. We conduct experiments on widely-adopted datasets and show that our model achieves state-of-the-art performance. Our code is available at: https://github.com/NEU-IDKE/ET-MIER

Using special tokens (e.g., gist, memory, or compressed tokens) to compress context information is a common practice for large language models (LLMs). However, existing approaches often neglect that position encodings inherently induce local inductive biases in models, causing the compression process to ignore holistic contextual dependencies. We propose **Enhanced Position Layout (EPL)**, a simple yet effective method that improves the context compression capability of LLMs by only adjusting position IDs, the numerical identifiers that specify token positions. EPL minimizes the distance between context tokens and their corresponding special tokens and at the same time maintains the sequence order in position IDs between context tokens, special tokens, and the subsequent tokens. Integrating EPL into our best performing context compression model results in 1.9 ROUGE-1 F1 improvement on out-of-domain question answering datasets in average. When extended to multimodal scenarios, EPL brings an average accuracy gain of 2.6 to vision compression LLMs.

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Can Role Vectors Affect LLM Behaviour?
Daniele Potertì | Andrea Seveso | Fabio Mercorio

The influence of personas on Large Language Models (LLMs) has been widely studied, yet their direct impact on performance remains uncertain. This work explores a novel approach to guiding LLM behaviour through role vectors, an alternative to persona-based prompting. We construct 29 role vectors derived from model activations and evaluate their impact on benchmark performance across multiple domains. Our analysis investigates whether these vectors can effectively steer models toward domain-specific expertise. We measure two key interventions: (i) activation addition, which reinforces role-specific directions, and (ii) directional ablation, which removes them. Results on well-established benchmarks indicate that role vectors do, in fact, influence model behaviour, improving in-domain task performance while also yielding unexpected cross-domain gains.This, in turn, suggests that manipulating internal model representations has a greater impact on outcomes than persona-based prompting.

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Semantic Component Analysis: Introducing Multi-Topic Distributions to Clustering-Based Topic Modeling
Florian Eichin | Carolin M. Schuster | Georg Groh | Michael A. Hedderich

Topic modeling is a key method in text analysis, but existing approaches fail to efficiently scale to large datasets or are limited by assuming one topic per document. Overcoming these limitations, we introduce Semantic Component Analysis (SCA), a topic modeling technique that discovers multiple topics per sample by introducing a decomposition step to the clustering-based topic modeling framework. We evaluate SCA on Twitter datasets in English, Hausa and Chinese. There, it achieves competetive coherence and diversity compared to BERTopic, while uncovering at least double the topics and maintaining a noise rate close to zero. We also find that SCA outperforms the LLM-based TopicGPT in scenarios with similar compute budgets. SCA thus provides an effective and efficient approach for topic modeling of large datasets.

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ThinkQE: Query Expansion via an Evolving Thinking Process
Yibin Lei | Tao Shen | Andrew Yates

Effective query expansion for web search benefits from promoting both exploration and result diversity to capture multiple interpretations and facets of a query. While recent LLM-based methods have improved retrieval performance and demonstrate strong domain generalization without additional training, they often generate narrowly focused expansions that overlook these desiderata. We propose ThinkQE, a test-time query expansion framework addressing this limitation through two key components: a thinking-based expansion process that encourages deeper and comprehensive semantic exploration, and a corpus-interaction strategy that iteratively refines expansions using retrieval feedback from the corpus. Experiments on diverse web search benchmarks (DL19, DL20, and BRIGHT) show ThinkQE consistently outperforms prior approaches, including training-intensive dense retrievers and rerankers.

Large Language Models (LLMs) exhibit significant potential in complex software engineering tasks, however, their fault localization capabilities within repository are constrained by inherent limitations in max context length. Although Test-Time Scaling (TTS) can generate multiple candidate solutions, traditional selection strategies often fail to identify the optimal one. To solve this problem, we introduces Hierarchical Localization Reward Model (HiLoRM), which specifically designed to evaluate and select the most accurate fault localization candidates (at file, function, and line levels) from the multiple sampled outputs of LLMs, thereby enhancing localization accuracy. Furthermore, we constructed the HiFL-44k dataset, comprising approximately 44,000 fault localization instances, to train HiLoRM. Experimental results demonstrate that on the SWE-Bench-Lite dataset, HiLoRM improves the final line-level localization recall by 12% compared to a baseline model that does not use a reward model. Concurrently, HiLoRM exhibits a strong capability to evaluate predictions from larger LLMs (e.g., 32B parameters) and demonstrates transferability and generalization potential when applied to other fault localization methods. This work provides an effective methodology and an accessible model to significantly improve the accuracy and reliability of LLMs for repository-level fault localization. Our codes and datasets are available at https://github.com/SZU-ZJW/HiFL-Method.

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Layer Duplication in LLMs
Neo Eyal | Nachum Dershowitz | Kfir Bar

We investigate the effect of duplicating multihead self-attention layers in large language models (LLMs) across a range of language tasks, with and without fine-tuning. The results demonstrate that duplicating the initial layers once or twice often yields a significant performance boost. Attention analysis uncovered the underlying mechanisms driving the improvement when performing layer duplication. This method enhances LLM capabilities with or without additional training or labeled data.

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Semantic-Aware Action Space Compression via LLM-DRL Synergy for Efficient Task-oriented Dialogue Policy Exploration
Yangyang Zhao | Ben Niu | Yuxuan Tan | Shihan Wang | Libo Qin

The flexibility of natural language significantly expands the action space in task-oriented dialogue systems, causing inefficient exploration and slow convergence in deep reinforcement learning (DRL)-based policy optimization. Pre-trained large language models (LLMs), with world knowledge and semantic understanding, offer promising solutions. To this end, we propose LLM-Guided DRL via Semantic-Aware Action Pruning (LLMSAP), a novel framework that synergizes pretrained LLMs with DRL. LLMSAP leverages the world knowledge and contextual understanding of LLMs to guide decision-making via an action feasibility assessment. Instead of requiring LLMs to directly generate optimal actions due to their limited precision in sequential decision tasks, LLMSAP employs a lightweight action pruning mechanism. Specifically, LLMs act as action filters, rapidly eliminating semantically implausible or low-potential actions from multi-turn dialogue context, allowing the DRL agent to focus exploration on a refined candidate subset. This two-stage framework (“prune-then-optimize”) avoids extensive LLM fine-tuning while preserving the decision-making precision of DRL. Experiments on multiple benchmarks verify the effectiveness of LLMSAP.

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Linear Steerability in Language Models: When It Emerges and How It Evolves
Jianshu She | Xinyue Li | Eric P. Xing | Zhengzhong Liu | Qirong Ho

Language models can be steered by modifying their internal representations to control concepts such as emotion, style, or truthfulness in generation. However, the conditions for an effective intervention remain unclear and are often validated through heuristics and trial-and-error. To fill this gap, we demonstrate that intervention efficacy, measured by linear steerability (i.e., the ability to adjust output via linear transformations of hidden states), emerges during intermediate stages of training. Moreover, even closely related concepts (e.g., anger and sadness) exhibit steerability emergence at distinct stages of training*.To better interpret the dynamics of steerability during training, we adapt existing intervention techniques into a unified framework, referred to as the “Intervention Detector” (ID), which is designed to reveal how linear steerability evolves over the course of training through hidden state and representation analysis. ID reveals that concepts become increasingly linearly separable in the hidden space as training progresses, which strongly correlates with the emergence of linear steerability. We further introduce ID-based metrics, such as heatmaps, entropy trends, and cosine similarity, to help interpret how linear steerability evolves throughout training. In addition, we apply ID across different model families to ensure the generality of our findings on steerability dynamics.

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A Comprehensive Survey on Learning from Rewards for Large Language Models: Reward Models and Learning Strategies
Xiaobao Wu

Recent developments in Large Language Models (LLMs) have shifted from pre-training scaling to post-training and test-time scaling. Across these developments, a key unified paradigm has arisen: Learning from Rewards, where reward signals act as the guiding stars to steer LLM behavior. It has underpinned a wide range of prevalent techniques, such as reinforcement learning (RLHF, RLAIF, DPO, and GRPO), reward-guided decoding, and post-hoc correction. Crucially, this paradigm enables the transition from passive learning from static data to active learning from dynamic feedback. This endows LLMs with aligned preferences and deep reasoning capabilities for diverse tasks. In this survey, we present a comprehensive overview of learning from rewards, from the perspective of reward models and learning strategies across training, inference, and post-inference stages. We further discuss the benchmarks for reward models and the primary applications. Finally we highlight the challenges and future directions.

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InFact: Informativeness Alignment for Improved LLM Factuality
Roi Cohen | Russa Biswas | Gerard de Melo

Factual completeness is a general term that captures how detailed and informative a factually correct text is. For instance, the factual sentence “Barack Obama was born in the United States” is factually correct, though less informative than the factual sentence “Barack Obama was born in Honolulu, Hawaii, United States”. Despite the known fact that LLMs tend to hallucinate and generate factually incorrect text, they might also tend to choose to generate factual text that is indeed factually correct and yet less informative than other, more informative choices. In this work, we tackle this problem by proposing an informativeness alignment mechanism. This mechanism takes advantage of recent factual informativeness benchmarks to propose an informativeness alignment objective. This objective prioritizes answers that are both correct and informative. We find that when training a model to maximize this objective or optimize its preference, we can improve not just informativeness but also factuality.

Large language models (LLMs) are increasingly applied to finance, yet challenges remain in aligning their capabilities with real-world institutional demands. In this survey, we provide a systematic, dual-perspective review bridging financial practice and LLM research. From a practitioner-centric standpoint, we introduce a functional taxonomy covering five core financial domains—Data Analysis, Investment Research, Trading, Investment Management, and Risk Management—mapping each to representative tasks, datasets, and institutional constraints. From a research-focused perspective, we analyze key modeling challenges, including numerical reasoning limitations, prompt sensitivity, and lack of real-time adaptability. We comprehensively catalog over 30 financial benchmarks and 20 representative models, and compare them across modalities, tasks, and deployment limitations. Finally, we identify open challenges and outline emerging directions such as continual adaptation, coordination-aware multi-agent systems, and privacy-compliant deployment. We emphasize deeper researcher–practitioner collaboration and transparent model architectures as critical pathways to safer and more scalable AI adoption in finance.

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Problem Solved? Information Extraction Design Space for Layout-Rich Documents using LLMs
Gaye Colakoglu | Gürkan Solmaz | Jonathan Fürst

This paper defines and explores the design space for information extraction (IE) from layout-rich documents using large language models (LLMs). The three core challenges of layout-aware IE with LLMs are 1) data structuring, 2) model engagement, and 3) output refinement. Our study investigates the sub-problems and methods within these core challenges, such as input representation, chunking, prompting, selection of LLMs, and multimodal models. It examines the effect of different design choices through LayIE-LLM, a new, open-source, layout-aware IE test suite, benchmarking against traditional, fine-tuned IE models. The results on two IE datasets show that LLMs require adjustment of the IE pipeline to achieve competitive performance: the optimized configuration found with LayIE-LLM achieves 13.3–37.5 F1 points more than a general-practice baseline configuration using the same LLM. To find a well-working configuration, we develop a one-factor-at-a-time (OFAT) method that achieves near-optimal results. Our method is only 0.8–1.8 points lower than the best full factorial exploration with a fraction (~2.8%) of the required computation. Overall, we demonstrate that, if well-configured, general-purpose LLMs match the performance of specialized models, providing a cost-effective, finetuning-free alternative. Our test-suite is available at https://github.com/gayecolakoglu/LayIE-LLM

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Generation-Augmented Retrieval: Rethinking the Role of Large Language Models in Zero-Shot Relation Extraction
Zehan Li | Fu Zhang | Tianyue Peng | He Liu | Jingwei Cheng

Recent advances in Relation Extraction (RE) emphasize Zero-Shot methodologies, aiming to recognize unseen relations between entities with no annotated data. Although Large Language Models (LLMs) have demonstrated outstanding performance in many NLP tasks, their performance in Zero-Shot RE (ZSRE) without entity type constraints still lags behind Small Language Models (SLMs). LLM-based ZSRE often involves manual interventions and significant computational overhead, especially when scaling to large-scale multi-choice data.To this end, we introduce RE-GAR-AD, which not only leverages the generative capability of LLMs but also utilizes their representational power without tuning LLMs. We redefine LLM-based ZSRE as a retrieval challenge, utilizing a Generation-Augmented Retrieval framework coupled with a retrieval Adjuster. Specifically, our approach guides LLMs through crafted prompts to distill sentence semantics and enrich relation labels. We encode sentences and relation labels using LLMs and match their embeddings in a triplet fashion. This retrieval technique significantly reduces token input requirements. Additionally, to further optimize embeddings, we propose a plug-in retrieval adjuster with only 2M parameters, which allows rapid fine-tuning without accessing LLMs’ parameters. Our LLM-based model demonstrates comparable performance on multiple benchmarks.

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Following Occam’s Razor: Dynamic Combination of Structured Knowledge for Multi-Hop Question Answering using LLMs
Wei Chen | Zhi Zheng | Lili Zhao | Huijun Hou | Tong Xu

Multi-hop question answering is a challenging task that requires capturing information from different positions in multiple documents. Recently, several methods propose to enhance Large Language Models (LLMs) by incorporating structured knowledge, aiming to grasp key information for solving this task. Despite certain achievements, they still face the following challenges: 1) The neglect of text-based reasoning capabilities. 2) Information redundancy between text and triples. 3) Information loss during structured knowledge extraction. To solve the above challenges, in this paper, we propose Dynamic Combination of Structured Knowledge (DCSK), a novel framework for integrating text-based and triple-based paradigms. Following Occam’s Razor, DCSK dynamically determine the necessity of structured knowledge by the designed multi-faceted evaluation, which systematically assess the correctness, clarity, and informativeness of text-based prediction. For questions that require structured knowledge, we develop an iterative fact refiner that screens for question-relevant triples, verifies their factual adequacy, and thereby effectively excludes irrelevant and redundant information. Furthermore, based on the verification, we construct an adaptive knowledge reasoner that dynamically adjusts the need for text supplementation, thus mitigating the information deficiency in selected triples. Extensive experiments on three MHQA datasets demonstrate the efficiency and effectiveness of DCSK.

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Large Language Models as Reader for Bias Detection
Xuan Luo | Jing Li | Zhong Wenzhong | Geng Tu | Ruifeng Xu

Detecting bias in media content is crucial for maintaining information integrity and promoting inclusivity. Traditional methods analyze text from the writer’s perspective, which analyzes textual features directly from the writer’s intent, leaving the reader’s perspective underexplored. This paper investigates whether Large Language Models (LLMs) can be leveraged as readers for bias detection by generating reader-perspective comments. Experiments are conducted on the BASIL (news bias) and BeyondGender (gender bias) datasets with LLMs Gemma-7B, Phi-3-3.8B, Llama3.1-8B, Llama3.1-70B, and GPT4. The results demonstrate the effectiveness of reader-perspective comments for open-source LLMs, achieving performance comparable to GPT4’s. The findings highlight the significance of emotion-related comments, which are generally more beneficial than value-related ones in bias detection. In addition, experiments on Llamas show that comment selection ensures consistent performance regardless of model sizes and comment combinations. This study is particularly beneficial for small-size open-source LLMs.

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LOHRec: Leveraging Order and Hierarchy in Generative Sequential Recommendation
Jiawen Xie | Haiyang Wu | Deyi Ji | Yuekui Yang | Shaoping Ma

The sequential recommendation task involves predicting the items users will be interested in next based on their past interaction sequence. Recently, sequential recommender systems with generative retrieval have garnered significant attention. However, during training, these generative recommenders focus only on maximizing the prediction probability of the next target item in the temporal sequence, while neglecting awareness of diverse plausible potential items.Although introducing large language models (LLMs) with world knowledge and adding a set of auxiliary tasks that can link item identifiers to their real-world meanings can alleviate this issue, the high inference costs associated with these LLM-based recommenders make them challenging to deploy in practical scenarios. In this paper, we propose a novel learning framework, LOHRec, which leverages the order and hierarchy in generative recommendation using quantized identifiers to further explore the performance ceiling of lightweight generative recommenders. Under fair comparisons with approximate backbone parameter sizes, comprehensive experiments show that all variants of generative recommenders using our framework outperform strong prior baselines across multiple datasets. Furthermore, we empirically demonstrate that LOHRec can efficiently align lightweight generative recommenders with LLM recommendation preferences in low-resource scenarios, further demonstrating its practical utility. Our code repository is available at [https://github.com/xjw-nlp/LOHRec](https://github.com/xjw-nlp/LOHRec).

Large language models (LLMs) have shown remarkable capabilities in general domains, but their application to multi-omics biology remains underexplored. To address this gap, we introduce Biology-Instructions, the first large-scale instruction-tuning dataset for multi-omics biological sequences, including DNA, RNA, proteins, and multi-molecules. This dataset bridges LLMs and complex biological sequence-related tasks, enhancing their versatility and reasoning while maintaining conversational fluency. We also highlight significant limitations of current state-of-the-art LLMs on multi-omics tasks without specialized training. To overcome this, we propose ChatMultiOmics, a strong baseline with a novel three-stage training pipeline, demonstrating superior biological understanding through Biology-Instructions. Both resources are publicly available, paving the way for better integration of LLMs in multi-omics analysis. The Biology-Instructions is publicly available at: https://github.com/hhnqqq/Biology-Instructions.

Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human data scientists do in practice. To answer this question, we introduce AssistedDS (Assisted Data Science), a benchmark designed to systematically evaluate how LLMs handle domain knowledge in tabular prediction tasks. AssistedDS features both synthetic datasets with explicitly known generative mechanisms and real-world Kaggle competitions, each accompanied by curated bundles of helpful and adversarial documents. These documents provide domain-specific insights into data cleaning, feature engineering, and model selection. We assess state-of-the-art LLMs on their ability to discern and apply beneficial versus harmful domain knowledge, evaluating submission validity, information recall, and predictive performance. Our results demonstrate three key findings: (1) LLMs frequently exhibit an uncritical adoption of provided information, significantly impairing their predictive performance when adversarial content is introduced, (2) helpful guidance is often insufficient to counteract the negative influence of adversarial information, and (3) in Kaggle datasets, LLMs often make errors in handling time-series data, applying consistent feature engineering across different folds, and interpreting categorical variables correctly. These findings highlight a substantial gap in current models’ ability to critically evaluate and leverage expert knowledge, underscoring an essential research direction for developing more robust, knowledge-aware automated data science systems. Our data and code are publicly available [here](https://github.com/jeremyxianx/Assisted-DS).

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Are you sure? Measuring models bias in content moderation through uncertainty
Alessandra Urbinati | Mirko Lai | Simona Frenda | Marco Stranisci

Automatic content moderation is crucial to ensuring safety in social media. Language Model-based classifiers are increasingly adopted for this task, but it has been shown that they perpetuate racial and social biases. Even if several resources and benchmark corpora have been developed to challenge this issue, measuring the fairness of models in content moderation remains an open issue. In this work, we present an unsupervised approach that benchmarks models on the basis of their uncertainty in classifying messages annotated by people belonging to vulnerable groups. We use uncertainty, computed by means of the conformal prediction technique, as a proxy to analyze the bias of 11 models (LMs and LLMs) against women and non-white annotators and observe to what extent it diverges from metrics based on performance, such as the F1 score. The results show that some pre-trained models predict with high accuracy the labels coming from minority groups, even if the confidence in their prediction is low. Therefore, by measuring the confidence of models, we are able to see which groups of annotators are better represented in pre-trained models and lead the debiasing process of these models before their effective use.

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FOSSIL: Harnessing Feedback on Suboptimal Samples for Data-Efficient Generalisation with Imitation Learning for Embodied Vision-and-Language Tasks
Sabrina McCallum | Amit Parekh | Alessandro Suglia

Current approaches to embodied AI tend to learn policies from expert demonstrations. However, without a mechanism to evaluate the quality of demonstrated actions, they are limited to learning from optimal behaviour or risk replicating errors and inefficiencies. While reinforcement learning offers one alternative, the associated exploration typically results in sacrificing data efficiency. This work explores how agents trained with imitation learning can learn robust representations from both optimal and suboptimal demonstrations when given access to constructive language feedback as a means to contextualise different modes of behaviour. We directly provide language feedback embeddings as part of the input sequence into a Transformer-based policy, and optionally complement the traditional next action prediction objective with auxiliary self-supervised learning objectives for feedback prediction. We test our approach on a range of embodied Vision-and-Language tasks in our custom BabyAI-XGen environment and show significant improvements in agents’ compositional generalisation abilities and robustness, suggesting that our data-efficient method allows models to successfully convert suboptimal behaviour into learning opportunities. Overall, our results suggest that language feedback is a competitive and intuitive alternative to intermediate scalar rewards for language-specified embodied tasks.

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Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities
Bhagesh Gaur | Karan Gupta | Aseem Srivastava | Manish Gupta | Md Shad Akhtar

Online Mental Health Communities (OMHCs) provide crucial peer and expert support, yet many posts remain unanswered due to missing support attributes that signal the need for help. We present a novel framework that identifies these gaps and prompts users to enrich their posts, thereby improving engagement. To support this, we introduce REDDME, a new dataset of 4,760 posts from mental health subreddits annotated for the span and intensity of three key support attributes: event what happened?, effect what did the user experience?, and requirement what support they need?. Next, we devise a hierarchical taxonomy, CueTaxo, of support attributes for controlled question generation. Further, we propose MH-COPILOT, a reinforcement learning-based system that integrates (a) contextual attribute-span identification, (b) support attribute intensity classification, (c) controlled question generation via a hierarchical taxonomy, and (d) a verifier for reward modeling. Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information. Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. A human evaluation further validates the model’s effectiveness in real-world OMHC settings.

Long-form outline generation for expository articles requires both comprehensive knowledge coverage and logical coherence, which is essential for creating detailed Wikipedia-like content. However, existing methods face critical limitations: outlines generated in the pre-writing stage often have low knowledge density and lack detail, while retrieval-augmented approaches struggle to maintain logical coherence across retrieved information. Additionally, unlike human writers who can iteratively improve through peer feedback and reference similar topics, current approaches lack effective mechanisms for systematic outline refinement. To address these challenges, we propose Logic, a Long-form Outline Generation system via Imitative and Critical self-refinement that mimics human writers’ refinement process. Logic establishes a coherent planning framework and structured knowledge base, learns from similar topic outlines through imitation, and continuously improves through model-based critique. Experiments on FreshWiki and our dataset WikiOutline show that, compared to the best baseline, Logic’s long-form outlines are more organized (with increases of 22.85% and 21.65% respectively) and more logically coherent (with increases of 16.19% and 12.24% respectively). Human evaluation further validates Logic’s effectiveness in generating comprehensive and well-structured long-form outlines.

Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilities of large language models (LLMs). However, is this effectiveness and cost-efficiency truly a free lunch? In this study, we comprehensively investigate the fairness costs associated with RAG by proposing a practical three-level threat model from the perspective of user awareness of fairness. Specifically, varying levels of user fairness awareness result in different degrees of fairness censorship on external datasets. We examine the fairness implications of RAG using uncensored, partially censored, and fully censored datasets. Our experiments demonstrate that fairness alignment can be easily undermined through RAG without the need for fine-tuning or retraining. Even with fully censored and supposedly unbiased external datasets, RAG would still lead to biased outputs. Our findings underscore the limitations of current alignment methods in the context of RAG-based LLMs and highlight the urgent need for new strategies to ensure fairness. We propose potential mitigations and call for further research to develop robust fairness safeguards in RAG-based LLMs.

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LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors
Rao Ma | Tongzhou Chen | Kartik Audhkhasi | Bhuvana Ramabhadran

Recently, large-scale pre-trained speech encoders and Large Language Models (LLMs) have been released, which show state-of-the-art performance on a range of spoken language processing tasks, including Automatic Speech Recognition (ASR). To effectively combine both models for better performance, continuous speech prompts and ASR error correction have been adopted. However, these methods are prone to suboptimal performance or are inflexible. In this paper, we propose a new paradigm, LegoSLM, that bridges speech encoders and LLMs using the ASR posterior matrices. The speech encoder is trained to generate Connectionist Temporal Classification (CTC) posteriors over the LLM vocabulary, which are used to reconstruct pseudo-audio embeddings by computing a weighted sum of the LLM input embeddings. These embeddings are concatenated with text embeddings in the LLM input space. Using the well-performing USM and Gemma models as an example, we demonstrate that our proposed LegoSLM method yields good performance on both ASR and speech translation tasks. By connecting USM with Gemma models, we can get an average of 49% WER reduction (WERR) over the USM-CTC baseline on 8 MLS testsets. The trained model also exhibits modularity in a range of settings – after fine-tuning the Gemma model weights, the speech encoder can be switched and combined with the LLM in a zero-shot fashion. Additionally, we propose to control the decode-time influence of the USM and LLM using a softmax temperature, which shows effectiveness in domain adaptation.

Code translation benchmarks are essential for evaluating the accuracy and efficiency of LLM-based systems. Existing benchmarks mainly target individual functions, overlooking repository-level challenges like intermodule coherence and dependency management. Recent repository-level efforts exist, but suffer from poor maintainability and coarse evaluation granularity. We introduce Skeleton-Guided-Translation, a framework for benchmarking Java-to-C# translation at the repository level, featuring fine-grained quality evaluation. It follows a two-step process: first translating repository “skeletons”, then refining the entire repository guided by these skeletons. Based on this, we present TRANSREPO-BENCH , the first test-driven benchmark of high-quality Java repositories paired with C# skeletons, unit tests, and build configurations. Our adaptive unit tests support multiple and incremental translations without manual tuning, enhancing automation and scalability. We also propose fine-grained metrics that evaluate translation quality per test case, overcoming limitations of binary metrics in distinguishing build failures. Evaluations using TRANSREPO-BENCH reveal issues like broken cross-file references, showing that our structured approach reduces dependency errors and preserves interface consistency.

Humans have an inherent need for community belongingness. This paper investigates this fundamental social motivation by compiling a large collection of parallel datasets comprising over 7 million posts and comments from Reddit and 200,000 posts and comments from Dread, a dark web discussion forum, covering similar topics. Grounded in five theoretical aspects of the Sense of Community framework, our analysis indicates that users on Dread exhibit a stronger sense of community membership. Our data analysis reveals striking similarities in post content across both platforms, despite the dark web’s restricted accessibility. However, these communities differ significantly in community-level closeness, including member interactions and greeting patterns that influence user retention and dynamics. We publicly release the parallel community datasets for other researchers to examine key differences and explore potential directions for further study.

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Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data
Olia Toporkov | Alan Akbik | Rodrigo Agerri

Lemmatization is the task of transforming all words in a given text to their dictionary forms. While large language models (LLMs) have demonstrated their ability to achieve competitive results across a wide range of NLP tasks, there is no prior evidence of how effective they are in the contextual lemmatization task. In this paper, we empirically investigate the capacity of the latest generation of LLMs to perform in-context lemmatization, comparing it to the traditional fully supervised approach. In particular, we consider the setting in which supervised training data is not available for a target domain or language, comparing (i) encoder-only supervised approaches, fine-tuned out-of-domain, and (ii) cross-lingual methods, against direct in-context lemma generation with LLMs. Our experimental investigation across 12 languages of different morphological complexity finds that, while encoders remain competitive in out-of-domain settings when fine-tuned on gold data, current LLMs reach state-of-the-art results for most languages by directly generating lemmas in-context without prior fine-tuning, provided just with a few examples. Data and code will be made available upon publication.

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LlmFixer: Fix the Helpfulness of Defensive Large Language Models
Zelong Yu | Xiaoming Zhang | Litian Zhang | Yu Yuan | Chaozhuo Li

Defense strategies of large language models besides alignment are introduced to defend against jailbreak attacks, and they have managed to decrease the success rate of jailbreak attacks. However, these defense strategies weakened the helpfulness of large language models. In this work, we propose a universal framework, LlmFixer, acting on large language models equipped with any defense strategy to recover their original helpfulness. LlmFixer consists of an input prompt re-writer and a logic patch. The prompt re-writer is a pre-model for clarifying the intention of input prompts, which promotes large language models to be more helpful to benign inputs and more rejective to malicious inputs. The logic patch is a lightweight structure that enhances large language models’ comprehension capacity by supplementing certain logical relationships. Without updating the parameters of a defensive large language model, LlmFixer fixes its helpfulness while preserving safety. Experiments on three large language models, five jailbreak attacks, and four defense strategies show the effectiveness of LlmFixer.

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Universal Acoustic Adversarial Attacks for Flexible Control of Speech-LLMs
Rao Ma | Mengjie Qian | Vyas Raina | Mark Gales | Kate Knill

The combination of pre-trained speech encoders with large language models has enabled the development of speech LLMs that can handle a wide range of spoken language processing tasks. While these models are powerful and flexible, this very flexibility may make them more vulnerable to adversarial attacks. To examine the extent of this problem, in this work we investigate universal acoustic adversarial attacks on speech LLMs. Here a fixed, universal, adversarial audio segment is prepended to the original input audio. We initially investigate attacks that cause the model to either produce no output or to perform a modified task overriding the original prompt. We then extend the nature of the attack to be selective so that it activates only when specific input attributes, such as a speaker gender or spoken language, are present. Inputs without the targeted attribute should be unaffected, allowing fine-grained control over the model outputs. Our findings reveal critical vulnerabilities in Qwen2-Audio and Granite-Speech and suggest that similar speech LLMs may be susceptible to universal adversarial attacks. This highlights the need for more robust training strategies and improved resistance to adversarial attacks.

In the past year, large (>100B parameter) mixture-of-expert (MoE) models have become increasingly common in the open domain. While their advantages are often framed in terms of efficiency, prior work has also explored functional differentiation through routing behavior. We investigate whether expert routing in large MoE models is influenced by the semantics of the inputs. To test this, we design two controlled experiments. First, we compare activations on sentence pairs with a shared target word used in the same or different senses. Second, we fix context and substitute the target word with semantically similar or dissimilar alternatives. Comparing expert overlap across these conditions reveals clear, statistically significant evidence of semantic routing in large MoE models.

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CMT-Eval: A Novel Chinese Multi-turn Dialogue Evaluation Dataset Addressing Real-world Conversational Challenges
Siyu Tian | Kaijie Mo | Yupei Wang | Renfen Hu

Multi-turn dialogue is a key paradigm for interaction between users and Large Language Models (LLMs). However, existing evaluation benchmarks fail to capture users’ evolving needs and how their diverse conversation styles affect the dialogue flow. To address these limitations, we propose CMT-Eval, the first dedicated dataset for fine-grained evaluation of Chinese multi-turn dialogue systems. Built upon a linguistic theory-driven Speech Act Framework, diverse user personas, and varied conversational challenges, CMT-Eval comprises 596 high-quality dialogues with 4,431 turns, simulating realistic, multifaceted, and challenging conversations. Experiments reveal that models struggle with specific speech acts, user personas, and complex scenarios, highlighting the effectiveness of CMT-Eval in assessing LLMs’ multi-turn dialogue capabilities and providing valuable insights for their enhancement. The dataset, code, and prompts are available at https://github.com/hejaida/CMT-Eval.

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LastingBench: Defend Benchmarks Against Knowledge Leakage
Yixiong Fang | Tianran Sun | Yuling Shi | Min Wang | Xiaodong Gu

The increasing size and complexity of large language models (LLMs) raise concerns about their ability to “cheat” on standard Question Answering (QA) benchmarks by memorizing task-specific data. This undermines the validity of benchmark evaluations, as they no longer reflect genuine model capabilities but instead the effects of data leakage. While existing methods detect such leakage, they fail to address the long-term challenge of mitigating it. In this paper, we introduce LastingBench, a novel approach to reinforce and safeguard existing benchmarks against knowledge leakage. Our method involves identifying leakage points through perturbation-based detection, followed by counterfactual rewriting to disrupt memorization while preserving the benchmark’s original evaluative intent. We demonstrate that our approach significantly reduces memorization effects in long-context QA benchmarks, providing a more accurate assessment of model reasoning and generalization abilities. Our experiments show that LastingBench not only uncovers substantial leakage in benchmarks like HotpotQA but also yields a more reliable evaluation of state-of-the-art models, ensuring that benchmarks remain effective and resilient over time.

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Learning API Functionality from In-Context Demonstrations for Tool-based Agents
Bhrij Patel | Ashish Jagmohan | Aditya Vempaty

Digital tool-based agents, powered by Large Language Models (LLMs), that invoke external Application Programming Interfaces (APIs) often rely on documentation to understand API functionality. However, such documentation is frequently missing, outdated, privatized, or inconsistent—hindering the development of reliable, general-purpose agents. In this work, we propose a new research direction: learning of API functionality directly from in-context demonstrations. This task is a new paradigm applicable in scenarios without documentation. Using API benchmarks, we collect demonstrations from both expert agents and from self-exploration. To understand what information demonstrations must convey for successful task completion, we extensively study how the number of demonstrations and the use of LLM-generated summaries and evaluations affect the task success rate of the API-based agent. Our experiments across 3 datasets and 6 models show that learning functionality from in-context demonstrations remains a non-trivial challenge, even for state-of-the-art LLMs. We find that providing explicit function calls and natural language critiques significantly improves the agent’s task success rate due to more accurate parameter filling. We analyze failure modes, identify sources of error, and highlight key open challenges for future work in documentation-free, self-improving, API-based agents.

Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics (e.g., to serve as risk models or augment survey datasets). However, when should a user have confidence that an LLM will provide high-quality predictions for their particular task? To address this question, we conduct a large-scale empirical study of LLMs’ zero-shot predictive capabilities across a wide range of tabular prediction tasks. We find that LLMs’ performance is highly variable, both on tasks within the same dataset and across different datasets. However, when the LLM performs well on the base prediction task, its predicted probabilities become a stronger signal for individual-level accuracy. Then, we construct metrics to predict LLMs’ performance at the task level, aiming to distinguish between tasks where LLMs may perform well and where they are likely unsuitable. We find that some of these metrics, each of which are assessed without labeled data, yield strong signals of LLMs’ predictive performance on new tasks.

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GAMIC: Graph-Aligned Molecular In-context Learning for Molecule Analysis via LLMs
Ali Al Lawati | Jason S Lucas | Zhiwei Zhang | Prasenjit Mitra | Suhang Wang

In-context learning (ICL) effectively conditions large language models (LLMs) for molecular tasks, such as property prediction and molecule captioning, by embedding carefully selected demonstration examples into the input prompt. This approach eliminates the computational overhead of extensive pre-training and fine-tuning. However, current prompt retrieval methods for molecular tasks rely on molecule feature similarity, such as Morgan fingerprints, which do not adequately capture the global molecular and atom-binding relationships. As a result, these methods fail to represent the full complexity of molecular structures during inference. Moreover, medium-sized LLMs, which offer simpler deployment requirements in specialized systems, have remained largely unexplored in the molecular ICL literature. To address these gaps, we propose a self-supervised learning technique, GAMIC (Graph-Aligned Molecular In-Context learning), which aligns global molecular structures, represented by graph neural networks (GNNs), with textual captions (descriptions) while leveraging local feature similarity through Morgan fingerprints. In addition, we introduce a Maximum Marginal Relevance (MMR) based diversity heuristic during retrieval to optimize input prompt demonstration samples. Our experimental findings using diverse benchmark datasets show GAMIC outperforms simple Morgan-based ICL retrieval methods across all tasks by up to 45%. Our code is available at: https://github.com/aliwister/mol-icl.

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Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation
Keren Artiaga | Sabyasachi Kamila | Haithem Afli | Conor Lynch | Mohammed Hasanuzzaman

Sign Language Translation has advanced with deep learning, yet evaluations remain largely signer-dependent, with overlapping signers across train/dev/test. This raises concerns about whether models truly generalise or instead rely on signer-specific regularities. We conduct signer-fold cross-validation on GFSLT-VLP, GASLT, and SignCL—three leading, publicly available, gloss-free SLT models—on CSL-Daily and PHOENIX14T. Under signer-independent evaluation, performance drops sharply: on PHOENIX14T, GFSLT-VLP falls from BLEU-4 21.44 to 3.59 and ROUGE-L 42.49 to 11.89; GASLT from 15.74 to 8.26; and SignCL from 22.74 to 3.66. We also observe that in CSL-Daily many target sentences are performed by multiple signers, so common splits can place identical sentences in both training and test, inflating absolute scores by rewarding recall of recurring sentences rather than genuine generalisation. These findings indicate that signer-dependent evaluation can substantially overestimate SLT capability. We recommend: (1) adopting signer-independent protocols to ensure generalisation to unseen signers; (2) restructuring datasets to include explicit signer-independent, sentence-disjoint splits for consistent benchmarking; and (3) reporting both signer-dependent and signer-independent results together with train–test sentence overlap to improve transparency and comparability.

Suicide remains a major global mental health challenge, and early intervention hinges on recognizing signs of suicidal ideation. In private conversations, such ideation is often expressed in subtle or conflicted ways, making detection especially difficult. Existing data sets are mainly based on public help-seeking platforms such as Reddit, which fail to capture the introspective and ambiguous nature of suicidal ideation in more private contexts. To address this gap, we introduce , a novel dataset of 1,200 test cases simulating implicit suicidal ideation within psychologically rich dialogue scenarios. Each case is grounded in psychological theory, combining the Death/Suicide Implicit Association Test (D/S-IAT) patterns, expanded suicidal expressions, cognitive distortions, and contextual stressors. In addition, we propose a psychology-guided evaluation framework to assess the ability of LLMs to identify implicit suicidal ideation through their responses. Experiments with eight widely used LLMs across varied prompting conditions reveal that current models often struggle significantly to recognize implicit suicidal ideation. Our findings highlight the urgent need for more clinically grounded evaluation frameworks and design practices to ensure the safe use of LLMs in sensitive support systems.

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Adaptive Platt Scaling with Causal Interpretations for Self-Reflective Language Model Uncertainty Estimates
Anthony Sicilia | Malihe Alikhani

As large language models (LLMs) are consumed by more users and deployed in increasingly autonomous capacities, their ability to self-monitor and ask for human intervention is of vital importance. Underlying this capability are fundamental skills like self-reflection and expression of uncertainty. In this work, we provide a formal analysis of LLM self-reflection for uncertainty estimation, using domain adaptation theory to model the shift between base predictions and reflective judgments. We use this to motivate a temperature scaling algorithm that calibrates uncertainty using comparisons between base predictions and LLM self-reflections. We evaluate our approach on challenging question-answering tasks requiring reasoning, demonstrating that our methods can improve calibration of uncertainty estimates and also offer improvements in human interpretation. More broadly, this use case shows how domain adaptation presents a promising analytical tool for understanding the underlying statistical properties of LLM self-reflections.

Vision-Language Models (VLMs) excel at tasks such as image captioning and visual question answering but frequently produce hallucinated outputs that deviate from the actual visual input or prompt. While prior work links hallucination to biases in data or representation, their causal origins remain unclear. We propose a causal framework to analyze and mitigate hallucination in VLMs. Our key hypothesis is that hallucinations arise from unintended direct influences of the vision or text modality that bypass the intended multi-modal fusion. To examine this, we construct a causal graph of the VLM and use counterfactual analysis to estimate the Natural Direct Effect (NDE) of each modality and their interaction. By systematically identifying and suppressing these direct effects, we encourage outputs that are more faithfully grounded in true cross-modal reasoning. Our approach consists of three steps: (1) designing structural causal graphs to distinguish correct fusion pathways from spurious modality shortcuts, (2) estimating modality-specific and cross-modal NDE using perturbed image representations, hallucinated text embeddings, and degraded visual inputs, and (3) implementing a test-time intervention module to dynamically adjust the model’s dependence on each modality. Experimental results demonstrate that our method significantly reduces hallucination while preserving task performance, providing a robust and interpretable framework for improving VLM reliability.

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Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning
Daeun Lee | Jaehong Yoon | Jaemin Cho | Mohit Bansal

Recent advances in chain-of-thought (CoT) reasoning have improved complex video understanding, but existing methods often struggle to adapt to domain-specific skills (e.g., temporal grounding, event detection, spatial relations) over various video content. To address this, we propose Video-Skill-CoT (aka Video-SKoT) a framework that automatically constructs and leverages skill-aware CoT supervisions for domain-adaptive video reasoning. First, we construct skill-based CoT annotations: We extract domain-relevant reasoning skills from training questions, cluster them into a shared skill taxonomy, and create detailed multi-step CoT rationale tailored to each video question pair for training. Second, we introduce a skill-specific expert learning framework. Each expert module specializes in a subset of reasoning skills and is trained with lightweight adapters using the collected CoT supervision. We demonstrate the effectiveness of the proposed approach on three video understanding benchmarks, where Video-SKoT consistently outperforms strong baselines. We also provide in-depth analyses on comparing different CoT annotation pipelines and learned skills over multiple video domains.

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Glitter: A Multi-Sentence, Multi-Reference Benchmark for Gender-Fair German Machine Translation
A Pranav | Janiça Hackenbuchner | Giuseppe Attanasio | Manuel Lardelli | Anne Lauscher

Machine translation (MT) research addressing gender inclusivity has gained attention for promoting non-exclusionary language representing all genders. However, existing resources are limited in size, most often consisting of single sentences, or single gender-fair formulation types, leaving questions about MT models’ ability to use context and diverse inclusive forms. We introduce Glitter, an English-German benchmark featuring extended passages with professional translations implementing three gender-fair alternatives: neutral rewording, typographical solutions (gender star), and neologistic forms (-ens forms). Our experiments reveal significant limitations in state-of-the-art language models, which default to masculine generics, struggle to interpret explicit gender cues in context, and rarely produce gender-fair translations. Through a systematic prompting analysis designed to elicit fair language, we demonstrate that these limitations stem from models’ fundamental misunderstanding of gender phenomena, as they fail to implement inclusive forms even when explicitly instructed. Glitter establishes a challenging benchmark, advancing research in gender-fair English-German MT. It highlights substantial room for improvement among leading models and can guide the development of future MT models capable of accurately representing gender diversity.

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From n-gram to Attention: How Model Architectures Learn and Propagate Bias in Language Modeling
Mohsinul Kabir | Tasfia Tahsin | Sophia Ananiadou

Current research on bias in language models (LMs) predominantly focuses on data quality, with significantly less attention paid to model architecture and temporal influences of data. Even more critically, few studies systematically investigate the origins of bias. We propose a methodology grounded in comparative behavioral theory to interpret the complex interaction between training data and model architecture in bias propagation during language modeling. Building on recent work that relates transformers to n-gram LMs, we evaluate how data, model design choices, and temporal dynamics affect bias propagation. Our findings reveal that: (1) n-gram LMs are highly sensitive to context window size in bias propagation, while transformers demonstrate architectural robustness; (2) the temporal provenance of training data significantly affects bias; and (3) different model architectures respond differentially to controlled bias injection, with certain biases (e.g. sexual orientation) being disproportionately amplified. As language models become ubiquitous, our findings highlight the need for a holistic approach- tracing bias to its origins across both data and model dimensions, not just symptoms, to mitigate harm.

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SENTRA: Selected-Next-Token Transformer for LLM Text Detection
Mitchell Plyler | Yilun Zhang | Alexander Tuzhilin | Saoud Khalifah | Sen Tian

LLMs are becoming increasingly capable and widespread. Consequently, the potential and reality of their misuse is also growing. In this work, we address the problem of detecting LLM-generated text that is not explicitly declared as such. We present a novel, general-purpose, and supervised LLM text detector, SElected-Next-Token tRAnsformer (SENTRA). SENTRA is a Transformer-based encoder leveraging selected-next-token-probability sequences and utilizing contrastive pre-training on large amounts of unlabeled data. Our experiments on three popular public datasets across 24 domains of text demonstrate SENTRA is a general-purpose classifier that significantly outperforms popular baselines in the out-of-domain setting.

We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data, implementing multimodal agent-based evaluation that filters factors based on market status, predictive quality while maintaining category balance, and deploying dynamic weight optimization that adapts to market conditions. Experimental results demonstrate the robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. Our work extends LLMs capabilities to quantitative trading, providing a scalable architecture for financial signal extraction and portfolio construction. The overall framework significantly outperforms all benchmarks with 53.17% cumulative return on SSE50 (Jan 2023 to Jan 2024), demonstrating superior risk-adjusted performance and downside protection on the market.

Recent advances in large language models (LLMs) have enabled automatic generation of chain-of-thought (CoT) reasoning, leading to strong performance on tasks such as math and code. However, when reasoning steps reflect social stereotypes (e.g., those related to gender, race or age), they can reinforce harmful associations and lead to misleading conclusions. We present the first systematic evaluation of social bias within LLM-generated reasoning, using the BBQ dataset to analyze both prediction accuracy and bias. Our study spans a wide range of mainstream reasoning models, including instruction-tuned and CoT-augmented variants of DeepSeek-R1 (8B/32B), ChatGPT, and other open-source LLMs. We quantify how biased reasoning steps correlate with incorrect predictions and often lead to stereotype expression. To mitigate reasoning-induced bias, we propose Answer Distribution as Bias Proxy (ADBP), a lightweight mitigation method that detects bias by tracking how model predictions change across incremental reasoning steps. ADBP outperforms a stereotype-free baseline in most cases, mitigating bias and improving the accuracy of LLM outputs.

Process reward models (PRMs) have shown success in complex reasoning tasks for large language models (LLMs). However, their application to machine translation (MT) remains underexplored due to the lack of systematic methodologies and evaluation benchmarks. To address this gap, we introduce MT-RewardTree, a comprehensive framework for constructing, evaluating, and deploying process reward models in MT. Unlike traditional vanilla preference pair construction, we propose a novel method for automatically generating token-level preference pairs using approximate Monte Carlo Tree Search (MCTS), which mitigates the prohibitive cost of human annotation for fine-grained steps. Then, we establish the first MT-specific reward model benchmark and provide a systematic comparison of different reward modeling architectures, revealing that token-level supervision effectively captures fine-grained preferences. Experimental results demonstrate that our MT-PRM-Qwen-2.5-3B achieves state-of-the-art performance in both token-level and sequence-level evaluation given the same input prefix. Furthermore, we showcase practical applications where MT-PRMs successfully identify token-level translation differences and enable test-time alignment for LLMs without additional alignment training. Our work provides valuable insights into the role of reward models in MT research. Our code and data are released in https://sabijun.github.io/MT_RewardTreePage.

A dangerous assumption that can be made from prior work on the bias transfer hypothesis (BTH) is that biases do not transfer from pre-trained large language models (LLMs) to adapted models. We invalidate this assumption by studying the BTH in causal models under prompt adaptations, as prompting is an extremely popular and accessible adaptation strategy used in real-world applications. In contrast to prior work, we find that biases can transfer through prompting and that popular prompt-based mitigation methods do not consistently prevent biases from transferring. Specifically, the correlation between intrinsic biases and those after prompt adaptation remained moderate to strong across demographics and tasks: gender (rho >= 0.94) in co-reference resolution, and for age (rho >= 0.98), religion (rho >= 0.69), etc., in question answering. Further, we find that biases remain strongly correlated when varying few-shot composition parameters, such as sample size, stereotypical content, occupational distribution and representational balance (rho >= 0.90). We evaluate several prompt-based debiasing strategies and find that different approaches have distinct strengths, but none consistently reduce bias transfer across models, tasks or demographics. These results demonstrate that correcting bias, and potentially improving reasoning ability, in intrinsic models may be reliable ways to prevent propagation of biases to downstream tasks.

Large Language Models (LLMs) typically rely on a large number of parameters for token embedding, leading to substantial storage requirements and memory footprints. In particular, LLMs deployed on edge devices are memory-bound, and reducing the memory footprint by compressing the embedding layer not only frees up the memory bandwidth but also speeds up inference. To address this, we introduce CARVQ, a post-training novel Corrective Adaptor combined with group Residual Vector Quantization. CARVQ relies on the composition of both linear and non-linear maps and mimics the original model embedding to compress to approximately 1.6 bits without requiring specialized hardware to support lower-bit storage. We test our method on pre-trained LLMs such as LLaMA-3.2-1B, LLaMA-3.2-3B, LLaMA-3.2-3B-Instruct, LLaMA-3.1-8B, Qwen2.5-7B, Qwen2.5-Math-7B and Phi-4, evaluating on common generative, discriminative, math and reasoning tasks. We show that in most cases, CARVQ can achieve lower average bitwidth-per-parameter while maintaining reasonable perplexity and accuracy compared to scalar quantization. Our contributions include a novel compression technique that is compatible with state-of-the-art transformer quantization methods and can be seamlessly integrated into any hardware supporting 4-bit memory to reduce the model’s memory footprint in memory-constrained devices. This work demonstrates a crucial step toward the efficient deployment of LLMs on edge devices.

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Consistent Discourse-level Temporal Relation Extraction Using Large Language Models
Yi Fan | Michael Strube

Understanding temporal relations between events in a text is essential for determining its temporal structure. Recent advancements in large language models (LLMs) have spurred research on temporal relation extraction. However, LLMs perform poorly in zero-shot and few-shot settings, often underperforming smaller fine-tuned models. Despite these limitations, little attention has been given to improving LLMs in temporal structure extraction tasks. This study systematically examines LLMs’ ability to extract and infer discourse-level temporal relations, identifying factors influencing their reasoning and extraction capabilities, including input context, reasoning process and ensuring consistency. We propose a three-step framework to improve LLMs’ temporal relation extraction capabilities: context selection, prompts inspired by Allen’s interval algebra (Allen, 1983), and reflection-based consistency learning (Shinn et al., 2024). Our results show the effectiveness of our method in guiding LLMs towards structured processing of temporal structure in discourse.

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MMPlanner: Zero-Shot Multimodal Procedural Planning with Chain-of-Thought Object State Reasoning
Afrina Tabassum | Bin Guo | Xiyao Ma | Hoda Eldardiry | Ismini Lourentzou

Multimodal Procedural Planning (MPP) aims to generate step-by-step instructions that combine text and images, with the central challenge of preserving object-state consistency across modalities while producing informative plans. Existing approaches often leverage large language models (LLMs) to refine textual steps; however, visual object-state alignment and systematic evaluation are largely underexplored.We present MMPlanner, a zero-shot MPP framework that introduces Object State Reasoning Chain-of-Thought (OSR-CoT) prompting to explicitly model object-state transitions and generate accurate multimodal plans. To assess plan quality, we design LLM-as-a-judge protocols for planning accuracy and cross-modal alignment, and further propose a visual step-reordering task to measure temporal coherence.Experiments on RecipePlan and WikiPlan show that MMPlanner achieves state-of-the-art performance, improving textual planning by +6.8%, cross-modal alignment by +11.9%, and visual step ordering by +26.7%.

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Internal states before wait modulate reasoning patterns
Dmitrii Troitskii | Koyena Pal | Chris Wendler | Callum Stuart McDougall

Prior work has shown that a significant driver of performance in reasoning models is their ability to reason and self-correct. A distinctive marker in these reasoning traces is the token wait, which often signals reasoning behavior such as backtracking. Despite being such a complex behavior, little is understood of exactly why models do or do not decide to reason in this particular manner, which limits our understanding of what makes a reasoning model so effective. In this work, we address the question whether model’s latents preceding wait tokens contain relevant information for modulating the subsequent reasoning process. We train crosscoders at multiple layers of DeepSeek-R1-Distill-Llama-8B and its base version, and introduce a latent attribution technique in the crosscoder setting. We locate a small set of features relevant for promoting/suppressing wait tokens’ probabilities. Finally, through a targeted series of experiments analyzing max-activating examples and causal interventions, we show that many of our identified features indeed are relevant for the reasoning process and give rise to different types of reasoning patterns such as restarting from the beginning, recalling prior knowledge, expressing uncertainty, and double-checking.

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Sparsity May Be All You Need: Sparse Random Parameter Adaptation
Jesus Rios | Pierre Dognin | Ronny Luss | Karthikeyan Natesan Ramamurthy

Full fine-tuning of large language models for alignment and task adaptation has become prohibitively expensive as models have grown in size. Parameter-Efficient Fine-Tuning (PEFT) methods aim at significantly reducing the computational and memory resources needed for fine-tuning these models by only training on a small number of parameters instead of all model parameters. Currently, the most popular PEFT method is the Low-Rank Adaptation (LoRA), which freezes the parameters of the model and introduces a small set of trainable parameters in the form of low-rank matrices. We propose simply reducing the number of trainable parameters by randomly selecting a small proportion of the model parameters to train on, while fixing all other parameters, without any additional prior assumptions such as low-rank structures. In this paper, we compare the efficiency and performance of our proposed approach to other PEFT methods as well as full parameter fine-tuning. We find our method to be competitive with LoRA when using a similar number of trainable parameters. Our findings suggest that what truly matters for a PEFT technique to perform well is not necessarily the specific adapter structure, but rather the number of trainable parameters being used.

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Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition
Panagiotis Kaliosis | John Pavlopoulos

Handwritten text recognition aims to convert visual input into machine-readable text, and it remains challenging due to the evolving and context-dependent nature of handwriting. Character sets change over time, and character frequency distributions shift across historical periods or regions, often causing models trained on broad, heterogeneous corpora to underperform on specific subsets. To tackle this, we propose a novel loss function that incorporates the Wasserstein distance between the character frequency distribution of the predicted text and a target distribution empirically derived from training data. By penalizing divergence from expected distributions, our approach enhances both accuracy and robustness under temporal and contextual intra-dataset shifts. Furthermore, we demonstrate that character distribution alignment can also improve existing models at inference time without requiring retraining by integrating it as a scoring function in a guided decoding scheme. Experimental results across multiple datasets and architectures confirm the effectiveness of our method in boosting generalization and performance. We open source our code at https://github.com/pkaliosis/fada.

Large-scale reinforcement learning (RL) methods have proven highly effective in enhancing the reasoning abilities of large language models (LLMs), particularly for tasks with verifiable solutions such as mathematics and coding. However, applying this idea to machine translation (MT), where outputs are flexibly formatted and difficult to automatically evaluate with explicit rules, remains underexplored. In this work, we introduce MT-R1-Zero, the first open-source adaptation of the R1-Zero RL framework for MT without supervised fine-tuning or cold-start. We propose a rule-metric mixed reward mechanism to guide LLMs towards improved translation quality via emergent reasoning. On the WMT 24 English-Chinese benchmark, our MT-R1-Zero-3B-Mix achieves competitive performance, surpassing TowerInstruct-7B-v0.2 by an average of 1.26 points. Meanwhile, our MT-R1-Zero-7B-Mix attains a high average score of 62.25 across all metrics, placing it on par with advanced proprietary models such as GPT-4o and Claude-3.5-Sonnet, while the MT-R1-Zero-7B-Sem variant achieves state-of-the-art scores on semantic metrics. Moreover, our work exhibits strong generalization capabilities on out-of-distribution MT tasks, robustly supporting multilingual and low-resource settings. Extensive analysis of model behavior across different initializations and reward metrics offers pioneering insight into the critical role of reward design, LLM adaptability, training dynamics, and emergent reasoning patterns within the R1-Zero paradigm for MT. Our code is available at https://github.com/fzp0424/MT-R1-Zero.

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Discrete Minds in a Continuous World: Do Language Models Know Time Passes?
Minghan Wang | Ye Bai | Thuy-Trang Vu | Ehsan Shareghi | Gholamreza Haffari

While Large Language Models (LLMs) excel at temporal reasoning tasks like event ordering and duration estimation, their ability to perceive the actual passage of time remains unexplored. We investigate whether LLMs perceive the passage of time and adapt their decision-making accordingly through three complementary experiments. First, we introduce the Token-Time Hypothesis, positing that LLMs can map discrete token counts to continuous wall-clock time, and validate this through a dialogue duration judgment task. Second, we demonstrate that LLMs could use this awareness to adapt their response length while maintaining accuracy when users express urgency in question answering tasks. Finally, we develop BombRush, an interactive navigation challenge that examines how LLMs modify behavior under progressive time pressure in dynamic environments. Our findings indicate that LLMs possess certain awareness of time passage, enabling them to bridge discrete linguistic tokens and continuous physical time, though this capability varies with model size and reasoning abilities. This work establishes a theoretical foundation for enhancing temporal awareness in LLMs for time-sensitive applications.

Temporal knowledge graph (TKG) reasoning, a central task in temporal knowledge representation, focuses on predicting future facts by leveraging historical temporal contexts. However, current approaches face two major challenges: limited generalization to unseen facts and insufficient interpretability of reasoning processes. To address these challenges, this paper proposes the **D**enoising **L**ogic-based **T**emporal **K**nowledge **G**raph (DLTKG) framework, which employs a denoising diffusion process to complete reasoning tasks by introducing a noise source and a historical conditionguiding mechanism. Specifically, DLTKG constructs fuzzy entity representations by treating historical facts as noise sources, thereby enhancing the semantic associations between entities and the generalization ability for unseen facts. Additionally, the condition-based guidance mechanism, rooted in the relationship evolutionary paths, is designed to improve the interpretability of the reasoning process. Furthermore, we introduce a fine-tuning strategy that optimizes the denoising process by leveraging shortest path information between the head entity and candidate entities. Experimental results on three benchmark datasets demonstrate that DLTKG outperforms state-of-the-art methods across multiple evaluation metrics. Our code is available at: https://github.com/NEU-IDKE/DLTKG

Although large audio-language models (LALMs) have demonstrated remarkable capabilities in audio perception, their performance in affective computing scenarios, particularly in emotion recognition, reasoning, and subtle sentiment differentiation, remains suboptimal. Recent advances in reinforcement learning (RL) have shown promise in improving LALMs’ reasoning abilities. However, two critical challenges hinder the direct application of RL techniques to speech emotion recognition (SER) tasks: (1) convergence instability caused by ambiguous emotional boundaries and (2) limited reasoning ability when using relatively small models (e.g., 7B-parameter architectures). To address these challenges, we propose EMO-RL, a novel framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR). Built upon pretrained LALMs, our method employs group-relative policy optimization with emotion constraints. Comprehensive experiments demonstrate that our EMO-RL training strategies can significantly enhance the emotional reasoning capabilities of LALMs, achieving state-of-the-art performance on the MELD and IEMOCAP datasets, and cross-dataset experiments prove the strong superiority of generalization.

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MANTA: A Scalable Pipeline for Transmuting Massive Web Corpora into Instruction Datasets
Heuiyeen Yeen | Seokhee Hong | Hyeongu Yun | Jinsik Lee

We introduce MANTA, an automated pipeline that generates high-quality large-scale instruction fine-tuning datasets from massive web corpora while preserving their diversity and scalability. By extracting structured syllabi from web documents and leveraging high-performance LLMs, our approach enables highly effective query-response generation with minimal human intervention. Extensive experiments on 8B-scale LLMs demonstrate that fine-tuning on the MANTA-1M dataset significantly outperforms other massive dataset generation methodologies, particularly in knowledge-intensive tasks such as MMLU and MMLU-Pro, while also delivering superior performance across a broad spectrum of tasks. Moreover, MANTA supports seamless scalability by allowing the continuous integration of web corpus data, enabling expansion into domains requiring intensive knowledge.

Large Language Models (LLMs) have achieved impressive performance across a range of natural language processing tasks. However, recent advances demonstrate that further gains—particularly in complex reasoning tasks—require more than merely scaling up model sizes or training data. One promising direction is to enable models to “think” during the reasoning process. Recently, Quiet-STaR significantly improves reasoning by generating token-level thought traces, but incurs substantial inference overhead. In this work, we propose Fast Quiet-STaR, a more efficient reasoning framework that preserves the benefits of token-level reasoning while reducing computational cost. Our method introduces a curriculum-learning-based training strategy that gradually reduces the number of thought tokens, enabling the model to internalize more abstract and concise reasoning processes. We further extend this approach to the standard Next Token Prediction (NTP) setting through reinforcement learning-based fine-tuning, resulting in Fast Quiet-STaR NTP, which eliminates the need for explicit thought token generation during inference. Experiments on four benchmark datasets with Mistral 7B and Qwen2.5 7B demonstrate that Fast Quiet-STaR consistently outperforms Quiet-STaR in terms of average accuracy under the same inference time budget. Notably, Fast Quiet-STaR NTP achieves an average accuracy improvement of 9% on Mistral 7B and 5.7% on Qwen2.5 7B, while maintaining the same inference latency.

The unlearning method aims at effectively removing harmful, sensitive, or outdated knowledge without costly retraining the model. However, existing methods suffer from two critical limitations: (1) collateral forgetting, where erasing target data inadvertently removes related but desirable knowledge, and (2) generality forgetting, where aggressive unlearning degrades the model’s general capabilities. To address these challenges, we propose DirectiOn Guide unlEarning (DOGE), a novel method that enables precise knowledge erasure by identifying and leveraging a targeted “unlearning direction” in the model’s parameter space. DOGE first extracts this direction through differential analysis of representations for forgotten and retained samples, pinpointing the exact subspace associated with unwanted knowledge. It then selectively applies updates along this direction, ensuring minimal interference with retained information and general model performance. Experiments across multiple benchmarks demonstrate that Doge achieves state-of-the-art unlearning precision while preserving both related knowledge and general capabilities.

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UniRAG: A Unified RAG Framework for Knowledge-Intensive Queries with Decomposition, Break-Down Reasoning, and Iterative Rewriting
Gun Il Kim | Jong Wook Kim | Beakcheol Jang

Knowledge-intensive queries require accurate answers that are explicitly grounded in retrieved evidence. However, existing retrieval-augmented generation (RAG) approaches often struggle with query complexity, suffer from propagated reasoning errors, or rely on incomplete or noisy retrieval, limiting their effectiveness. To address these limitations, we introduce UniRAG, a unified RAG framework that integrates entity-grounded query decomposition, break-down reasoning, and iterative query rewriting. Specifically, UniRAG decomposes queries into semantically coherent sub-queries, explicitly verifies retrieved sub-facts through a dedicated reasoning module, and adaptively refines queries based on identified knowledge gaps, significantly improving answer completeness and reliability. Extensive benchmark evaluations on complex question-answering datasets, including multi-hop HotPotQA and 2WikiMultihopQA, biomedical MedMCQA and MedQA, and fact-verification FEVER and SciFact, demonstrate that UniRAG consistently achieves performance improvements across various state-of-the-art LLMs, such as LLaMA-3.1-8B, GPT-3.5-Turbo, and Gemini-1.5-Flash.

Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have shown improved performance in generating accurate responses. However, the dependence on external knowledge bases introduces potential security vulnerabilities, particularly when these knowledge bases are publicly accessible and modifiable. While previous studies have exposed knowledge poisoning risks in RAG systems, existing attack methods suffer from critical limitations: they either require injecting multiple poisoned documents (resulting in poor stealthiness) or can only function effectively on simplistic queries (limiting real-world applicability). This paper reveals a more realistic knowledge poisoning attack against RAG systems that achieves successful attacks by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. Our proposed AuthChain address three challenges to ensure the poisoned documents are reliably retrieved and trusted by the LLM, even against large knowledge bases and LLM’s own knowledge. Extensive experiments across six popular LLMs demonstrate that AuthChain achieves significantly higher attack success rates while maintaining superior stealthiness against RAG defense mechanisms compared to state-of-the-art baselines.

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From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment
Jing Ye | Lu Xiang | Yaping Zhang | Chengqing Zong

Effective emotional support hinges on understanding users’ emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great potential for expressing empathy; however, they often deliver generic responses that fail to address users’ specific needs. To tackle this issue, we propose a self-evolution framework designed to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context. Our framework consists of two distinct phases: (1) Emotional Support Experience Acquisition, where LLMs are fine-tuned on limited emotional support conversation data to provide basic support, and (2) Self-Improvement for Personalized Emotional Support, where LLMs leverage self-reflection and self-refinement to generate personalized responses. Through iterative direct preference optimization between the pre- and post-refined responses, our model generates responses that reflect a better understanding of the user’s implicit preferences. Extensive experiments and evaluations demonstrate that our method significantly enhances the model’s performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs.

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MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding
Jingyuan Deng | Yujiu Yang

Large vision-language models (LVLMs) have shown remarkable performance in visual-language understanding for downstream multimodal tasks. While their capabilities are improving, problems emerge simultaneously. Among those problems, the hallucinations have attracted much attention, which stands for the phenomenon where LVLMs generate contradictory content to their input visual and text contents. Many approaches have been proposed to deal with this issue, such as contrastive decoding and attention manipulation. However, contrastive decoding methods struggle in constructing appropriate contrastive samples, and attention manipulation methods are highly sensitive, lacking stability. In this work, we propose image head Masked Contrastive Decoding (MaskCD). Our approach utilizes the “image heads” in LVLMs, masking them to construct contrastive samples for contrastive decoding. We evaluated MaskCD on LLaVA-1.5-7b and Qwen-VL-7b, using various benchmarks such as CHAIR, POPE, AMBER and MME. The results demonstrate that MaskCD effectively alleviates the phenomenon of hallucinations and retains the general capabilities of LVLMs. Corresponding resources could be found at: https://github.com/Deng-Jingyuan/MaskCD.

Gradient-based data influence approximation has been leveraged to select useful data samples in the supervised fine-tuning of large language models. However, the computation of gradients throughout the fine-tuning process requires too many resources to be feasible in practice. In this paper, we propose an efficient gradient-based data selection framework with clustering and a modified Upper Confidence Bound (UCB) algorithm. Based on the intuition that data samples with similar gradient features will have similar influences, we first perform clustering on the training data pool. Then, we frame the inter-cluster data selection as a constrained computing budget allocation problem and consider it a multi-armed bandit problem. A modified UCB algorithm is leveraged to solve this problem. Specifically, during the iterative sampling process, historical data influence information is recorded to directly estimate the distributions of each cluster, and a cold start is adopted to balance exploration and exploitation. Experimental results on various benchmarks show that our proposed framework, ClusterUCB, can achieve comparable results to the original gradient-based data selection methods while greatly reducing computing consumption.

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TrapDoc: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents
Hyundong Jin | Sicheol Sung | Shinwoo Park | SeungYeop Baik | Yo-Sub Han

The reasoning, writing, text-editing, and retrieval capabilities of proprietary large language models (LLMs) have advanced rapidly, providing users with an ever-expanding set of functionalities. However, this growing utility has also led to a serious societal concern: the over-reliance on LLMs. In particular, users increasingly delegate tasks such as homework, assignments, or the processing of sensitive documents to LLMs without meaningful engagement. This form of over-reliance and misuse is emerging as a significant social issue. In order to mitigate these issues, we propose a method injecting imperceptible phantom tokens into documents, which causes LLMs to generate outputs that appear plausible to users but are in fact incorrect. Based on this technique, we introduce TrapDoc, a framework designed to deceive over-reliant LLM users. Through empirical evaluation, we demonstrate the effectiveness of our framework on proprietary LLMs, comparing its impact against several baselines. TrapDoc serves as a strong foundation for promoting more responsible and thoughtful engagement with language models.

Large language models (LLMs) have shown remarkable progress in reasoning abilities and general natural language processing (NLP) tasks, yet their performance on Arabic data, characterized by rich morphology, diverse dialects, and complex script, remains underexplored. This paper presents a comprehensive benchmarking study of multiple reasoning-focused LLMs, with a special emphasis on the newly introduced DeepSeek models, across a suite of fifteen Arabic NLP tasks. We experiment with various strategies, including zero-shot, few-shot, and fine-tuning. This allows us to systematically evaluate performance on datasets covering a range of applications to examine their capacity for linguistic reasoning under different levels of complexity. Our experiments reveal several key findings. First, carefully selecting just three in-context examples delivers an average uplift of over 13 F1 points on classification tasks—boosting sentiment analysis from 35.3% to 87.5% and paraphrase detection from 56.1% to 87.0%. Second, reasoning-focused DeepSeek architectures outperform a strong GPT o4-mini baseline by an average of 12 F1 points on complex inference tasks in the zero-shot setting. Third, LoRA-based fine-tuning yields up to an additional 8 points in F1 and BLEU compared to equivalent increases in model scale. The code is available at https://anonymous.4open.science/r/AraReasoner41299

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Tales of Morality: Comparing Human- and LLM-Generated Moral Stories from Visual Cues
Rezvaneh Rezapour | Sullam Jeoung | Zhiwen You | Jana Diesner

Do moral values align between images, the stories humans write about them, and the narratives generated by large language models (LLMs)? This question matters because stories are central to how humans communicate moral values, yet little is known about how people and LLMs perform this task in a multimodal (text and image) setting. We present a systematic comparison of moral values represented in human- and LLM-generated narratives based on images annotated by humans for moral content. Our analysis shows that while human stories reflect a balanced distribution of moral foundations and coherent narrative arcs, LLMs disproportionately emphasize the Care foundation and often lack emotional resolution. Even with moral conditioning, these biases persist in LLMs. We introduce a novel dataset and framework for evaluating moral storytelling in vision-language models, highlighting key challenges in aligning AI with human moral reasoning across cultures.

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AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation
Wenfeng Feng | Chuzhan Hao | Yuewei Zhang | Guochao Jiang | Jingyi Song

Leveraging the autonomous decision-making capabilities of large language models (LLMs) has demonstrated superior performance in reasoning tasks. However, despite the success of iterative or agentic retrieval-augmented generation (RAG) techniques, these methods are often constrained to a single solution space when confronted with complex problems. In this paper, we propose a novel thinking pattern in RAG that integrates autonomous strategic planning with efficient reasoning actions, significantly activating intrinsic reasoning capabilities and expanding the solution space of specific tasks via Monte Carlo Tree Search (MCTS), which we refer to as AirRAG. Specifically, our approach designs five fundamental reasoning actions, which are expanded to a broad tree-based reasoning space using MCTS. The approach also incorporates self-consistency verification to explore potential reasoning paths and inference scaling law. Additionally, computationally optimal strategies are employed to allocate more inference resources to key actions, thereby enhancing overall performance. Experimental results demonstrate the effectiveness of AirRAG, showing significant performance gains on complex question-answering datasets. Furthermore, AirRAG is flexible and lightweight, making it easy to integrate with other advanced technologies and models.

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Evaluating NL2SQL via SQL2NL
Mohammadtaher Safarzadeh | Afshin Oroojlooy | Dan Roth

Robust evaluation in the presence of linguistic variation is key to understanding the generalization capabilities of Natural Language to SQL (NL2SQL) models, yet existing benchmarks rarely address this factor in a systematic or controlled manner. We propose a novel schema-aligned paraphrasing framework that leverages SQL-to-NL (SQL2NL) to automatically generate semantically equivalent, lexically diverse queries while maintaining alignment with the original schema and intent. This enables the first targeted evaluation of NL2SQL robustness to linguistic variation in isolation-distinct from prior work that primarily investigates ambiguity or schema perturbations. Ouranalysis reveals that state-of-the-art models are far more brittle than standard benchmarks suggest. For example, LLaMa3.3-70B exhibits a 10.23% drop in execution accuracy (from 77.11% to 66.9%) on paraphrased Spider queries, while LLaMa3.1-8B suffers an even larger drop of nearly 20% (from 62.9% to 42.5%). Smaller models (e.g., GPT-4o mini) are disproportionately affected. We also find that robustness degradation varies significantly with query complexity, dataset, and domain- highlighting the need for evaluation frameworks that explicitly measure linguistic generalization to ensure reliable performance in real-world settings.

Recent text-to-SQL systems powered by large language models (LLMs) have demonstrated remarkable performance in translating natural language queries into SQL.However, these systems often struggle with complex database structures and domain-specific queries, as they primarily focus on enhancing logical reasoning and SQL syntax while overlooking the critical need for comprehensive database understanding.To address this limitation, we propose DB-Explore, a novel framework that systematically aligns LLMs with database knowledge through automated exploration and instruction synthesis.DB-Explore constructs database graphs to capture complex relational schemas, leverages GPT-4 to systematically mine structural patterns and semantic knowledge, and synthesizes instructions to distill this knowledge for efficient fine-tuning of LLMs.Our framework enables comprehensive database understanding through diverse sampling strategies and automated instruction generation, bridging the gap between database structures and language models.Experiments conducted on the SPIDER and BIRD benchmarks validate the effectiveness of DB-Explore, achieving an execution accuracy of 67.0% on BIRD and 87.8% on SPIDER. Notably, our open‐source implementation based on Qwen2.5‐Coder‐7B achieves state‐of‐the‐art results at minimal computational cost, outperforming several GPT‐4‐driven Text‐to‐SQL systems.

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Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs?
Junyan Zhang | Yiming Huang | Shuliang Liu | Yubo Gao | Xuming Hu

The rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification. This study challenges the prevailing “LLM-centric” trend by systematically comparing three category methods, *i.e.,* BERT-like models fine-tuning, LLM internal state utilization, and LLM zero-shot inference across six challenging datasets. Our findings reveal that BERT-like models often outperform LLMs. We further categorize datasets into three types, perform PCA and probing experiments, and identify task-specific model strengths: BERT-like models excel in pattern-driven tasks, while LLMs dominate those requiring deep semantics or world knowledge. Subsequently, we conducted experiments on a broader range of text classification tasks to demonstrate the generalizability of our findings. We further investigated how the relative performance of different models varies under different levels of data availability. Finally, based on these findings, we propose **TaMAS**, a fine-grained task selection strategy, advocating for a nuanced, task-driven approach over a one-size-fits-all reliance on LLMs. Code is available at [https://github.com/jyzhang2002/TaMAS-TextClass](https://github.com/jyzhang2002/TaMAS-TextClass).

LLM-based optimization has shown remarkable potential in improving agentic systems. However, the conventional approach of prompting LLM-based generative optimizer with the trajectories on the whole training dataset in a single pass becomes untenable as datasets grow, leading to context window overflow and degraded pattern recognition. To address these challenges, we propose Fine-grained Generative Optimization (FGO), a scalable framework that divides large optimization tasks into manageable subsets, performs targeted optimizations, and systematically combines optimized components through progressive merging.Evaluation across ALFWorld, LogisticsQA, and GAIA benchmarks demonstrates that FGO outperforms conventional approach by 1.6-8.6% while reducing average prompt token consumption by 56.3%. Our framework provides a practical solution for scaling up LLM-based generative optimization of increasingly sophisticated agentic systems. Further analysis demonstrates that FGO achieves the most consistent performance gain in all training dataset sizes, showcasing its scalability and efficiency.

Hallucinations pose a significant obstacle to the reliability and widespread adoption of language models, yet their accurate measurement remains a persistent challenge. While many task- and domain-specific metrics have been proposed to assess faithfulness and factuality concerns, the robustness and generalization of these metrics are still untested. In this paper, we conduct a large-scale empirical evaluation of 6 diverse sets of hallucination detection metrics across 4 datasets, 37 language models from 5 families, and 5 decoding methods. Our extensive investigation reveals concerning gaps in current hallucination evaluation: metrics often fail to align with human judgments, take an overtly myopic view of the problem, and show inconsistent gains with parameter scaling. Encouragingly, LLM-based evaluation, particularly with GPT-4, yields the best overall results, and mode-seeking decoding methods seem to reduce hallucinations, especially in knowledge-grounded settings. These findings underscore the need for more robust metrics to understand and quantify hallucinations, and better strategies to mitigate them.

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The Progress Illusion: Revisiting meta-evaluation standards of LLM evaluators
Tianruo Rose Xu | Vedant Gaur | Liu Leqi | Tanya Goyal

LLM judges have gained popularity as an inexpensive and performant substitute for human evaluation. However, we observe that the meta-evaluation setting in which the reliability of these LLM evaluators is established is substantially different from their use in model development. To address this, we revisit meta-evaluations of LLM evaluators under a setting that more closely aligns with practice by examining evaluators’ ability to distinguish test system pairs that are closer in capability. Our fine-grained approach shows that all LLM evaluator’s correlations with human judgments are concerningly low when the models perform similarly, showcasing a key limitation of current norms. Equipped with this better methodology, we next analyze the impact that the choice of the reference model makes to LLM-as-a-judge evaluator performance. We show that single-reference evaluators only perform well at ranking test systems that fall within particular capability ranges, even if the standard meta-evaluation reports high overall correlation. Taken together, our analysis shows critical issues with current LLM meta-evaluation and recommend avenues for improvement.

As large language models (LLMs) are increasingly applied across various domains, enhancing safety while maintaining the helpfulness of LLMs has become a critical challenge. Recent studies solve this problem through safety-constrained online preference optimization or safety-constrained offline preference optimization. However, the safety-constrained online methods often suffer from excessive safety, which might reduce helpfulness, while the safety-constrained offline methods perform poorly in adaptively balancing safety and helpfulness. To address these limitations, we propose MidPO, a Mixture of Experts (MoE) framework for safety-helpfulness dual Preference Optimization. Firstly, MidPO devises single-preference enhanced direct preference optimization approach to transform the base model into two independent experts, termed safety and helpfulness experts, and fine-tunes the two independent experts for optimal safety or helpfulness performance. Secondly, to achieve an effective balance between safety and helpfulness, MidPO incorporates the two experts into the MoE framework and designs a dynamic routing mechanism to allocate contributions from each expert adaptively. We conduct quantitative and qualitative experiments on three popular datasets to demonstrate the proposed MidPO significantly outperforms state-of-the-art approaches in both safety and helpfulness. Code is available at https: //github.com/OutdoorManofML/MidPO.

The development of Large Language Models (LLMs) requires robust benchmarks that encompass not only academic domains but also industrial fields to effectively evaluate their applicability in real-world scenarios. In this paper, we introduce two Korean expert-level benchmarks. KMMLU-Redux, reconstructed from the existing KMMLU consists of questions from the Korean National Technical Qualification exams, with critical errors removed to enhance reliability. KMMLU-Pro is based on Korean National Professional Licensure exams to reflect professional knowledge in Korea. Our experiments demonstrate that these benchmarks comprehensively represent industrial knowledge in Korea.

While various multimodal multi-image evaluation datasets have been emerged, but these datasets are primarily based on English, and there has yet to be a Chinese multi-image dataset. To fill this gap, we introduce RealBench, the first Chinese multimodal multi-image dataset, which contains 9393 samples and 69910 images. RealBench distinguishes itself by incorporating real user-generated content, ensuring high relevance to real-world applications. Additionally, the dataset covers a wide variety of scenes, image resolutions, and image structures, further increasing the difficulty of multi-image understanding. Ultimately, we conduct a comprehensive evaluation of RealBench using 21 multimodal LLMs of different sizes, including closed-source models that support multi-image inputs as well as open-source visual and video models. The experimental results indicate that even the most powerful closed-source models still face challenges when handling multi-image Chinese scenarios. Moreover, there remains a noticeable performance gap of around 71.8% on average between open-source visual/video models and closed-source models. These results show that RealBench provides an important research foundation for further exploring multi-image understanding capabilities in the Chinese context. Our datasets will be publicly available.

The Impression section of a radiology report summarizes critical findings of a radiology report and thus plays a crucial role in communication between radiologists and physicians. Research on radiology report summarization mostly focuses on generating the Impression section by summarizing information from the Findings section, which typically details the radiologist’s observations in the radiology images. Recent work start to explore how to incorporate radiology images as input to multimodal summarization models, with the assumption that it can improve generated summary quality, as it contains richer information. However, the real effectiveness of radiology images remains unclear. To answer this, we conduct a thorough analysis to understand whether current multimodal models can utilize radiology images in summarizing Findings section. Our analysis reveals that current multimodal models often fail to effectively utilize radiology images. For example, masking the image input leads to minimal or no performance drop. Expert annotation study shows that radiology images are unnecessary when they write the Impression section.

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Localizing Malicious Outputs from CodeLLM
Mayukh Borana | Junyi Liang | Sai Sathiesh Rajan | Sudipta Chattopadhyay

We introduce FreqRank, a mutation-based defense to localize malicious components in LLM outputs and their corresponding backdoor triggers. FreqRank assumes that the malicious sub-string(s) consistently appear in outputs for triggered inputs and uses a frequency-based ranking system to identify them. Our ranking system then leverages this knowledge to localize the backdoor triggers present in the inputs. We create nine malicious models through fine-tuning or custom instructions for three downstream tasks, namely, code completion (CC), code generation (CG), and code summarization (CS), and show that they have an average attack success rate (ASR) of 86.6%. Furthermore, FreqRank’s ranking system highlights the malicious outputs as one of the top five suggestions in 98% of cases. We also demonstrate that FreqRank’s effectiveness scales as the number of mutants increases and show that FreqRank is capable of localizing the backdoor trigger effectively even with a limited number of triggered samples. Finally, we show that our approach is 35-50% more effective than other defense methods.

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Knowing More, Acting Better: Hierarchical Representation for Embodied Decision-Making
Chunhui Zhang | Zhongyu Ouyang | Xingjian Diao | Zheyuan Liu | Soroush Vosoughi

Modern embodied AI uses multimodal large language models (MLLMs) as policy models, predicting actions from final-layer hidden states. This widely adopted approach, however, assumes that monolithic last-layer representations suffice for decision-making—a structural simplification at odds with decades of cognitive science, which highlights the importance of distributed, hierarchical processing for perception and action. Addressing this foundational asymmetry, we introduce a hierarchical action probing method that explicitly aggregates representations from all layers, mirroring the brain’s multi-level organization. Experiments reveal that early layers facilitate spatial grounding, middle layers support contextual integration, and later layers enable abstract generalization—which shows MLLMs inherently encode distributed action-relevant structures. These layer-wise features are integrated by a lightweight probe for spatial reasoning and contextual understanding, without costly backbone fine-tuning. This hierarchical solution shows significant improvements over standard last-layer embodied models in physical simulators, achieving a 46.6% success rate and a 62.5% gain in spatial reasoning tasks. These findings challenge conventional assumptions in embodied AI, establishing hierarchical probing as a principled alternative grounded in both cognitive theory and empirical evidence.

The prevailing “trivia-centered paradigm” for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced and widely deployed. Existing approaches typically reduce culture to static facts or values, testing models via multiple-choice or short-answer questions that treat culture as isolated trivia. Such methods neglect the pluralistic and interactive realities of culture, and overlook how cultural assumptions permeate even ostensibly “neutral” evaluation settings.In this position paper, we argue for intentionally cultural evaluation: an approach that systematically examines the cultural assumptions embedded in all aspects of evaluation, not just in explicitly cultural tasks. We systematically characterize the what, how, and circumstances by which culturally contingent considerations arise in evaluation, and emphasize the importance of researcher positionality for fostering inclusive, culturally aligned NLP research. Finally, we discuss implications and future directions for moving beyond current benchmarking practices, discovering important applications that we don’t know exist, and involving communities in evaluation design through HCI-inspired participatory methodologies.

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Fairness in Automatic Speech Recognition Isn’t a One-Size-Fits-All
Hend ElGhazaly | Bahman Mirheidari | Heidi Christensen | Nafise Sadat Moosavi

Modern Automatic Speech Recognition (ASR) systems are increasingly deployed in high-stakes settings, including clinical interviews, public services, and educational tools, where equitable performance across speaker groups is essential. While pre-trained speech models like Whisper achieve strong overall accuracy, they often exhibit inconsistent group-level performance that varies across domains. These disparities are not fixed properties of the model, but emerge from the interaction between model, data, and task—posing challenges for fairness interventions designed in-domain.We frame fairness in ASR as a generalisation problem. We fine-tune a Whisper model on the Fair-Speech corpus using four strategies: basic fine-tuning, demographic rebalancing, gender-swapped data augmentation, and a novel contrastive learning objective that encourages gender-invariant representations. We evaluate performance across multiple aspects of fairness and utility, both in-domain and on three out-of-domain test sets: LibriSpeech, EdAcc, and CognoSpeak.Our findings show that the method with the best in-domain fairness performed worst out-of-domain, illustrating that fairness gains do not always generalise. Demographic balancing generalises more consistently, while our contrastive method offers a practical alternative: it achieves stable, cross-domain fairness improvements without requiring changes to the training data distribution, and with minimal accuracy trade-offs.

Large language models (LLMs) often exhibit tendencies that diverge from human preferences, such as favoring certain writing styles or producing overly verbose outputs. While crucial for improvement, identifying the factors driving these misalignments remains challenging due to existing evaluation methods’ reliance on coarse-grained comparisons and lack of explainability.To address this, we introduce PROFILE, an automated framework to uncover and measure factor-level preference alignment of humans and LLMs.Using PROFILE, we analyze preference alignment across three key tasks: summarization, instruction-following, and document-based QA. We find a significant discrepancy: while LLMs show poor factor-level alignment with human preferences when generating texts, they demonstrate strong alignment in discrimination tasks. We demonstrate how leveraging the identified generation-discrimination gap can be used to improve LLM alignment through multiple approaches, including fine-tuning with self-guidance.Our work highlights the value of factor-level analysis for identifying hidden misalignments and provides a practical framework for improving LLM-human preference alignment.

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Adaptive Preference Optimization with Uncertainty-aware Utility Anchor
Xiaobo Wang | Zixia Jia | Jiaqi Li | Qi Liu | Zilong Zheng

Offline preference optimization methods are efficient for large language models (LLMs) alignment. Direct Preference optimization (DPO)-like learning, one of the most popular approaches, stands out for its efficiency in reward modeling. However, these methods typically follow the convention to use Bradley-Terry (BT) reward modeling that faces several critical assumptions, including the requirement for pairwise training data, model distribution shifting, human rationality assumption, etc. To address these limitations, we propose a general framework for offline preference optimization methods, Adaptive Preference Optimization with Utility Anchor (UAPO), which introduces an anchoring function to estimate the uncertainties brought from preference data annotation. Our method enables training even in scenarios where the data is unpaired, significantly enhancing data utilization efficiency. Moreover, the anchor design makes UAPO more robust in the training process. Experimental results demonstrate that UAPO achieves competitive outcomes without the strict dependency on data pairing, paving the way for more flexible and effective preference optimization methods.

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GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning
Oussama Gabouj | Kamel Charaf | Ivan Zakazov | Nicolas Baldwin | Robert West

Large Language Models (LLMs) achieve strong performance across diverse tasks, but their effectiveness often depends on the quality of the provided context. Retrieval-Augmented Generation (RAG) enriches prompts with external information, but its reliance on static databases constrains adaptability and can result in irrelevant demonstrations. In this work, we propose a Generative Retrieval-Aligned Demonstrator (GRAD), a dynamic demonstration-based approach where an LLM model is trained to generate input-specific concise demonstrations. By tailoring demonstrations to each input, our method offers better contextual support than traditional RAG approaches. We demonstrate the superiority of GRAD under budget constraints, where we limit both the number of tokens used per demonstration and the number of tokens used for the final output. Trained solely on a math dataset, GRAD consistently outperforms strong baselines on Qwen2.5-14B across mathematical reasoning and advanced STEM questions, highlighting GRAD’s robust generalization to out-of-distribution (OOD) domains such as physics, chemistry, and computer science. Furthermore, we show that demonstrations generated by trained smaller models can effectively guide larger target models, reducing training costs while maintaining competitive accuracy. Overall, this work introduces a scalable demonstration generator model presenting the first step toward a dynamic few-shot learning paradigm in resource-constrained settings. We release the code used for the project: https://github.com/charafkamel/GRAD-demonstration-sampler

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IoTMigrator: LLM-driven Embedded IoT Code Migration across Different OSes for Cloud-device Integration
Yq | Kaijie Gong | Yi Gao | Hao Wang | Wei Dong

The increasing prevalence of embedded systems has necessitated manufacturers to migrate product code, transferring existing products to new embedded operating systems (OSes) for getting better compatibility and performance. Since manufacturers’ product code predominantly employs the Thing Specification Language (TSL) paradigm for cloud connectivity, migrated code consequently adheres to the same TSL standard. However, embedded code migration under the TSL paradigm proves more complex than conventional code migration. Neither outline-based code generation nor common code translation techniques can adequately address this challenge, despite their prevalence in existing systems. There exists a growing demand for a algorithm tailored to TSL paradigm embedded code migration. In response to this demand, we have developed IoTMigrator that employs a multi-agent pipeline to handle the issue. The key insight of our algorithm is the TSL enhancer, specifically designed for the characteristics of the TSL paradigm, which serves as a crucial component in the agent pipeline.To demonstrate the superiority of our algorithm, we have established our own benchmark, which includes six tasks across two OSes, RIOT and Zephyr. We adopted two key metrics: compilation pass rate and task completeness score. The experiment results show that our algorithm outperforms the baseline by an average of at least 50.5% for pass rate and 13.0% for completeness across all tasks in RIOT, and at least 83.4% for pass rate and 18.4% for completeness in Zephyr. This work will be open-sourced in the future.

Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge to improve factuality. However, existing RAG systems frequently underutilize the retrieved documents, failing to extract and integrate the key clues needed to support faithful and interpretable reasoning, especially in cases where relevant evidence is implicit, scattered, or obscured by noise. To address this issue, we propose ClueAnchor, a novel framework for enhancing RAG via clue-anchored reasoning exploration and optimization. ClueAnchor extracts key clues from retrieved content and generates multiple reasoning paths based on different knowledge configurations, optimizing the model by selecting the most appropriate reasoning path for the given context through reward-based preference optimization. Experiments show that ClueAnchor significantly outperforms prior RAG baselines in the completeness and robustness of reasoning. Further analysis confirms its strong resilience to noisy or partially relevant retrieved content, as well as its capability to identify supporting evidence even in the absence of explicit clue supervision during inference. All codes are available at https://github.com/thunlp/ClueAnchor.

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BAGELS: Benchmarking the Automated Generation and Extraction of Limitations from Scholarly Text
Ibrahim Al Azher | Miftahul Jannat Mokarrama | Zhishuai Guo | Sagnik Ray Choudhury | Hamed Alhoori

In scientific research, “limitations” refer to the shortcomings, constraints, or weaknesses of a study. A transparent reporting of such limitations can enhance the quality and reproducibility of research and improve public trust in science. However, authors often underreport limitations in their papers and rely on hedging strategies to meet editorial requirements at the expense of readers’ clarity and confidence. This tendency, combined with the surge in scientific publications, has created a pressing need for automated approaches to extract and generate limitations from scholarly papers. To address this need, we present a full architecture for computational analysis of research limitations. Specifically, we (1) create a dataset of limitations from ACL, NeurIPS, and PeerJ papers by extracting them from the text and supplementing them with external reviews; (2) we propose methods to automatically generate limitations using a novel Retrieval Augmented Generation (RAG) technique; (3) we design a fine-grained evaluation framework for generated limitations, along with a meta-evaluation of these techniques. Code and datasets are available at: Code: https://github.com/IbrahimAlAzhar/BAGELS_Limitation_GenDataset: https://huggingface.co/datasets/IbrahimAlAzhar/limitation-generation-dataset-bagels

This work stems from an observed limitation of text encoders: embeddings may not be able to recognize fine-grained entities or events within encoded semantics, resulting in failed retrieval even in simple cases. To examine such behaviors, we first introduce a new evaluation dataset, CapRetrieval, in which passages are image captions and queries are phrases targeting entity or event concepts in diverse forms. Zero-shot evaluation suggests that encoders often struggle with these fine-grained matching, regardless of training sources or model size. Aiming for enhancement, we proceed to finetune encoders with our proposed data generation strategies, enabling a small 0.1B encoder to outperform the state-of-the-art 7B model. Within this process, we further uncover the granularity dilemma, a challenge for embeddings to capture fine-grained salience while aligning with overall semantics. Our dataset, code and models in this work are publicly released at https://github.com/lxucs/CapRetrieval.

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Over-Generation and Compaction: A Prompting Strategy for Procedural Text Adaptation with Large Language Models
Hyeongsik Kim | Yanheng Xu | Chaoqun Dong | Fei Du

Procedural text adaptation—such as modifying recipes or revising instructional guides—has traditionally relied on specialized models extensively fine‐tuned for specific domains. To address the scalability limitations of such approaches, recent research has increasingly turned to general‐purpose large language models (LLMs). However, existing prompting strategies for LLMs often yield superficial or erroneous adaptations due to alignment‐induced biases and the inherent complexity of procedural editing. To overcome these challenges, we propose the Over‐generation‐and‐Compaction (OC) prompting strategy, which first elicits an exhaustive set of procedural details to leverage the model’s latent knowledge, and subsequently compacts them into concise, coherent adaptations. We further introduce Recipe Consistency & Feasibility (RCF), a novel metric for systematically assessing procedural validity and practicality in cooking recipe adaptations. Experiments on public datasets demonstrate that OC significantly improves adaptation consistency and feasibility compared to baseline prompting methods, without the need for additional fine-tuning or curated training resources.

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TransBERT: A Framework for Synthetic Translation in Domain-Specific Language Modeling
Julien Knafou | Luc Mottin | Anaïs Mottaz | Alexandre Flament | Patrick Ruch

The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP) tools. We present TransBERT, a novel framework for pre-training language models using exclusively synthetically translated text, and introduce TransCorpus, a scalable translation toolkit. Focusing on the life sciences domain in French, our approach demonstrates that state-of-the-art performance on various downstream tasks can be achieved solely by leveraging synthetically translated data. We release the TransCorpus toolkit, the TransCorpus-bio-fr corpus (36.4GB of French life sciences text), TransBERT-bio-fr, its associated pre-trained language model and reproducible code for both pre-training and fine-tuning. Our results highlight the viability of synthetic translation in a high-resource translation direction for building high-quality NLP resources in low-resource language/domain pairs.

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Beyond Fixed-Length Calibration for Post-Training Compression of LLMs
Jaehoon Oh | Dokwan Oh

As large language models (LLMs) continue to grow in size, their practical deployment increasingly relies on a range of compression techniques, such as quantization, pruning, and low-rank approximation. Especially, post-training compression methods–which do not require re-training–have drawn considerable interest. Many recent methods leverage calibration data to capture magnitude or second-order characteristics of input activations. However, the role and significance of calibration data remain underexplored. In this study, we demonstrate that the sequence length of calibration data plays a crucial role in the effectiveness of post-training compression methods for LLMs. We then analyze input activations and find that, within the normalized hidden states, the embedding of the first token exhibits characteristics opposite to those of subsequent tokens. Building on this insight, we introduce state-aware length calibration, a technique that applies masking along the sequence axis, specifically targeting normalized hidden states. Experimental results show that our approach improves perplexity and zero-shot downstream tasks performance.

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Attributes as Textual Genes: Leveraging LLMs as Genetic Algorithm Simulators for Conditional Synthetic Data Generation
Guangzeng Han | Weisi Liu | Xiaolei Huang

Large Language Models (LLMs) excel at generating synthetic data, but ensuring its quality and diversity remains challenging. We propose Genetic Prompt, a novel framework that combines genetic algorithms with LLMs to augment synthetic data generation. Our approach treats semantic text attributes as gene sequences and leverages the LLM to simulate crossover and mutation operations. This genetic process enhances data quality and diversity by creating novel attribute combinations, yielding synthetic distributions closer to real-world data. To optimize parent selection, we also integrate an active learning scheme that expands the offspring search space. Our experiments on multiple NLP tasks reveal several key findings: Genetic Prompt not only significantly outperforms state-of-the-art baselines but also shows robust performance across various generator model sizes and scales. Moreover, we demonstrate that fusing our synthetic data with the original training set significantly boosts downstream model performance, particularly for class-imbalanced scenarios. Our findings validate that Genetic Prompt is an effective method for producing high-quality synthetic data for a wide range of NLP applications.

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ReCoVeR the Target Language: Language Steering without Sacrificing Task Performance
Hannah Sterz | Fabian David Schmidt | Goran Glavaš | Ivan Vulić

As they become increasingly multilingual, Large Language Models (LLMs) exhibit more language confusion, i.e., they tend to generate answers in a language different from the language of the prompt or the answer language explicitly requested by the user. In this work, we propose ReCoVeR (REducing language COnfusion in VEctor Representations), a novel lightweight approach for reducing language confusion based on language-specific steering vectors. We first isolate language vectors with the help of multi-parallel corpus and then effectively leverage those vectors for effective LLM steering via fixed (i.e., unsupervised) as well as trainable steering functions. Our extensive evaluation, encompassing three benchmarks and 18 languages, shows that ReCoVeR effectively mitigates language confusion in both monolingual and cross-lingual setups while at the same time—and in contrast to prior language steering methods—retaining task performance. Our data code is available at https://github.com/hSterz/recover.

Recent advancements in Large Language Models (LLMs) have demonstrated sophisticated capabilities, including the ability to process and comprehend extended contexts. These emergent capabilities necessitate rigorous evaluation methods to effectively assess their performance in long-context understanding. In this paper, we present LC-Eval, a bilingual, multi-task evaluation benchmark designed to evaluate long-context understanding in English and Arabic, targeting context lengths ranging from 4k to over 128k tokens. LC-Eval introduces four novel and challenging tasks: multi-document question answering, bilingual question answering, claim verification within a paragraph, and multiple-choice questions based on long contexts. These tasks are designed to assess LLMs’ abilities in deep reasoning, document comprehension, information tracing, and bilingual information extraction and understanding. The benchmark includes datasets in both Arabic and English for each task, allowing for a comparative analysis of their performance across different text genres. Evaluations were conducted on both open-weight and closed LLMs, with results indicating that LC-Eval presents significant challenges. Even high-performing models, such as GPT-4o, struggled with certain tasks, highlighting the complexity and rigor of the benchmark.

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OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models
Monika Wysoczańska | Shyamal Buch | Anurag Arnab | Cordelia Schmid

Large vision-language models (VLMs) often struggle to generate long and factual captions. However, traditional measures for hallucination and factuality are not well suited for evaluating longer, more diverse captions and in settings where ground-truth human-annotated captions are unavailable. We introduce OVFact, a novel method for measuring caption factuality of long captions that leverages open-vocabulary visual grounding and tool-based verification without depending on human annotations. Our method improves agreement with human judgements and captures both caption descriptiveness (recall) and factual precision in the same metric. Furthermore, unlike previous metrics, our reference-free method design enables new applications towards factuality-based data filtering. We observe models trained on an OVFact-filtered (2.5-5x less) subset of a large-scale, noisy (VLM-generated) pretraining set meaningfully improve factuality precision without sacrificing caption descriptiveness across a range of downstream long caption benchmarks.

Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities. Although recent large language model (LLM)-based methods have demonstrated strong performance, they typically adopt static fusion strategies that integrate all available modalities uniformly. This overlooks the fact that the necessity of multimodal cues can vary significantly across utterances. In this work, we propose an adaptive modality selection framework for MERC. The core of our approach is a modality selection module based on Group Relative Policy Optimization (GRPO), which enables a LoRA-tuned LLM to reason about the necessity of multimodal input via chain-of-thought (CoT) generation. This process does not require manually labeled modality selection data and is trained in a fully unsupervised manner. The selected modality configuration is then provided as input to a downstream emotion classifier, which is also implemented using a LoRA-tuned LLM and trained to predict emotional states. Experimental results on benchmark multimodal dialogue datasets show that our method consistently outperforms strong baselines, demonstrating the effectiveness of adaptive modality selection in improving recognition accuracy. Our code is available at https://github.com/youflyaway/Modality-Selection-Enhanced-LoRA-Tuned-LLMs.

The integration of Large Language Models (LLMs) with external sources is becoming increasingly common, with Retrieval-Augmented Generation (RAG) being a prominent example. However, this integration introduces vulnerabilities of Indirect Prompt Injection (IPI) attacks, where hidden instructions embedded in external data can manipulate LLMs into executing unintended or harmful actions. We recognize that IPI attacks fundamentally rely on the presence of instructions embedded within external content, which can alter the behavioral states of LLMs. Can the effective detection of such state changes help us defend against IPI attacks? In this paper, we propose InstructDetector, a novel detection-based approach that leverages the behavioral states of LLMs to identify potential IPI attacks. Specifically, we demonstrate the hidden states and gradients from intermediate layers provide highly discriminative features for instruction detection. By effectively combining these features, InstructDetector achieves a detection accuracy of 99.60% in the in-domain setting and 96.90% in the out-of-domain setting, and reduces the attack success rate to just 0.03% on the BIPIA benchmark. The code is publicly available at https://github.com/MYVAE/Instruction-detection.

Evaluating text generation capabilities of large language models (LLMs) is challenging, particularly for low-resource languages where methods for direct assessment are scarce. We propose MUG-Eval, a novel framework that evaluates LLMs’ multilingual generation capabilities by transforming existing benchmarks into conversational tasks and measuring the LLMs’ accuracies on those tasks. We specifically designed these conversational tasks to require effective communication in the target language. Then, we simply use task success rate as a proxy for successful conversation generation. Our approach offers two key advantages: it is independent of language-specific NLP tools or annotated datasets, which are limited for most languages, and it does not rely on LLMs-as-judges, whose evaluation quality degrades outside a few high-resource languages. We evaluate 8 LLMs across 30 languages spanning high, mid, and low-resource categories, and we find that MUG-Eval correlates strongly with established benchmarks (r > 0.75) while enabling standardized comparisons across languages and models. Our framework provides a robust and resource-efficient solution for evaluating multilingual generation that can be extended to thousands of languages.

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CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks
Sunguk Choi | Yonghoon Kwon | Heondeuk Lee

Long chain-of-thought (CoT) prompting helps Large Language Models (LLMs) solve difficult problems, but very long traces often slow or even degrade performance on fast, intuitive “System-1” tasks. We introduce Connector-Aware Compact CoT (CAC-CoT) — a method that deliberately restricts reasoning to a small, fixed set of connector phrases, steering the model toward concise and well — structured explanations. Despite its simplicity, our synthetic method with general-purpose LLMs yields a high-quality training quality. CAC-CoT achieves ≈ 85% on GSM8K and ≈ 40% on GPQA (System-2) while also achieving ≈ 85% on S1-Bench (System-1), surpassing the baseline by over 20%. Its reasoning traces average ≈ 300 tokens(ART), about one-third the length of baseline traces, delivering higher efficiency without loss of accuracy.

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On the Versatility of Sparse Autoencoders for In-Context Learning
Ikhyun Cho | Gaeul Kwon | Julia Hockenmaier

Sparse autoencoders (SAEs) are emerging as a key analytical tool in the field of mechanistic interpretability for large language models (LLMs). While SAEs have primarily been used for interpretability, we shift focus and explore an understudied question: “Can SAEs be applied to practical tasks beyond interpretability?” Given that SAEs are trained on billions of tokens for sparse reconstruction, we believe they can serve as effective extractors, offering a wide range of useful knowledge that can benefit practical applications. Building on this motivation, we demonstrate that SAEs can be effectively applied to in-context learning (ICL). In particular, we highlight the utility of the SAE-reconstruction loss by showing that it provides a valuable signal in ICL—exhibiting a strong correlation with LLM performance and offering a powerful unsupervised approach for prompt selection. These findings underscore the versatility of SAEs and reveal their potential for real-world applications beyond interpretability. Our code is available at https://github.com/ihcho2/SAE-GPS.

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More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG
Shahar Levy | Nir Mazor | Lihi Shalmon | Michael Hassid | Gabriel Stanovsky

Retrieval-Augmented Generation (RAG) enhances the accuracy of Large Language Model (LLM) responses by leveraging relevant external documents during generation. Although previous studies noted that retrieving many documents can degrade performance, they did not isolate how the quantity of documents affects performance while controlling for context length. We evaluate various language models on custom datasets derived from a multi-hop QA task. We keep the context length and position of relevant information constant while varying the number of documents, and find that increasing the document count in RAG settings poses significant challenges for most LLMs, reducing performance by up to 20%. However, Qwen2 maintained consistent results across increasing document counts, indicating better multi-document handling capability. Finally, our results indicate that processing multiple documents is a separate challenge from handling long contexts. We will publicly release the datasets and code upon publication to facilitate further research in multi-document retrieval.

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CLEAR: A Comprehensive Linguistic Evaluation of Argument Rewriting by Large Language Models
Thomas Huber | Christina Niklaus

While LLMs have been extensively studied on general text generation tasks, there is less research on text rewriting, a task related to general text generation, and particularly on the behavior of models on this task. In this paper we analyze what changes LLMs make in a text rewriting setting. We focus specifically on argumentative texts and their improvement, a task named Argument Improvement (ArgImp). We present CLEAR: an evaluation pipeline consisting of 57 metrics mapped to four linguistic levels: lexical, syntactic, semantic and pragmatic. This pipeline is used to examine the qualities of LLM-rewritten arguments on a broad set of argumentation corpora and compare the behavior of different LLMs on this task and analyze the behavior of different LLMs on this task in terms of linguistic levels. By taking all four linguistic levels into consideration, we find that the models perform ArgImp by shortening the texts while simultaneously increasing average word length and merging sentences. Overall we note an increase in the persuasion and coherence dimensions.

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ALRPHFS: Adversarially Learned Risk Patterns with Hierarchical Fast & Slow Reasoning for Robust Agent Defense
Shiyu Xiang | Tong Zhang | Ronghao Chen

LLM Agents are becoming central to intelligent systems. However, their deployment raises serious safety concerns. Existing defenses largely rely on “Safety Checks”, which struggle to capture the complex semantic risks posed by harmful user inputs or unsafe agent behaviors—creating a significant semantic gap between safety checks and real-world risks. To bridge this gap, we propose a novel defense framework, ALRPHFS (Adversarially Learned Risk Patterns with Hierarchical Fast & Slow Reasoning). ALRPHFS consists of two core components: (1) an offline adversarial self-learning loop to iteratively refine a generalizable and balanced library of risk patterns, substantially enhancing robustness without retraining the base LLM, and (2) an online hierarchical fast & slow reasoning engine that balances detection effectiveness with computational efficiency. Experimental results demonstrate that our approach achieves superior overall performance compared to existing baselines, achieving a best‐in‐class average accuracy of 80% and exhibiting strong generalizability across agents and tasks.

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Stop Playing the Guessing Game! Evaluating Conversational Recommender Systems via Target-free User Simulation
SungHwan Kim | Kwangwook Seo | Tongyoung Kim | Jinyoung Yeo | Dongha Lee

Recent developments in Conversational Recommender Systems (CRSs) have focused on simulating real-world interactions between users and CRSs to create more realistic evaluation environments. Despite considerable advancements, reliably assessing the capability of CRSs in eliciting user preferences remains a significant challenge. We observe that user-CRS interactions in existing evaluation protocols resemble a guessing game, as they construct target-biased simulators pre-encoded with target item knowledge, thereby allowing the CRS to shortcut the elicitation process. Moreover, we reveal that current evaluation metrics, which predominantly emphasize single-turn recall of target items, suffer from target ambiguity in multi-turn settings and overlook the intermediate process of preference elicitation. To address these issues, we introduce PEPPER, a novel CRS evaluation protocol with target-free user simulators that enable users to gradually discover their preferences through enriched interactions, along with detailed measures for comprehensively assessing the preference elicitation capabilities of CRSs. Through extensive experiments, we validate PEPPER as a reliable simulation environment and offer a thorough analysis of how effectively current CRSs perform in preference elicitation and recommendation.

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Out-of-Context Reasoning in Large Language Models
Jonathan Shaki | Emanuele La Malfa | Michael J. Wooldridge | Sarit Kraus

We study how large language models (LLMs) reason about memorized knowledge through simple binary relations such as equality (=), inequality (<), and inclusion (⊂). Unlike in-context reasoning, the axioms (e.g., a < b, b < c) are only seen during training and not provided in the task prompt (e.g., evaluating a < c). The tasks require one or more reasoning steps, and data aggregation from one or more sources, showing performance change with task complexity. We introduce a lightweight technique, out-of-context representation learning, which trains only new token embeddings on axioms and evaluates them on unseen tasks. Across reflexivity, symmetry, and transitivity tests, LLMs mostly perform statistically significant better than chance, making the correct answer extractable when testing multiple phrasing variations, but still fall short of consistent reasoning on every single query. Analysis shows that the learned embeddings are organized in structured ways, suggesting real relational understanding. Surprisingly, it also indicates that the core reasoning happens during the training, not inference.

Reasoning ability of large language models (LLMs) is a crucial ability,especially in complex decision-making tasks. One significant task to show LLMs’reasoning capability is code time complexity prediction, which involves variousintricate factors such as the input range of variables and conditional loops.Current benchmarks fall short of providing a rigorous assessment due to limiteddata, language constraints, and insufficient labeling. They do not consider timecomplexity based on input representation and merely evaluate whether predictionsfall into the same class, lacking a measure of how close incorrect predictionsare to the correct ones.To address these dependencies, we introduce CodeComplex, the first robust andextensive dataset designed to evaluate LLMs’ reasoning abilities in predictingcode time complexity. CodeComplex comprises 4,900 Java codes and an equivalentnumber of Python codes, overcoming language and labeling constraints, carefullyannotated with complexity labels based on input characteristics by a panel ofalgorithmic experts. Additionally, we propose specialized evaluation metrics forthe reasoning of complexity prediction tasks, offering a more precise andreliable assessment of LLMs’ reasoning capabilities. We release our dataset andbaseline models publicly to encourage the relevant (NLP, SE, and PL) communitiesto utilize and participate in this research. Our code and data are available athttps://github.com/sybaik1/CodeComplex.

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Weak2Wise: An Automated, Lightweight Framework for Weak-LLM-Friendly Reasoning Synthesis
Jianing Lin | Yuanfang Guo | Shunning Liu | Zeming Liu | Yunhong Wang

Recent advances in large language model (LLM) fine‐tuning have shown that training data augmented with high-quality reasoning traces can remarkably improve downstream performance. However, existing approaches usually rely on expensive manual annotations or auxiliary models, and fail to address the unique constraints of smaller “weak” LLMs. To bridge these gaps, we introduce Weak2Wise, a fully automated, lightweight framework for synthesizing high‐quality, weak-LLM-friendly reasoning traces. Starting from a QA dataset, Weak2Wise filters out the samples that can already be correctly answered by the weak LLM, gathers diverse candidate reasoning traces from multiple strong LLMs, and leverages our Step‐Mask scoring to rank and truncate the most guidance‐effective traces. These reasoning traces are then used for fine‐tuning, yielding substantial improvements in the weak LLM’s reasoning abilities. The name Weak2Wise has two meanings: using a “weak” LLM to select the “wisest” reasoning traces generated by stronger LLMs, and fine‐tuning the same weak LLM on these reasoning traces to become “wiser”. We further use Weak2Wise to build GR-1K, a 1,000‐sample math and science QA‐reasoning dataset optimized for weak LLMs, and fine‐tune Qwen2.5‐7B on it to create GR‐7B, which achieves superior performance on AIME2024, MATH‐500, and GPQA Diamond benchmarks. Our codes are publicly released to facilitate further research.

We introduce Spire, a speech-augmented language model (LM) capable of both translating and transcribing speech input from English into 10 other languages as well as translating text input in both language directions. Spire integrates the speech modality into an existing multilingual LM via speech discretization and continued pre-training using only 42.5 K hours of speech. In particular, we adopt the pretraining framework of multilingual LMs and treat discretized speech input as an additional translation language. This approach not only equips the model with speech capabilities, but also preserves its strong text-based performance. We achieve this using significantly less data than existing speech LMs, demonstrating that discretized speech input integration as an additional language is feasible during LM adaptation. We make our code and models available to the community.

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LLM Agents at the Roundtable: A Multi-Perspective and Dialectical Reasoning Framework for Essay Scoring
Jinhee Jang | Ayoung Moon | Minkyoung Jung | YoungBin Kim | Seung Jin Lee

The emergence of large language models (LLMs) has brought a new paradigm to automated essay scoring (AES), a long-standing and practical application of natural language processing in education. However, achieving human-level multi-perspective understanding and judgment remains a challenge. In this work, we propose Roundtable Essay Scoring (RES), a multi-agent evaluation framework designed to perform precise and human-aligned scoring under a zero-shot setting. RES constructs evaluator agents based on LLMs, each tailored to a specific prompt and topic context. Each agent independently generates a trait-based rubric and conducts a multi-perspective evaluation. Then, by simulating a roundtable-style discussion, RES consolidates individual evaluations through a dialectical reasoning process to produce a final holistic score that more closely aligns with human evaluation. By enabling collaboration and consensus among agents with diverse evaluation perspectives, RES outperforms prior zero-shot AES approaches. Experiments on the ASAP dataset using ChatGPT and Claude show that RES achieves up to a 34.86% improvement in average QWK over straightforward prompting (Vanilla) methods.

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NormAL LoRA: What is the perfect size?
Aastik | Topu Sai Meghana | Chinmay Prakash Kulkarni | Pragya Paramita Sahu

Large Language Models (LLMs) are pivotal in enabling intelligent experiences across various applications, from summarization to advanced content organization and retrieval functionalities. However, deploying LLMs for diverse tasks is fundamentally constrained by memory and compute limitations, making it impractical to fine-tune separate models for each task. Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) offer a scalable solution for multi-task LLM deployment. Despite its potential, LoRA faces challenges in selecting optimal ranks and layers for each task-model pair, often resulting in inefficiencies and unnecessary parameters. We introduce Norm Adaptive Localized (NormAL) LoRA, a novel variant that employs rank-norm regularization to dynamically determine the optimal rank for each weight matrix, ensuring adaptation is concentrated where it is most impactful. Our approach reduces adapter parameters by 37% while preserving full fine-tuning performance, making NormAL LoRA a transformative tool for enabling efficient, scalable, and space-constrained AI deployments across diverse industries and applications.

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Inclusive Leadership in the Age of AI: A Dataset and Comparative Study of LLMs vs. Real-Life Leaders in Workplace Action Planning
Vindhya Singh | Sabine Schulte im Walde | Ksenia Keplinger

Generative Large Language Models have emerged as useful tools, reshaping professional workflows. However, their efficacy in inherently complex and human-centric tasks such as leadership and strategic planning remains underexplored. In this interdisciplinary study, we present a novel dataset and compare LLMs and human leaders in the context of workplace action planning, specifically focusing on translating the abstract idea of inclusion into actionable SMART goals. We developed the Leader Success Bot, a script-based chatbot co-designed with domain experts, to guide more than 250 real-life leaders in generating inclusive workplace action plans. We systematically prompted seven state-of-the-art chat-based LLMs to perform the same task using the socio-demographic data of real-life leaders and instructions co-developed with domain experts. Our publicly released dataset enables direct comparison between human and LLM-generated workplace action plans, offering insights into their respective strengths, biases, and limitations. Our findings highlight critical gaps and opportunities for LLMs in leadership applications, fostering interdisciplinary collaboration and NLP applications.

Direct Preference Optimization (DPO) has been demonstrated to be highly effective in mitigating hallucinations in Large Vision Language Models (LVLMs) by aligning their outputs more closely with human preferences. Despite the recent progress, existing methods suffer from two drawbacks: 1) Lack of scalable token-level rewards; and 2) Neglect of visual-anchored tokens. To this end, we propose a novel Token Preference Optimization model with self-calibrated rewards (dubbed as TPO), which adaptively attends to visual correlated tokens without fine-grained annotations. Specifically, we introduce a token-level visual-anchored reward as the difference of the logistic distributions of generated tokens conditioned on the raw image and the corrupted one. In addition, to highlight the informative visual-anchored tokens, a visual-aware training objective is proposed to enhance more accurate token-level optimization. Extensive experimental results have manifested the state-of-the-art performance of the proposed TPO. For example, by building on top of LLaVA and Qwen, our TPO boosts the performance absolute improvement for hallucination benchmarks.

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EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion
Advait Joglekar | Divyanshu Singh | Rooshil Rohit Bhatia | Srinivasan Umesh

Voice Conversion research in recent times has increasingly focused on improving the zero-shot capabilities of existing methods. Despite remarkable advancements, current architectures still tend to struggle in zero-shot cross-lingual settings. They are also often unable to generalize for speakers of unseen languages and accents. In this paper, we adopt a simple yet effective approach that combines discrete speech representations from self-supervised models with a non-autoregressive Diffusion-Transformer based conditional flow matching speech decoder. We show that this architecture allows us to train a voice-conversion model in a purely textless, self-supervised fashion. Our technique works without requiring multiple encoders to disentangle speech features. Our model also manages to excel in zero-shot cross-lingual settings even for unseen languages. We provide our code, model checkpoint and demo samples here: https://github.com/ez-vc/ez-vc

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Length Representations in Large Language Models
Sangjun Moon | Dasom Choi | Jingun Kwon | Hidetaka Kamigaito | Manabu Okumura

Large language models (LLMs) have shown remarkable capabilities across various tasks, that are learned from massive amounts of text-based data. Although LLMs can control output sequence length, particularly in instruction-based settings, the internal mechanisms behind this control have been unexplored yet. In this study, we provide empirical evidence on how output sequence length information is encoded within the internal representations in LLMs. In particular, our findings show that multi-head attention mechanisms are critical in determining output sequence length, which can be adjusted in a disentangled manner. By scaling specific hidden units within the model, we can control the output sequence length without losing the informativeness of the generated text, thereby indicating that length information is partially disentangled from semantic information. Moreover, some hidden units become increasingly active as prompts become more length-specific, thus reflecting the model’s internal awareness of this attribute. Our findings suggest that LLMs have learned robust and adaptable internal mechanisms for controlling output length without any external control.

Program-of-Thought, which aims to use program instead of natural language in reasoning, is an important way for LLMs to solve mathematical problems. Since different programming languages excel in different areas, it is natural to use the most suitable language for solving specific problems. However, current research only focuses on single language PoT, ignoring the differences between programming languages. Therefore, this paper proposes a multilingual programme reasoning method, MultiLingPoT, and deeply explores the impact of multilingual integration in the training and inference. This method allows the model to answer questions using multiple languages by fine-tuning on multilingual data and improving individual language’s reasoning accuracy by 2.5%. Additionally, prior and posterior selection methods are used to help the model select the most suitable language during inference, and achieves 8% performance gains. Finally, our code metric analysis shows that language differences manifest in encapsulation levels and implementation granularity, while strategic deviation from language conventions can enhances code performance.

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Simulating Identity, Propagating Bias: Abstraction and Stereotypes in LLM-Generated Text
Pia Sommerauer | Giulia Rambelli | Tommaso Caselli

Persona-prompting is a growing strategy to steer LLMs toward simulating particular perspectives or linguistic styles through the lens of a specified identity. While this method is often used to personalize outputs, its impact on how LLMs represent social groups remains underexplored. In this paper, we investigate whether persona-prompting leads to different levels of linguistic abstraction—an established marker of stereotyping—when generating short texts linking socio-demographic categories with stereotypical or non-stereotypical attributes. Drawing on the Linguistic Expectancy Bias framework, we analyze outputs from six open-weight LLMs under three prompting conditions, comparing 11 persona-driven responses to those of a generic AI assistant. To support this analysis, we introduce Self-Stereo, a new dataset of self-reported stereotypes from Reddit. We measure abstraction through three metrics: concreteness, specificity, and negation. Our results highlight the limits of persona-prompting in modulating abstraction in language, confirming criticisms about the ecology of personas as representative of socio-demographic groups and raising concerns about the risk of propagating stereotypes even when seemingly evoking the voice of a marginalized groups.

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Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs
Zhikai Ding | Shiyu Ni | Keping Bi

Large Vision-Language Models (LVLMs) demonstrate strong visual question answering (VQA) capabilities but are shown to hallucinate. A reliable model should perceive its knowledge boundaries—knowing what it knows and what it does not. This paper investigates LVLMs’ perception of their knowledge boundaries by evaluating three types of confidence signals: probabilistic confidence, answer consistency-based confidence, and verbalized confidence. Experiments on three LVLMs across three VQA datasets show that, although LVLMs possess a reasonable perception level, there is substantial room for improvement. Among the three confidence, probabilistic and consistency-based signals are more reliable indicators, while verbalized confidence often leads to overconfidence. To enhance LVLMs’ perception, we adapt several established confidence calibration methods from Large Language Models (LLMs) and propose three effective methods. Additionally, we compare LVLMs with their LLM counterparts, finding that jointly processing visual and textual inputs decreases question-answering performance but reduces confidence, resulting in improved perception level compared to LLMs.

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Benchmarking Large Language Models for Cryptanalysis and Side-Channel Vulnerabilities
Utsav Maskey | Chencheng Zhu | Usman Naseem

Recent advancements in Large Language Models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarking across diverse tasks. However, cryptanalysis—a critical area for data security and its connection to LLMs’ generalization abilities remains underexplored in LLM evaluations. To address this gap, we evaluate the cryptanalytic potential of state‐of‐the‐art LLMs on ciphertexts produced by a range of cryptographic algorithms. We introduce a benchmark dataset of diverse plaintexts—spanning multiple domains, lengths, writing styles, and topics—paired with their encrypted versions. Using zero‐shot and few‐shot settings along with chain‐of‐thought prompting, we assess LLMs’ decryption success rate and discuss their comprehension abilities. Our findings reveal key insights into LLMs’ strengths and limitations in side‐channel scenarios and raise concerns about their susceptibility to under-generalization related attacks. This research highlights the dual‐use nature of LLMs in security contexts and contributes to the ongoing discussion on AI safety and security.

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MTabVQA: Evaluating Multi-Tabular Reasoning of Language Models in Visual Space
Anshul Singh | Chris Biemann | Jan Strich

Vision-Language Models (VLMs) have demonstrated remarkable capabilities in interpreting visual layouts and text. However, a significant challenge remains in their ability to interpret robustly and reason over multi-tabular data presented as images, a common occurrence in real-world scenarios like web pages and digital documents. Existing benchmarks typically address single tables or non-visual data (text/structured). This leaves a critical gap: they don’t assess the ability to parse diverse table images, correlate information across them, and perform multi-hop reasoning on the combined visual data. To bridge this evaluation gap, we introduce MTabVQA, a novel benchmark specifically designed for multi-tabular visual question answering. MTabVQA comprises 3,745 complex question-answer pairs that necessitate multi-hop reasoning across several visually rendered table images. We provide extensive benchmark results for state-of-the-art VLMs on MTabVQA, revealing significant performance limitations. We further investigate post-training techniques to enhance these reasoning abilities and release MTabVQA-Instruct, a large-scale instruction-tuning dataset. Our experiments show that fine-tuning VLMs with MTabVQA-Instruct substantially improves their performance on visual multi-tabular reasoning. Code and dataset are available online: .

Despite impressive advances in large language models (LLMs), existing benchmarks often focus on single-turn or single-step tasks, failing to capture the kind of iterative reasoning required in real-world settings. To address this limitation, we introduce **TurnBench**, a novel benchmark that evaluates multi-turn, multi-step reasoning through an interactive code-breaking task inspired by the “Turing Machine Board Game.” In each episode, a model must uncover hidden logical or arithmetic rules by making sequential guesses, receiving structured feedback, and integrating clues across multiple rounds. This dynamic setup requires models to reason over time, adapt based on past information, and maintain consistency across steps—capabilities underexplored in current benchmarks. TurnBench includes two modes: *Classic*, which tests standard reasoning, and *Nightmare*, which introduces increased complexity and requires robust inferential chains. To support fine-grained analysis, we provide ground-truth annotations for intermediate reasoning steps. Our evaluation of state-of-the-art LLMs reveals significant gaps: the best model achieves 84% accuracy in Classic mode, but performance drops to 18% in Nightmare mode. In contrast, human participants achieve 100% in both, underscoring the challenge TurnBench poses to current models. By incorporating feedback loops and hiding task rules, TurnBench reduces contamination risks and provides a rigorous testbed for diagnosing and advancing multi-step, multi-turn reasoning in LLMs.

Step-by-step reasoning has become a standard approach for large language models (LLMs) to tackle complex tasks. While this paradigm has proven effective, it raises a fundamental question: How can we verify that an LLM’s reasoning is accurately grounded in knowledge? To address this question, we introduce a novel evaluation suite that systematically assesses the knowledge grounding of intermediate reasoning. Our framework comprises three key components. (1) Principal Knowledge Collection, a large-scale repository of atomic knowledge essential for reasoning. Based on the collection, we propose (2) knowledge-grounded evaluation metrics designed to measure how well models recall and apply prerequisite knowledge in reasoning. These metrics are computed by our (3) evaluator LLM, a lightweight model optimized for cost-effective and reliable metric computation. Our evaluation suite demonstrates remarkable effectiveness in identifying missing or misapplied knowledge elements, providing crucial insights for uncovering fundamental reasoning deficiencies in LLMs. Beyond evaluation, we demonstrate how these metrics can be integrated into preference optimization, showcasing further applications of knowledge-grounded evaluation. Our evaluation suite is publicly available.

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Stratified Selective Sampling for Instruction Tuning with Dedicated Scoring Strategy
Paramita Mirza | Lucas Weber | Fabian Küch

Recent work shows that post-training datasets for LLMs can be substantially downsampled without noticeably deteriorating performance. However, data selection often incurs high computational costs or is limited to narrow domains. In this paper, we demonstrate that data selection can be both—efficient and universal—by using a multi-step pipeline in which we efficiently bin data points into groups, estimate quality using specialized models, and score difficulty with a robust, lightweight method. Task-based categorization allows us to control the composition of our final data—crucial for finetuning multi-purpose models. To guarantee diversity, we improve upon previous work using embedding models and a clustering algorithm. This integrated strategy enables high-performance fine-tuning with minimal overhead.

Chain-of-Thought (CoT) distillation has emerged as a promising paradigm to enhance the reasoning ability of small language models by imitating the reasoning and outputs of larger teacher models. However, existing approaches suffer from a critical limitation: a distribution mismatch between teacher-generated training trajectories and the student model’s own generative distribution. This mismatch leads to exposure bias during inference and often induces mode collapse or mode averaging, thereby degrading the student model’s generative diversity and robustness. To address these issues, we propose CoTD-PO (Chain-of-Thought Distillation with Preference Optimization), a reinforcement learning framework that shifts the training paradigm from passive imitation to active trajectory exploration. Instead of forcing the student to imitate exact teacher traces, our method enables the student to sample its own answer paths. To support training with non-open-source teacher models, we approximate the teacher’s output distribution through preference-based scoring. Furthermore, we adopt an offline iterative training procedure that enables stable and efficient optimization. Experiments on diverse open-ended generation tasks demonstrate that CoTD-PO significantly outperforms standard CoT distillation baselines, achieving higher output quality while mitigating mode collapse and preserving semantic diversity.

In the daily work, vast amounts of documents are stored in pixel-based formats such as images and scanned PDFs, posing challenges for efficient database management and data processing. Existing methods often fragment the parsing process into the pipeline of separated subtasks on the layout element level, resulting in incomplete semantics and error propagation. Even though models based on multi-modal large language models (MLLMs) mitigate the issues to some extent, they also suffer from absent or sub-optimal grounding ability for visual information. To address these challenges, we introduce the Intelligent Document Parsing (IDP) framework, an end-to-end document parsing framework leveraging the vision-language priors of MLLMs, equipped with an elaborately designed document representation and decoding mechanism to decouple the content parsing and layout grounding to fully activate the potential of MLLMs for document parsing. Experimental results demonstrate that the IDP method surpasses existing methods, significantly advancing MLLM-based document parsing.

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Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions
Xiaoyi Wang | Jiwei Zhang | Guangtao Zhang | Honglei Guo

Synthetic therapy dialogues generated by large language models (LLMs) are increasingly used in mental health NLP to simulate counseling scenarios, train models, and supplement limited real-world data. However, it remains unclear whether these synthetic conversations capture the nuanced emotional dynamics of real therapy. In this work, we introduce RealCBT, a dataset of authentic cognitive behavioral therapy (CBT) dialogues, and conduct the first comparative analysis of emotional arcs between real and LLM-generated CBT sessions. We adapt the Utterance Emotion Dynamics framework to analyze fine-grained affective trajectories across valence, arousal, and dominance dimensions. Our analysis spans both full dialogues and individual speaker roles (counselor and client), using real sessions from the RealCBT dataset and synthetic dialogues from the CACTUS dataset. We find that while synthetic dialogues are fluent and structurally coherent, they diverge from real conversations in key emotional properties: real sessions exhibit greater emotional variability, more emotion-laden language, and more authentic patterns of reactivity and regulation. Moreover, emotional arc similarity remains low across all pairings, with especially weak alignment between real and synthetic speakers. These findings underscore the limitations of current LLM-generated therapy data and highlight the importance of emotional fidelity in mental health applications. To support future research, our dataset RealCBT is released at https://gitlab.com/xiaoyi.wang/realcbt-dataset.

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Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models
Sangmin Song | Juhwan Choi | JungMin Yun | YoungBin Kim

Large language models (LLMs) have demonstrated remarkable performance in zero-shot dialogue state tracking (DST), reducing the need for task-specific training. However, conventional DST benchmarks primarily focus on structured user-agent conversations, failing to capture the complexities of real-world multi-user interactions. In this study, we assess the robustness of LLMs in multi-user DST while minimizing dataset construction costs. Inspired by recent advances in LLM-based data annotation, we extend an existing DST dataset by generating utterances of a second user based on speech act theory. Our methodology systematically incorporates a second user’s utterances into conversations, enabling a controlled evaluation of LLMs in multi-user settings. Experimental results reveal a significant performance drop compared to single-user DST, highlighting the limitations of current LLMs in extracting and tracking dialogue states amidst multiple speakers. Our findings emphasize the need for future research to enhance LLMs for multi-user DST scenarios, paving the way for more realistic and robust DST models.

We introduce MAIA (Multimodal AI Assessment), a native-Italian benchmark designed for fine-grained investigation of the reasoning abilities of visual language models on videos. MAIA differs from other available video benchmarks for its design, its reasoning categories, the metric it uses, and the language and culture of the videos. MAIA evaluates Vision Language Models (VLMs) on two aligned tasks: a visual statement verification task, and an open-ended visual question-answering task, both on the same set of video-related questions. It considers twelve reasoning categories that aim to disentangle language and vision relations by highlighting the role of the visual input. Thanks to its carefully taught design, it evaluates VLMs’ consistency and visually grounded natural language comprehension and generation simultaneously through an aggregated metric revealing low results that highlight models’ fragility. Last but not least, the video collection has been carefully selected to reflect the Italian culture, and the language data are produced by native-speakers.Data available at *[GitHub](https://github.com/Caput97/MAIA-Multimodal_AI_Assessment.git).*

We examine three evaluation paradigms: standard benchmarks (e.g., MMLU and BBH), interactive games (e.g., Signalling Games or Taboo), and cognitive tests (e.g., for working memory or theory of mind). First, we investigate which of the former two—benchmarks or games—is most effective at discriminating LLMs of varying quality. Then, inspired by human cognitive assessments, we compile a suite of targeted tests that measure cognitive abilities deemed essential for effective language use, and we investigate their correlation with model performance in benchmarks and games. Our analyses reveal that interactive games are superior to standard benchmarks in discriminating models. Causal and logical reasoning correlate with both static and interactive tests, while differences emerge regarding core executive functions and social/emotional skills, which correlate more with games. We advocate for the development of new interactive benchmarks and targeted cognitive tasks inspired by assessing human abilities but designed specifically for LLMs.

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Entity Profile Generation and Reasoning with LLMs for Entity Alignment
Rumana Ferdous Munne | Md Mostafizur Rahman | Yuji Matsumoto

Entity alignment (EA) involves identifying and linking equivalent entities across different knowledge graphs (KGs). While knowledge graphs provide structured information about real-world entities, only a small fraction of these entities are aligned. The entity alignment process is challenging due to heterogeneity in KGs, such as differences in structure, terminology, and attribute details. Traditional EA methods use multi-aspect entity embeddings to align entities. Although these methods perform well in certain scenarios, their effective- ness is often constrained by sparse or incomplete data in knowledge graphs and the limitations of embedding techniques. We propose ProLEA ( Profile Generation and Reasoning with LLMs for Entity Alignment) an entity alignment method that combines large language models (LLMs) with entity embed- dings. LLMs generate contextual profiles for entities based on their properties. Candidate entities identified by entity embedding techniques are then re-evaluated by the LLMs, using its background knowledge and the generated profile. A thresholding mechanism is introduced to resolve conflicts between LLMs predictions and embedding-based alignments. This method enhances alignment accuracy, robustness, and explainability, particularly for complex, het- erogeneous knowledge graphs. Furthermore, ProLEA is a generalized framework. Its profile generation and LLM-enhanced entity align- ment components can improve the performance of existing entity alignment models.

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Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
Frederic Kirstein | Sonu Kumar | Terry Ruas | Bela Gipp

Meeting summarization with large language models (LLMs) remains error-prone, often producing outputs with hallucinations, omissions, and irrelevancies. We present FRAME, a modular pipeline that reframes summarization as a semantic enrichment task. FRAME extracts and scores salient facts, organizes them thematically, and uses these to enrich an outline into an abstractive summary. To personalize summaries, we introduce SCOPE, a reason-out-loud protocol that has the model build a reasoning trace by answering nine questions before content selection. For evaluation, we propose P-MESA, a multi-dimensional, reference-free evaluation framework to assess if a summary fits a target reader. P-MESA reliably identifies error instances, achieving ≥ 89% balanced accuracy against human annotations and strongly aligned with human severity ratings (𝜌 ≥ 0.70). On QMSum and FAME, FRAME reduces hallucination and omission by 2 out of 5 points (measured with MESA), while SCOPE improves knowledge fit and goal alignment over prompt-only baselines. Our findings advocate for rethinking summarization to improve control, faithfulness, and personalization.

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Attack as Defense: Safeguarding Large Vision-Language Models from Jailbreaking by Adversarial Attacks
Chongxin Li | Hanzhang Wang | Yuchun Fang

Adversarial vulnerabilities in vision-language models pose a critical challenge to the reliability of large language systems, where typographic manipulations and adversarial perturbations can effectively bypass language model defenses. We introduce Attack as Defense (AsD), the first approach to proactively defend at the cross-modality level, embedding protective perturbations in vision to disrupt attacks before they propagate to the language model. By leveraging the semantic alignment between vision and language, AsD enhances adversarial robustness through model perturbations and system-level prompting. Unlike prior work that focuses on text-stage defenses, our method integrates visual defenses to reinforce prompt-based protections, mitigating jailbreaking attacks across benchmarks. Experiments on the LLaVA-1.5 show that AsD reduces attack success rates from 56.7% to 12.6% for typographic attacks and from 89.0% to 47.5% for adversarial perturbations. Further analysis reveals that the key bottleneck in vision-language security lies not in isolated model vulnerabilities, but in cross-modal interactions, where adversarial cues in the vision model fail to consistently activate the defense mechanisms of the language model.

Automatic open-domain dialogue evaluation has attracted increasing attention, yet remains challenging due to the complexity of assessing response appropriateness. Traditional evaluation metrics, typically trained with true positive and randomly selected negative responses, tend to assign higher scores to responses that share greater content similarity with contexts. However, adversarial negative responses, despite possessing high lexical overlap with contexts, can be semantically incongruous. Consequently, existing metrics struggle to evaluate such responses effectively, resulting in low correlations with human judgments. While recent studies have demonstrated the effectiveness of Large Language Models (LLMs) for open-domain dialogue evaluation, they still face challenges in handling adversarial negative examples. We propose a novel evaluation framework that integrates Abstract Meaning Representation (AMR) enhanced domain-specific language models (SLMs) with LLMs. Our SLMs explicitly incorporate AMR graph information through a gating mechanism for enhanced semantic representation learning, while both SLM predictions and AMR knowledge are integrated into LLM prompts for robust evaluation. Extensive experiments on open-domain dialogue evaluation tasks demonstrate the superiority of our method compared to state-of-the-art baselines, particularly in discriminating adversarial negative responses. Our framework achieves strong correlations with human judgments across multiple datasets, establishing a new benchmark for dialogue evaluation. Our code and data are publicly available at https://github.com/Bernard-Yang/SIMAMR.

Multimodal Knowledge Graphs (MMKGs) enhance knowledge representations by integrating structural and multimodal information of entities. Recently, MMKGs have proven effective in tasks such as information retrieval, knowledge discovery, and question answering. Current methods typically utilize pre-trained visual encoders to extract features from images associated with each entity, emphasizing complex cross-modal interactions. However, these approaches often overlook the varying relevance of visual information across entities. Specifically, not all entities benefit from visual data, and not all associated images are pertinent, with irrelevant images introducing noise and potentially degrading model performance. To address these issues, we propose the Differentiated Vision for Multimodal Knowledge Graphs (DVMKG) model. DVMKG evaluates the necessity of visual modality for each entity based on its intrinsic attributes and assesses image quality through representativeness and diversity. Leveraging these metrics, DVMKG dynamically adjusts the influence of visual data during feature integration, tailoring it to the specific needs of different entity types. Extensive experiments on multiple benchmark datasets confirm the effectiveness of DVMKG, demonstrating significant improvements over existing methods.

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Post Persona Alignment for Multi-Session Dialogue Generation
Yi-Pei Chen | Noriki Nishida | Hideki Nakayama | Yuji Matsumoto

Multi-session persona-based dialogue generation presents challenges in maintaining long-term consistency and generating diverse, personalized responses. While large language models (LLMs) excel in single-session dialogues, they struggle to preserve persona fidelity and conversational coherence across extended interactions. Existing methods typically retrieve persona information before response generation, which can constrain diversity and result in generic outputs. We propose Post Persona Alignment (PPA), a novel two-stage framework that reverses this process. PPA first generates a general response based solely on dialogue context, then retrieves relevant persona memories using the response as a query, and finally refines the response to align with the speaker’s persona. This post-hoc alignment strategy promotes naturalness and diversity while preserving consistency and personalization. Experiments on multi-session LLM-generated dialogue data demonstrate that PPA significantly outperforms prior approaches in consistency, diversity, and persona relevance, offering a more flexible and effective paradigm for long-term personalized dialogue generation.

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MASSIVE-Agents: A Benchmark for Multilingual Function-Calling in 52 Languages
Mayank Kulkarni | Vittorio Mazzia | Judith Gaspers | Chris Hench | Jack FitzGerald

We present MASSIVE-Agents, a new benchmark for assessing multilingual function calling across 52 languages. We created MASSIVE-Agents by cleaning the original MASSIVE dataset and then reformatting it for evaluation within the Berkeley Function-Calling Leaderboard (BFCL) framework. The full benchmark comprises 47,020 samples with an average of 904 samples per language, covering 55 different functions and 286 arguments. We benchmarked 21 models using Amazon Bedrock and present the results along with associated analyses. MASSIVE-Agents is challenging, with the top model Nova Premier achieving an average Abstract Syntax Tree (AST) Accuracy of 34.05% across all languages, with performance varying significantly from 57.37% for English to as low as 6.81% for Amharic. Some models, particularly smaller ones, yielded a score of zero for the more difficult languages. Additionally, we provide results from ablations using a custom 1-shot prompt, ablations with prompts translated into different languages, and comparisons based on model latency.

Large Language Models (LLMs) demonstrate remarkable ability to comprehend instructions and generate human-like text, enabling sophisticated agent simulation beyond basic behavior replication. However, the potential for creating freely customisable characters remains underexplored. We introduce the Customisable Conversation Agent Framework, which employs LLMs to simulate real-world characters through personalised characteristic feature injection, enabling diverse character creation according to user preferences.We propose the SimsConv dataset, comprising 68 customised characters and 13,971 multi-turn role-playing dialogues across 1,360 real-world scenes. Characters are initially customised using pre-defined elements (career, aspiration, traits, skills), then expanded through personal and social profiles. Building on this, we present SimsChat, a freely customisable role-playing agent incorporating various realistic settings and topic-specified character interactions.Experimental results on both SimsConv and WikiRoleEval datasets demonstrate SimsChat’s superior performance in maintaining character consistency, knowledge accuracy, and appropriate question rejection compared to existing models. Comprehensive ablation studies validate each component’s contribution to overall performance, with the pre-defined aspects framework and scene construction showing particularly significant impact. Our framework provides valuable insights for developing more accurate and customisable human simulacra.Our data and code are publicly available at https://github.com/Bernard-Yang/SimsChat.

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Can LLMs Express Personality Across Cultures? Introducing CulturalPersonas for Evaluating Trait Alignment
Priyanka Dey | Aayush Bothra | Yugal Khanter | Jieyu Zhao | Emilio Ferrara

As LLMs become central to interactive applications, ranging from tutoring to mental health, the ability to express personality in culturally appropriate ways is increasingly important. While recent works have explored personality evaluation of LLMs, they largely overlook the interplay between culture and personality. To address this, we introduce , the first large-scale benchmark with human validation for evaluating LLMs’ personality expression in culturally grounded, behaviorally rich contexts. Our dataset spans 3,000 scenario-based questions across six diverse countries, designed to elicit personality through everyday scenarios rooted in local values. We evaluate how closely three models’ personality distributions align to real human populations through two evaluation settings: multiple-choice and open-ended response formats. Our results show– improves alignment with country-specific human personality distributions (over a 20% reduction in Wasserstein distance across models and countries) and elicits more expressive, culturally coherent outputs compared to existing benchmarks. surfaces meaningful modulate trait outputs in response to culturally grounded prompts, offering new directions for aligning LLMs to global norms of behavior. By bridging personality expression and cultural nuance, we envision that will pave the way for more socially intelligent and globally adaptive LLMs. Datasets and code are available at: https://github.com/limenlp/CulturalPersonas.

Large language models (LLMs) have been able to perform various forms of reasoning tasks ina wide range of scenarios, but are they truly engaging in task abstraction and rule-based reasoning beyond mere memorization? To answer this question, we propose a novel experimentalapproach, Misleading Fine-Tuning (MisFT), to examine whether LLMs perform abstract reasoning by altering their original understanding of fundamental rules. In particular, by constructing datasets with math expressions or logical formulas that contradict correct principles, we fine-tune the model to learn those contradictory rules and assess its generalization ability on unseen test domains. Through a series of experiments, we find that current LLMs are capable of applying contradictory rules to solve practical math word problems and natural language reasoning tasks, implying the presence of an internal mechanism in LLMs that abstracts before reasoning.

Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. However, the extent to which LRMs can think in other languages is less studied. This is as important as answer accuracy for real-world applications since users may find the thinking trace useful for oversight only if expressed in their languages. In this work, we comprehensively evaluate two leading families of LRMs on our established benchmark XReasoning. Surprisingly, even the most advanced models often revert to English or produce fragmented reasoning in other languages, revealing a substantial gap in the capability of thinking in non-English languages. Promoting models to reason in the user’s language via prompt hacking enhances readability and oversight. This could gain user trust, but reduces answer accuracy, exposing an important trade-off. We further demonstrate that targeted post-training, even with just 100 instances, can mitigate this language mismatch, although accuracy is still degraded. Our results reveal the limited multilingual reasoning capabilities of current LRMs and suggest directions for future research. All code and datasets are released at https://github.com/Betswish/mCoT-XReasoning.

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The Role of Model Confidence on Bias Effects in Measured Uncertainties for Vision-Language Models
Xinyi Liu | Weiguang Wang | Hangfeng He

With the growing adoption of Large Language Models (LLMs) for open-ended tasks, accurately assessing epistemic uncertainty, which reflects a model’s lack of knowledge, has become crucial to ensuring reliable outcomes. However, quantifying epistemic uncertainty in such tasks is challenging due to the presence of aleatoric uncertainty, which arises from multiple valid answers. While bias can introduce noise into epistemic uncertainty estimation, it may also reduce noise from aleatoric uncertainty. To investigate this trade-off, we conduct experiments on Visual Question Answering (VQA) tasks and find that mitigating prompt-introduced bias improves uncertainty quantification in GPT-4o. Building on prior work showing that LLMs tend to copy input information when model confidence is low, we further analyze how these prompt biases affect measured epistemic and aleatoric uncertainty across varying bias-free confidence levels with GPT-4o and Qwen2-VL. We find that all considered biases have greater effects in both uncertainties when bias-free model confidence is lower. Moreover, lower bias-free model confidence is associated with greater bias-induced underestimation of epistemic uncertainty, resulting in overconfident estimates, whereas it has no significant effect on the direction of bias effect in aleatoric uncertainty estimation. These distinct effects deepen our understanding of bias mitigation for uncertainty quantification and potentially inform the development of more advanced techniques.

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GAttention: Gated Attention for the Detection of Abusive Language
Horacio Jarquín Vásquez | Hugo Jair Escalante | Manuel Montes | Mario Ezra Aragon

Abusive language online creates toxic environments and exacerbates social tensions, underscoring the need for robust NLP models to interpret nuanced linguistic cues. This paper introduces GAttention, a novel Gated Attention mechanism that combines the strengths of Contextual attention and Self-attention mechanisms to address the limitations of existing attention models within the text classification task. GAttention capitalizes on local and global query vectors by integrating the internal relationships within a sequence (Self-attention) and the global relationships among distinct sequences (Contextual attention). This combination allows for a more nuanced understanding and processing of sequence elements, which is particularly beneficial in context-sensitive text classification tasks such as the case of abusive language detection. By applying this mechanism to transformer-based encoder models, we showcase how it enhances the model’s ability to discern subtle nuances and contextual clues essential for identifying abusive language, a challenging and increasingly relevant NLP task.

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Towards Low-Resource Alignment to Diverse Perspectives with Sparse Feedback
Chu Fei Luo | Samuel Dahan | Xiaodan Zhu

As language models have a greater impact on society, it is important to ensure they are aligned to a diverse range of perspectives and are able to reflect nuance in human values. However, the most popular training paradigms for modern language models often assume there is one optimal answer for every query, leading to generic responses and poor alignment. In this work, we aim to enhance pluralistic alignment of language models in a low-resource setting with two methods: pluralistic decoding and model steering. We empirically demonstrate that model steering offers consistent improvement over zero-shot and few-shot baselines with only 50 annotated samples. Our proposed methods decrease false positives in several high-stakes tasks such as hate speech detection and misinformation detection, and improves the distributional alignment to human values from different demographics. We hope our work highlights the importance of diversity and how language models can be adapted to consider nuanced perspectives.

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ProtoXTM: Cross-Lingual Topic Modeling with Document-Level Prototype-based Contrastive Learning
Seung-Won Seo | Soon-Sun Kwon

Cross-lingual topic modeling (CLTM) is an essential task in the field of data mining and natural language processing, aiming to extract aligned and semantically coherent topics from bilingual corpora. Recent advances in cross-lingual neural topic models have widely leveraged bilingual dictionaries to achieve word-level topic alignment. However, two critical challenges remain in cross-lingual topic modeling, the topic mismatch issue and the degeneration of intra-lingual topic interpretability. Due to linguistic diversity, some translated word pairs may not represent semantically coherent topics despite being lexical equivalents, and the objective of cross-lingual topic alignment in CLTM can consequently degrade topic interpretability within intra languages. To address these issues, we propose a novel document-level prototype-based contrastive learning paradigm for cross-lingual topic modeling. Additionally, we design a retrieval-based positive sampling strategy for contrastive learning without data augmentation. Furthermore, we introduce ProtoXTM, a cross-lingual neural topic model based on document-level prototype-based contrastive learning. Extensive experiments indicate that our approach achieves state-of-the-art performance on cross-lingual and mono-lingual benchmarks, demonstrating enhanced topic interpretability.

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One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning
Mengyu Wang | Sotirios Sabanis | Miguel de Carvalho | Shay B Cohen | Tiejun Ma

Domain-specific quantitative reasoning remains a major challenge for large language models (LLMs), especially in fields requiring expert knowledge and complex question answering (QA). In this work, we propose Expert Question Decomposition (EQD), an approach designed to balance the use of domain knowledge with computational efficiency. EQD is built on a two-step fine-tuning framework and guided by a reward function that measures the effectiveness of generated sub-questions in improving QA outcomes. It requires only a few thousand training examples and a single A100 GPU for fine-tuning, with inference time comparable to zero-shot prompting. Beyond its efficiency, EQD outperforms state-of-the-art domain-tuned models and advanced prompting strategies. We evaluate EQD in the financial domain, characterized by specialized knowledge and complex quantitative reasoning, across four benchmark datasets. Our method consistently improves QA performance by 0.6% to 10.5% across different LLMs. Our analysis reveals an important insight: in domain-specific QA, a single supporting question often provides greater benefit than detailed guidance steps.

Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of 4 methods for improving prompt robustness within a unified experimental framework. We benchmark these techniques on 8 models from Llama, Qwen and Gemma families across 52 tasks from Natural Instructions dataset. Our evaluation covers robustness methods from both fine-tuned and in-context learning paradigms, and tests their generalization against multiple types of distribution shifts. Finally, we extend our analysis to GPT-4.1 and DeepSeek V3 to assess frontier models’ current robustness to format perturbations. Our findings offer actionable insights into the relative effectiveness of these robustness methods, enabling practitioners to make informed decisions when aiming for stable and reliable LLM performance in real-world applications. Code: tthttps://github.com/AIRI-Institute/when-punctuation-matters.

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RAR²: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrieval
Kaishuai Xu | Wenjun Hou | Yi Cheng | Wenjie Li

Large Language Models (LLMs) have shown promising performance on diverse medical benchmarks, highlighting their potential in supporting real-world clinical tasks. Retrieval-Augmented Generation (RAG) has emerged as a key approach for mitigating knowledge gaps and hallucinations by incorporating external medical information. However, RAG still struggles with complex medical questions that require intensive reasoning, as surface-level input often fails to reflect the true knowledge needs of the task. Existing methods typically focus on refining queries without explicitly modeling the reasoning process, limiting their ability to retrieve and integrate clinically relevant knowledge. In this work, we propose RAR², a joint learning framework that improves both Reasoning-Augmented Retrieval and Retrieval-Augmented Reasoning. RAR² constructs a thought process to uncover implicit knowledge requirements and uses it to guide retrieval and answer generation. We build a training dataset of mixed preference pairs and apply Direct Preference Optimization (DPO) to train the model. Moreover, we design two test-time scaling strategies to explore the boundaries of our framework. Experiments demonstrate the effectiveness of RAR² across several biomedical question answering datasets, outperforming RAG baselines with or without fine-tuning.

The choice of a suitable visual language projector (VLP) is critical to the successful training of large visual language models (LVLMs). Mainstream VLPs can be broadly categorized into compressed and uncompressed projectors, and each offers distinct advantages in performance and computational efficiency. However, their security implications have not been thoroughly examined. Our comprehensive evaluation reveals significant differences in their security profiles: compressed projectors exhibit substantial vulnerabilities, allowing adversaries to successfully compromise LVLMs even with minimal knowledge of structure information. In stark contrast, uncompressed projectors demonstrate robust security properties and do not introduce additional vulnerabilities. These findings provide critical guidance for researchers in selecting optimal VLPs that enhance the security and reliability of visual language models. The code is available at https://github.com/btzyd/TCP.

We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.

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Radical Allomorphy: Phonological Surface Forms without Phonology
Salam Khalifa | Nizar Habash | Owen Rambow

Recent computational work typically frames morphophonology as generating surface forms (SFs) from abstract underlying representations (URs) by applying phonological rules or constraints. This generative stance presupposes that every morpheme has a well-defined UR from which all allomorphs can be derived, a theory-laden assumption that is expensive to annotate, especially in low-resource settings.We adopt an alternative view. Allomorphs and their phonological variants are treated as the basic, observed lexicon, not as outputs of abstract URs. The modeling task therefore shifts from deriving SFs to selecting the correct SF, given a meaning and a phonological context. This discriminative formulation removes the need to posit or label URs and lets the model exploit the surface evidence directly.

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Model Calibration for Emotion Detection
Mihaela Petre-Vlad | Cornelia Caragea | Florentina Hristea

In this paper, we propose a unified approach to model calibration for emotion detection that exploits the complementary strengths of knowledge distillation and the MixUp data augmentation technique to enhance the trustworthiness of emotion detection models. Specifically, we use a MixUp method informed by training dynamics that generates augmented data by interpolating easy-to-learn with ambiguous samples based on their similarity and dissimilarity provided by saliency maps. We use this MixUp method to calibrate the teacher model in the first generation of the knowledge distillation process. To further calibrate the teacher models in each generation, we employ dynamic temperature scaling to update the temperature used for scaling the teacher predictions. We find that calibrating the teachers with our method also improves the calibration of the student models. We test our proposed method both in-distribution (ID) and out-of-distribution (OOD). To obtain better OOD performance, we further fine-tune our models with a simple MixUp method that interpolates a small number of OOD samples with ambiguous ID samples.

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From Benchmark to Better Embeddings: Leveraging Synonym Substitution to Enhance Multimodal Models in Ukrainian
Volodymyr Mudryi | Yurii Laba

We study the robustness of text–image retrieval for Ukrainian under synonym-substitution attacks (SSA). On Multi30K with OpenCLIP, we evaluate two SSA methods: dictionary-based and LLM-based, and find Ukrainian degrades far more than English (e.g., GPT-4o SSA drops HIT@1 from 32.1 → 10.9 vs. 41.6 → 30.4). We introduce a Hybrid method that filters dictionary candidates with an LLM to preserve sense and grammar, yielding higher-quality perturbations (Ukrainian HIT@1 16.8 vs. 7.6/10.9). To mitigate this problem, we propose synonym-augmented fine-tuning, injecting one-word substitutions into training; it boosts robustness (Hybrid 28.1, GPT-4o 25.1) without harming original performance. This is the first systematic SSA evaluation for Ukrainian multimodal retrieval and a practical recipe for improving models in low-resource, morphologically rich languages. We release code, prompts, and trained checkpoints at https://github.com/YuriiLaba/UA-B2BE.

The behavior of Large Language Models (LLMs) when facing contextual information that conflicts with their internal parametric knowledge is inconsistent, with no generally accepted explanation for the expected outcome distribution. Recent work has identified in autoregressive transformer models a class of neurons – called entropy neurons – that produce a significant effect on the model output entropy while having an overall moderate impact on the ranking of the predicted tokens. In this paper, we investigate the preliminary claim that these neurons are involved in inhibiting context copying behavior in transformers by looking at their role in resolving conflicts between contextual and parametric information. We show that entropy neurons are responsible for suppressing context copying across a range of LLMs, and that ablating them leads to a significant change in the generation process. These results enhance our understanding of the internal dynamics of LLMs when handling conflicting information.

Rhetorical strategies are central to persuasive communication, from political discourse and marketing to legal argumentation. However, analysis of rhetorical strategies has been limited by reliance on human annotation, which is costly, inconsistent, difficult to scale. Their associated datasets are often limited to specific topics and strategies, posing challenges for robust model development. We propose a novel framework that leverages large language models (LLMs) to automatically generate and label synthetic debate data based on a four-part rhetorical typology (causal, empirical, emotional, moral). We fine-tune transformer-based classifiers on this LLM-labeled dataset and validate its performance against human-labeled data on this dataset and on multiple external corpora. Our model achieves high performance and strong generalization across topical domains. We illustrate two applications with the fine-tuned model: (1) the improvement in persuasiveness prediction from incorporating rhetorical strategy labels, and (2) analyzing temporal and partisan shifts in rhetorical strategies in U.S. Presidential debates (1960–2020), revealing increased use of affective over cognitive argument in U.S. Presidential debates.

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SecDecoding: Steerable Decoding for Safer LLM Generation
Jiayou Wang | Rundong Liu | Yue Hu | Huijia Wu | Zhaofeng He

Large language models (LLMs) have achieved remarkable performance across diverse tasks, yet ensuring output safety remains a fundamental challenge. Existing defense methods often suffer from limited generalization, high computational overhead, or significant utility degradation. In this work, we present SecDecoding, a lightweight decoding-time defense framework that significantly improves output safety without compromising model helpfulness. SecDecoding leverages a pair of small contrastive models, namely a base model and a safety fine-tuned expert, to estimate token-level safety signals by measuring divergence in their output distributions. These signals dynamically steer the target model’s generation toward safer trajectories, effectively suppressing unsafe content. Experimental results show that SecDecoding achieves near-zero attack success rates against a wide spectrum of advanced jailbreak attacks across multiple LLMs, while maintaining the model’s helpfulness with minimal degradation. Additionally, SecDecoding is a modular and resource-efficient approach that requires only an auxiliary 1-billion-parameter model and is compatible with speculative decoding, offering up to 1.5× inference speedup.

Uncertainty estimation is essential for enhancing the reliability of Large Language Models (LLMs), particularly in high-stakes applications. Existing methods often overlook semantic dependencies, relying on token-level probability measures that fail to capture structural relationships within the generated text. We propose GENUINE: Graph ENhanced mUlti-level uncertaINty Estimation for Large Language Models, a structure-aware framework that leverages dependency parse trees and hierarchical graph pooling to refine uncertainty quantification. By incorporating supervised learning, GENUINE effectively models semantic and structural relationships, improving confidence assessments. Extensive experiments across NLP tasks show that GENUINE achieves up to 29% higher AUROC than semantic entropy-based approaches and reduces calibration errors by over 15%, demonstrating the effectiveness of graph-based uncertainty modeling. The code is available at https://github.com/ODYSSEYWT/GUQ.

The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. In this work, we propose: (1) ReviewEval, a comprehensive evaluation framework for AI-generated reviews that measures alignment with human assessments, verifies factual accuracy, assesses analytical depth, identifies degree of constructiveness and adherence to reviewer guidelines; and (2) ReviewAgent, an LLM-based review generation agent featuring a novel alignment mechanism to tailor feedback to target conferences and journals, along with a self-refinement loop that iteratively optimizes its intermediate outputs and an external improvement loop using ReviewEval to improve upon the final reviews. ReviewAgent improves actionable insights by 6.78% and 47.62% over existing AI baselines and expert reviews respectively. Further, it boosts analytical depth by 3.97% and 12.73%, enhances adherence to guidelines by 10.11% and 47.26% respectively. This paper establishes essential metrics for AI-based peer review and substantially enhances the reliability and impact of AI-generated reviews in academic research.

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Overcoming Black-box Attack Inefficiency with Hybrid and Dynamic Select Algorithms
Abhinay Shankar Belde | Rohit Ramkumar | Jonathan Rusert

Adversarial text attack research plays a crucial role in evaluating the robustness of NLP models. However, the increasing complexity of transformer-based architectures has dramatically raised the computational cost of attack testing, especially for researchers with limited resources (e.g., GPUs). Existing popular black-box attack methods often require a large number of queries, which can make them inefficient and impractical for researchers. To address these challenges, we propose two new attack selection strategies called Hybrid and Dynamic Select, which better combine the strengths of previous selection algorithms. Hybrid Select merges generalized BinarySelect techniques with GreedySelect by introducing a size threshold to decide which selection algorithm to use. Dynamic Select provides an alternative approach of combining the generalized Binary and GreedySelect by learning which lengths of texts each selection method should be applied to. This greatly reduces the number of queries needed while maintaining attack effectiveness (a limitation of BinarySelect). Across 4 datasets and 6 target models, our best method(sentence-level Hybrid Select) is able to reduce the number of required queries per attack up 25.82% on average against both encoder models and LLMs, without losing the effectiveness of the attack.

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GmSLM : Generative Marmoset Spoken Language Modeling
Talia Sternberg | Michael London | David Omer | Yossi Adi

Marmoset monkeys exhibit complex vocal communication, challenging the view that nonhuman primates’ vocal communication is entirely innate, and show similar features of human speech, such as vocal labeling of others and turn-taking. Studying their vocal communication offers a unique opportunity to link it with brain activity—especially given the difficulty of accessing the human brain in speech and language research. Since Marmosets communicate primarily through vocalizations, applying standard LLM approaches is not straightforward. We introduce Generative Marmoset Spoken Language Modeling (GmSLM), an optimized spoken language model pipeline for Marmoset vocal communication. We designed a novel zero-shot evaluation metrics using unsupervised in-the-wild data, alongside weakly labeled conversational data, to assess GmSLM and demonstrate its advantage over a basic human-speech-based baseline. GmSLM generated vocalizations closely matched real resynthesized samples acoustically and performed well on downstream tasks. Despite being fully unsupervised, GmSLM effectively distinguish real from artificial conversations and may support further investigations of the neural basis of vocal communication and provides a practical framework linking vocalization and brain activity. We believe GmSLM stands to benefit future work in neuroscience, bioacoustics, and evolutionary biology. Samples are provided under: https://pages.cs.huji.ac.il/adiyoss-lab/GmSLM/.

Alignment of large language models (LLMs) with principles like helpfulness, honesty, and harmlessness typically relies on scalar rewards that obscure which objectives drive the training signal. We introduce QA-LIGN, which decomposes monolithic rewards into interpretable principle-specific evaluations through structured natural language programs. Models learn through a draft, critique, and revise pipeline, where symbolic evaluation against the rubrics provides transparent feedback for both initial and revised responses during GRPO training. Applied to uncensored Llama-3.1-8B-Instruct, QA-LIGN reduces attack success rates by up to 68.7% while maintaining a 0.67% false refusal rate, achieving Pareto optimal safety-helpfulness performance and outperforming both DPO and GRPO with state-of-the-art reward models given equivalent training. These results demonstrate that making reward signals interpretable and modular improves alignment effectiveness, suggesting transparency enhances LLM safety.

Large Language Models (LLMs) are widely used for a variety of tasks such as text generation, ranking, and decision-making. However, their outputs can be influenced by various forms of biases. One such bias is positional bias, where models prioritize items based on their position within a given prompt rather than their content or quality, impacting on how LLMs interpret and weigh information, potentially compromising fairness, reliability, and robustness. To assess positional bias, we prompt a range of LLMs to generate descriptions for a list of topics, systematically permuting their order and analyzing variations in the responses. Our analysis shows that ranking position affects structural features and coherence, with some LLMs also reordering or omitting topics. Nonetheless, the impact of positional bias varies across different LLMs and topics, indicating an interplay with other related biases.

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You Only Use Reactive Attention Slice When Retrieving From Long Context
Yun Joon Soh | Hanxian Huang | Yuandong Tian | Jishen Zhao

Retrieval-Augmented Generation is a powerful method for enhancing language models (LMs), but existing retrieval techniques are limited.Embedding-based methods are often inaccurate due to their reliance on lexical similarity, while neural retrievers are computationally expensive to train.To overcome these issues, we introduce You Only Use Reactive Attention slice (YOURA), a training-free and fine-tuning-free attention-based retrieval technique. When retrieving, YOURA uses a novel reaction score heuristic, which quantifies how an LM’s self-attention “reacts” to a user query. We also propose a sentence extraction algorithm to efficiently preprocess the context.Evaluations on three open-source LMs using the LongBench and BABILong datasets show YOURA’s effectiveness. Our framework improves QA task accuracy by up to 15% and inference throughput by up to 31% compared to embedding-based retrieval.

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Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring
Shuxin Lin | Dhaval C Patel | Christodoulos Constantinides

Small Language Models (SLMs) are becoming increasingly popular in specialized fields, such as industrial applications, due to their efficiency, lower computational requirements, and ability to be fine-tuned for domain-specific tasks, enabling accurate and cost-effective solutions. However, performing complex reasoning using SLMs in specialized fields such as Industry 4.0 remains challenging. In this paper, we propose a knowledge distillation framework for industrial asset health, which transfers reasoning capabilities via Chain-of-Thought (CoT) distillation from Large Language Models (LLMs) to smaller, more efficient models (SLMs). We discuss the advantages and the process of distilling LLMs using multi-choice question answering (MCQA) prompts to enhance reasoning and refine decision-making. We also perform in-context learning to verify the quality of the generated knowledge and benchmark the performance of fine-tuned SLMs with generated knowledge against widely used LLMs. The results show that the fine-tuned SLMs with CoT reasoning outperform the base models by a significant margin, narrowing the gap to their LLM counterparts. Our code is open-sourced at: https://github.com/IBM/FailureSensorIQ.

Large Vision-Language Models (LVLMs) process multimodal inputs consisting of text tokens and vision tokens extracted from images or videos. Due to the rich visual information, a single image can generate thousands of vision tokens, leading to high computational costs during the prefilling stage and significant memory overhead during decoding. Existing methods attempt to prune redundant vision tokens, revealing substantial redundancy in visual representations. However, these methods often struggle in shallow layers due to the lack of sufficient contextual information. We argue that many visual tokens are inherently redundant even in shallow layers and can be safely and effectively pruned with appropriate contextual signals. In this work, we propose CoViPAL, a layer-wise contextualized visual token pruning method that employs a Plug-and-Play Pruning Module (PPM) to predict and remove redundant vision tokens before they are processed by the LVLM. The PPM is lightweight, model-agnostic, and operates independently of the LVLM architecture, ensuring seamless integration with various models. Extensive experiments on multiple benchmarks demonstrate that CoViPAL outperforms training-free pruning methods under equal token budgets and surpasses training-based methods with comparable supervision. CoViPAL offers a scalable and efficient solution to improve inference efficiency in LVLMs without compromising accuracy.

Recent advances in large language models (LLMs) have shown potential in clinical text summarization, but their ability to handle long patient trajectories with multi-modal data spread across time remains underexplored. This study systematically evaluates several state-of-the-art open-source LLMs, their Retrieval Augmented Generation (RAG) variants and chain-of-thought (CoT) prompting on long-context clinical summarization and prediction. We examine their ability to synthesize structured and unstructured Electronic Health Records (EHR) data while reasoning over temporal coherence, by re-engineering existing tasks, including discharge summarization and diagnosis prediction from two publicly available EHR datasets. Our results indicate that long context windows improve input integration but do not consistently enhance clinical reasoning, and LLMs are still struggling with temporal progression and rare disease prediction. While RAG shows improvements in hallucination in some cases, it does not fully address these limitations. Our work fills the gap in long clinical text summarization, establishing a foundation for evaluating LLMs with multi-modal data and temporal reasoning.

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TransAlign: Machine Translation Encoders are Strong Word Aligners, Too
Benedikt Ebing | Christian Goldschmied | Goran Glavaš

In the absence of sizable training data for most world languages and NLP tasks, translation-based strategies such as translate-test—evaluating on noisy source language data translated from the target language—and translate-train—training on noisy target language data translated from the source language—have been established as competitive approaches for cross-lingual transfer (XLT). For token classification tasks, these strategies require label projection: mapping the labels from each token in the original sentence to its counterpart(s) in the translation. To this end, it is common to leverage multilingual word aligners (WAs) derived from encoder language models such as mBERT or LaBSE. Despite obvious associations between machine translation (MT) and WA, research on extracting alignments with MT models is largely limited to exploiting cross-attention in encoder-decoder architectures, yielding poor WA results. In this work, in contrast, we propose TransAlign, a novel word aligner that utilizes the encoder of a massively multilingual MT model. We show that TransAlign not only achieves strong WA performance but substantially outperforms popular WA and state-of-the-art non-WA-based label projection methods in MT-based XLT for token classification.

Neural network pruning has emerged as a promising approach for deploying LLMs in low-resource scenarios while preserving downstream task performance. However, for the first time, we reveal that such pruning disrupts LLMs’ internal activation features crucial for lie detection, where probing classifiers (typically small logistic regression models) trained on these features assess the truthfulness of LLM-generated statements. This discovery raises a crucial open question: how can we prune LLMs without sacrificing these critical lie detection capabilities? Our investigation further reveals that naively adjusting layer-wise pruning sparsity based on importance inadvertently removes crucial weights, failing to improve lie detection performance despite its reliance on the most crucial LLM layer. To address this issue, we propose Truthful Pruning aligned by Layer-wise Outliers (TPLO), which places greater emphasis on layers with more activation outliers and stronger discriminative features simultaneously. This preserves LLMs’ original performance while retaining critical features of inner states needed for robust lie detection. Moreover, we introduce a prompting rule to enrich the TruthfulQA benchmark for better calibrating LLM pruning. Empirical results show that our approach improves the hallucination detection for pruned LLMs (achieving 88% accuracy at 50% sparsity) and enhances their performance on TruthfulQA.

Table question answering is a popular task that assesses a model’s ability to understand and interact with structured data. However, the given table often does not contain sufficient information to answer the question, necessitating the integration of external knowledge. Existing methods either convert both the table and external knowledge into text, which neglects the structured nature of the table; or they embed queries for external sources in the interaction with the table, which complicates the process. In this paper, we propose a simple yet effective method to integrate external information in a given table. Our method first constructs an augmenting table containing the missing information and then generates a SQL query over the two tables to answer the question. Experiments show that our method outperforms strong baselines on three table QA benchmarks.

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Evaluating Large Language Models for Belief Inference: Mapping Belief Networks at Scale
Trisevgeni Papakonstantinou | Antonina Zhiteneva | Ana Yutong Ma | Derek Powell | Zachary Horne

Beliefs are interconnected, influencing how people process and update what they think. To study the interconnectedness of beliefs at scale, we introduce a novel analytical pipeline leveraging a finetuned GPT-4o model to infer belief structures from large-scale social media data. We evaluate the model’s performance by (1) comparing it to human annotated data (2) and its inferences to human-generated survey data. Our results show that a fine-tuned GPT-4o model can effectively recover belief structures, allowing for a level of scalability and efficiency that is impossible using traditional survey methods of data collection. This work demonstrates the potential for large language models to perform belief inference tasks and provides a framework for future research on the analysis of belief structures.

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Distinguishing fair from unfair compositional generalization tasks
Ahmad Jabbar | Cleo Condoravdi | Christopher Potts

Compositional generalization benchmarks seek to assess whether learning agents can successfully combine familiar concepts in novel ways. COGS (Kim & Linzen 2020, COGS, EMNLP) provides a suite of such tasks in the area of interpretive semantics (mapping sentences to logical forms). A noteworthy finding for COGS is that model performance varies widely across tasks. In this paper, we argue that these performance differences reflect deep properties of these tasks. We focus on two COGS tasks: an easy task (models are generally successful) and a hard task (no present-day models get any traction). Using both experiments and conceptual analysis, we argue that the easy task requires only a single distributional generalization that is well-supported by the training data, whereas the hard task involves a learning target that is ambiguous or even contradicted by the training data. We additionally argue that pretraining can disambiguate the hard task without compromising the goal of testing compositional generalization. Overall, our findings offer practical guidance to designers of compositional generalization benchmarks and also yield new insights into the nature of compositionality itself.

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SA-CLIP: Language Guided Image Spatial and Action Feature Learning
Guanlin Li | Wenhao Shao | Praboda Rajapaksha | Noel Crespi

We observed that Contrastive Language-Image Pretraining (CLIP) models struggle with real-world downstream tasks such as road traffic anomaly detection, due to their inability to effectively capture spatial and action relationships between objects within images. To address this, we compile and curate a dataset with 1M samples of images using language supervision provided by the common image caption dataset, in which each image is paired with subject-relationship-object descriptions emphasizing spatial and action interactions, and train a Spatial and Action relationship aware CLIP (SA-CLIP) model. We evaluated the proposed model on the Visual Spatial Reasoning (VSR) dataset and further verified its effectiveness on the Detection-of-Traffic-Anomaly (DoTA) dataset. Experiment results show that the proposed SA-CLIP demonstrates strong abilities in understanding spatial relationships while achieving good zero-shot performance on the traffic anomaly detection task.

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Inefficiencies of Meta Agents for Agent Design
Batu El | Mert Yuksekgonul | James Zou

Recent works began to automate the design of agentic systems using meta-agents that propose and iteratively refine new agent architectures. In this paper, we examine three key challenges in a common class of meta-agents. First, we investigate how a meta-agent learns across iterations and find that simply expanding the context with all previous agents, as proposed by previous works, performs worse than ignoring prior designs entirely. We show that the performance improves with an evolutionary approach. Second, although the meta-agent designs multiple agents during training, it typically commits to a single agent at test time. We find that the designed agents have low behavioral diversity, limiting the potential for their complementary use. Third, we assess when automated design is economically viable. We find that only in a few cases—specifically, two datasets—the overall cost of designing and deploying the agents is lower than that of human-designed agents when deployed on over 15,000 examples. In contrast, the performance gains for other datasets do not justify the design cost, regardless of scale.

Existing code large language models (LLMs) often rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs. In this paper, we explore the potential of small-scale open-source LLMs (e.g., 7B) as synthesizers for high-quality code instruction data construction. We first observe that the data synthesis capability of small-scale LLMs can be enhanced by training on a few superior data synthesis samples from proprietary LLMs. Building on this, we propose a novel iterative self-distillation approach to bootstrap small-scale LLMs, transforming them into powerful synthesizers that reduce reliance on proprietary LLMs and minimize costs. Concretely, in each iteration, to obtain diverse and high-quality self-distilled data, we design multi-checkpoint sampling and multi-aspect scoring strategies for initial data selection. Furthermore, to identify the most influential samples, we introduce a gradient-based influence estimation method for final data filtering. Based on the code instruction datasets from the small-scale synthesizers, we develop SCoder, a family of code generation models fine-tuned from DeepSeek-Coder. SCoder models achieve state-of-the-art code generation capabilities, demonstrating the effectiveness of our method.

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Linguistically-Controlled Paraphrase Generation
Mohamed Elgaar | Hadi Amiri

Controlled paraphrase generation produces paraphrases that preserve meaning while allowing precise control over linguistic attributes of the output. We introduce LingConv, an encoder-decoder framework that enables fine-grained control over 40 linguistic attributes in English. To improve reliability, we introduce a novel inference-time quality control mechanism that iteratively refines attribute embeddings to generate paraphrases that closely match target attributes without sacrificing semantic fidelity. LingConv reduces attribute error by up to 34% over existing models, with the quality control mechanism contributing an additional 14% improvement.

Although transformer architectures have achieved state-of-the-art performance across diverse domains, their quadratic computational complexity with respect to sequence length remains a significant bottleneck, particularly for latency-sensitive long-context applications. While recent linear-complexity alternatives are increasingly powerful, effectively training them from scratch is still resource-intensive. To overcome these limitations, we propose LAWCAT (Linear Attention with Convolution Across Time), a novel linearization framework designed to efficiently transfer the capabilities of pretrained transformers into a performant linear attention architecture. LAWCAT integrates causal Conv1D layers to enhance local dependency modeling and employs normalized gated linear attention to improve generalization across varying context lengths. Our comprehensive evaluations demonstrate that, distilling Mistral-7B with only 1K-length sequences yields over 90% passkey retrieval accuracy up to 22K tokens, significantly extending its effective context window. Similarly, Llama3.2-1B LAWCAT variant achieves competitive performance on S-NIAH 1&2&3 tasks (1K-8K context length) and BABILong benchmark (QA2&QA3, 0K-16K context length), requiring less than 0.1% pre-training tokens compared with pre-training models. Furthermore, LAWCAT exhibits faster prefill speeds than FlashAttention-2 for sequences exceeding 8K tokens. LAWCAT thus provides an efficient pathway to high-performance, long-context linear models suitable for edge deployment, reducing reliance on extensive long-sequence training data and computational resources.

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Analyzing Dialectical Biases in LLMs for Knowledge and Reasoning Benchmarks
Eileen Pan | Anna Seo Gyeong Choi | Maartje Ter Hoeve | Skyler Seto | Allison Koenecke

Large language models (LLMs) are ubiquitous in modern day natural language processing. However, previous work has shown degraded LLM performance for under-represented English dialects. We analyze the effects of typifying “standard” American English language questions as non-”standard” dialectal variants on multiple choice question answering tasks and find up to a 20% reduction in accuracy. Additionally, we investigate the grammatical basis of under-performance in non-”standard” English questions. We find that individual grammatical rules have varied effects on performance, but some are more consequential than others: three specific grammar rules (existential “it”, zero copula, and y’all) can explain the majority of performance degradation observed in multiple dialects. We call for future work to investigate bias mitigation methods focused on individual, high-impact grammatical structures.

Inference-time alignment enhances the performance of large language models without requiring additional training or fine-tuning but presents challenges due to balancing computational efficiency with high-quality output. Best-of-N (BoN) sampling, as a simple yet powerful approach, generates multiple responses and selects the best one, achieving improved performance but with a high computational cost. We propose TreeBoN, a novel framework that integrates a speculative tree-search strategy into Best-of-N (BoN) Sampling. TreeBoN maintains a set of parent nodes, iteratively branching and pruning low-quality responses, thereby reducing computational overhead while maintaining high output quality. Our approach also leverages token-level rewards from Direct Preference Optimization (DPO) to guide tree expansion and prune low-quality paths. We evaluate TreeBoN using AlpacaFarm, UltraFeedback, GSM8K, HH-RLHF, and TutorEval datasets, demonstrating consistent improvements. Specifically, TreeBoN achieves a 65% win rate at maximum lengths of 192 and 384 tokens, outperforming standard BoN with the same computational cost. Furthermore, TreeBoN achieves around a 60% win rate across longer responses, showcasing its scalability and alignment efficacy.

The increasing ubiquity of text-to-image (T2I) models as tools for visual content generation raises concerns about their ability to accurately represent diverse cultural contexts - where missed cues can stereotype communities and undermine usability. In this work, we present the first study to systematically quantify the alignment of T2I models and evaluation metrics with respect to both explicit (stated) as well as implicit (unstated, implied by the prompt’s cultural context) cultural expectations. To this end, we introduce CulturalFrames, a novel benchmark designed for rigorous human evaluation of cultural representation in visual generations. Spanning 10 countries and 5 socio-cultural domains, CulturalFrames comprises 983 prompts, 3637 corresponding images generated by 4 state-of-the-art T2I models, and over 10k detailed human annotations. We find that across models and countries, cultural expectations are missed an average of 44% of the time. Among these failures, explicit expectations are missed at a surprisingly high average rate of 68%, while implicit expectation failures are also significant, averaging 49%. Furthermore, we show that existing T2I evaluation metrics correlate poorly with human judgments of cultural alignment, irrespective of their internal reasoning. Collectively, our findings expose critical gaps, provide a concrete testbed, and outline actionable directions for developing culturally informed T2I models and metrics that improve global usability.

Multimodal large language models (MLLMs) have gained significant attention due to their impressive ability to integrate vision and language modalities. Recent advancements in MLLMs have primarily focused on improving performance through high-quality datasets, novel architectures, and optimized training strategies. However, in this paper, we identify a previously overlooked issue, language prior conflict, a mismatch between the inherent language priors of large language models (LLMs) and the language priors in training datasets. This conflict leads to suboptimal vision-language alignment, as MLLMs are prone to adapting to the language style of training samples. To address this issue, we propose a novel training method called Decoupled Proxy Alignment (DPA). DPA introduces two key innovations: (1) the use of a proxy LLM during pretraining to decouple the vision-language alignment process from language prior interference, and (2) dynamic loss adjustment based on visual relevance to strengthen optimization signals for visually relevant tokens. Extensive experiments demonstrate that DPA significantly mitigates the language prior conflict, achieving superior alignment performance across diverse datasets, model families, and scales. Our method not only improves the effectiveness of MLLM training but also shows exceptional generalization capabilities, making it a robust approach for vision-language alignment.

While powerful, large language models (LLMs) present significant fine-tuning challenges due to their size. Parameter-efficient fine-tuning (PEFT) methods like LoRA provide solutions, yet suffer from critical optimizer inefficiencies; notably basis redundancy in LoRA’s B matrix when using AdamW, which fundamentally limits performance. We address this by optimizing the B matrix on the Stiefel manifold, imposing explicit orthogonality constraints that achieve near-perfect orthogonality and full effective rank. This geometric approach dramatically enhances parameter efficiency and representational capacity. Our Stiefel optimizer consistently outperforms AdamW across benchmarks with both LoRA and DoRA, demonstrating that geometric constraints are the key to unlocking LoRA’s full potential for effective LLM fine-tuning.

Synthetic data adoption in healthcare is driven by privacy concerns, data access limitations, and high annotation costs. We explore synthetic Prolonged Exposure (PE) therapy conversations for PTSD as a scalable alternative for training clinical models. We systematically compare real and synthetic dialogues using linguistic, structural, and protocol-specific metrics like turn-taking and treatment fidelity. We introduce and evaluate PE-specific metrics, offering a novel framework for assessing clinical fidelity beyond surface fluency. Our findings show that while synthetic data successfully mitigates data scarcity and protects privacy, capturing the most subtle therapeutic dynamics remains a complex challenge. Synthetic dialogues successfully replicate key linguistic features of real conversations, for instance, achieving a similar Readability Score (89.2 vs. 88.1), while showing differences in some key fidelity markers like distress monitoring. This comparison highlights the need for fidelity-aware metrics that go beyond surface fluency to identify clinically significant nuances. Our model-agnostic framework is a critical tool for developers and clinicians to benchmark generative model fidelity before deployment in sensitive applications. Our findings help clarify where synthetic data can effectively complement real-world datasets, while also identifying areas for future refinement.

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Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization
Vishal Dey | Xiao Hu | Xia Ning

In real-world drug design, molecule optimization requires selectively improving multiple molecular properties up to pharmaceutically relevant levels, while maintaining others that already meet such criteria. However, existing computational approaches and instruction-tuned LLMs fail to capture such nuanced property-specific objectives, limiting their practical applicability. To address this, we introduce C-MuMOInstruct, the first instruction-tuning dataset focused on multi-property optimization with explicit, property-specific objectives. Leveraging C-MuMOInstruct, we develop \mathtt{GeLLM^4O\text{-}C}s, a series of instruction-tuned LLMs that can perform targeted property-specific optimization. Our experiments across 5 in-distribution and 5 out-of-distribution tasks show that \mathtt{GeLLM^4O\text{-}C}s consistently outperform strong baselines, achieving up to 126% higher success rate. Notably, \mathtt{GeLLM^4O\text{-}C}s exhibit impressive 0-shot generalization to novel optimization tasks and unseen instructions. This offers a step toward a foundational LLM to support realistic, diverse optimizations with property-specific objectives. C-MuMOInstruct and code are accessible through https://github.com/ninglab/GeLLMO-C.

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Measuring Lexical Diversity of Synthetic Data Generated through Fine-Grained Persona Prompting
Gauri Kambhatla | Chantal Shaib | Venkata S Govindarajan

Fine-grained personas have recently been used for generating ‘diverse’ synthetic data for pre-training and supervised fine-tuning of Large Language Models (LLMs). In this work, we measure the diversity of persona-driven synthetically generated prompts and responses with a suite of lexical diversity and redundancy metrics. First, we find that synthetic prompts/instructions are significantly less diverse than human-written ones. Next, we sample responses from LLMs of different sizes with fine-grained and coarse persona descriptions to investigate how much fine-grained detail in persona descriptions contribute to generated text diversity. Our results indicate that persona prompting produces higher lexical diversity than prompting without personas, particularly in larger models. In contrast, adding fine-grained persona details yields minimal gains in diversity compared to simply specifying a length cutoff in the prompt.

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Beyond Function-Level Search: Repository-Aware Dual-Encoder Code Retrieval with Adversarial Verification
Aofan Liu | Song Shiyuan | Haoxuan Li | Cehao Yang | Yiyan Qi

The escalating complexity of modern codebases has intensified the need for code retrieval systems capable of interpreting cross-component change intents—a capability fundamentally absent in conventional function-level search paradigms. While recent research has improved alignment between queries and code snippets, retrieving contextually relevant code for certain change request remains underexplored. To bridge this gap, we present RepoAlignBench, the first benchmark designed to evaluate repository-level code retrieval for change request-driven scenarios, encompassing 52k columns. The benchmark shifts the paradigm from function-centric retrieval to holistic repository analysis. In addition, we propose ReflectCode, an adversarial reflection-augmented dual-tower architecture featuring disentangled code_encoder and doc_encoder towers. Our framework dynamically integrates syntactic patterns, function dependency, and semantic expansion intent through LLM. Comprehensive evaluations demonstrate that ReflectCode achieves 12.2% Top-5 Accuracy and 7.1% Recall improvements over state-of-the-art baselines.

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Watermark under Fire: A Robustness Evaluation of LLM Watermarking
Jiacheng Liang | Zian Wang | Spencer Hong | Shouling Ji | Ting Wang

Various watermarking methods (“watermarkers”) have been proposed to identify LLM-generated texts; yet, due to the lack of unified evaluation platforms, many critical questions remain under-explored: i) What are the strengths/limitations of various watermarkers, especially their attack robustness? ii) How do various design choices impact their robustness? iii) How to optimally operate watermarkers in adversarial environments? To fill this gap, we systematize existing LLM watermarkers and watermark removal attacks, mapping out their design spaces. We then develop WaterPark, a unified platform that integrates 10 state-of-the-art watermarkers and 12 representative attacks. More importantly, by leveraging WaterPark, we conduct a comprehensive assessment of existing watermarkers, unveiling the impact of various design choices on their attack robustness. We further explore the best practices to operate watermarkers in adversarial environments. We believe our study sheds light on current LLM watermarking techniques while WaterPark serves as a valuable testbed to facilitate future research.

Long-context extension seeks to expand the contextual window in pre-trained large language models (LLMs), allowing them to handle several multiples of their original training context lengths. The primary method for extending the window length involves expanding the initial positional encodings, such as interpolating and extrapolation new positions based on Rotary Position Embedding (RoPE). This expansion inevitably disrupts the positional encodings learned during pre-training, thereby affecting the attention allotment and introducing unseen positional encoding distributions. To address this issue, we propose a new extension strategy based on RoPE, namely Periodic Extrapolation Positional Encodings (PEPE). This strategy expands pre-trained high dimensional components of positional encodings by replicating them in a periodic manner, thereby neither altering the learned positional encoding spaces nor introducing new positional encoding distributions. Experiments demonstrate that PEPE-based approaches can significantly improve long-context extension capabilities using just one-fourth the fine-tuning steps required by state-of-the-art methods. In addition, we analyze the characteristics of PEPE based methods and the key parameters that contribute to their effectiveness. The code is publicly available.

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Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models
Yin Jou Huang | Rafik Hadfi

Self-report questionnaires have long been used to assess LLM personality traits, yet they fail to capture behavioral nuances due to biases and meta-knowledge contamination. This paper proposes a novel multi-observer framework for personality trait assessments in LLM agents that draws on informant-report methods in psychology. Instead of relying on self-assessments, we employ multiple observer LLM agents, each of which is configured with a specific relationship (e.g., family member, friend, or coworker). The observer agents interact with the subject LLM agent before assessing its Big Five personality traits. We show that observer-report ratings align more closely with human judgments than traditional self-reports and reveal systematic biases in LLM self-assessments. Further analysis shows that aggregating ratings of multiple observers provides more reliable results, reflecting a wisdom of the crowd effect up to 5 to 7 observers.

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Controlled Retrieval-augmented Context Evaluation for Long-form RAG
Jia-Huei Ju | Suzan Verberne | Maarten de Rijke | Andrew Yates

Retrieval-augmented generation (RAG) enhances large language models by incorporating context retrieved from external knowledge sources. While the effectiveness of the retrieval module is typically evaluated with relevance-based ranking metrics, such metrics may be insufficient to reflect the retrieval’s impact on the final RAG result, especially in long-form generation scenarios. We argue that providing a comprehensive retrieval-augmented context is important for long-form RAG tasks like report generation and propose metrics for assessing the context independent of generation. We introduce CRUX, a Controlled Retrieval-aUgmented conteXt evaluation framework designed to directly assess retrieval-augmented contexts. This framework uses human-written summaries to control the information scope of knowledge, enabling us to measure how well the context covers information essential for long-form generation. CRUX uses question-based evaluation to assess RAG’s retrieval in a fine-grained manner. Empirical results show that CRUX offers more reflective and diagnostic evaluation. Our findings also reveal substantial room for improvement in current retrieval methods, pointing to promising directions for advancing RAG’s retrieval. Our data and code are publicly available to support and advance future research on retrieval for RAG. Github: https://github.com/DylanJoo/crux

Code generation is a core capability of large language models (LLMs), yet mainstream benchmarks (e.g., APPs and LiveCodeBench) contain questions with medium-level difficulty and pose no challenge to advanced LLMs. To better reflected the advanced reasoning and code generation ability, We introduce Humanity’s Last Code Exam (HLCE), comprising 235 most challenging problems from the International Collegiate Programming Contest (ICPC World Finals) and the International Olympiad in Informatics (IOI) spanning 2010 – 2024. As part of HLCE, we design a harmonized online–offline sandbox that guarantees fully reproducible evaluation. Through our comprehensive evaluation, we observe that even the strongest reasoning LLMs: o4-mini(high) and Gemini-2.5 Pro, achieve pass@1 rates of only 15.9% and 11.4%, respectively. Meanwhile, we propose a novel “self-recognition” task to measure LLMs’ awareness of their own capabilities. Results indicate that LLMs’ self-recognition abilities are not proportionally correlated with their code generation performance. Finally, our empirical validation of test-time scaling laws reveals that current advanced LLMs have substantial room for improvement on complex programming tasks. We expect HLCE to become a milestone challenge for code generation and to catalyze advances in high-performance reasoning and human–AI collaborative programming. Our code and dataset are also public available¹.https://github.com/Humanity-s-Last-Code-Exam/HLCE

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False Friends Are Not Foes: Investigating Vocabulary Overlap in Multilingual Language Models
Julie Kallini | Dan Jurafsky | Christopher Potts | Martijn Bartelds

Subword tokenizers trained on multilingual corpora naturally produce overlapping tokens across languages. Does token overlap facilitate cross-lingual transfer or instead introduce interference between languages? Prior work offers mixed evidence, partly due to varied setups and confounders, such as token frequency or subword segmentation granularity. To address this question, we devise a controlled experiment where we train bilingual autoregressive models on multiple language pairs under systematically varied vocabulary overlap settings. Crucially, we explore a new dimension to understanding how overlap affects transfer: the semantic similarity of tokens shared across languages. We first analyze our models’ hidden representations and find that overlap *of any kind* creates embedding spaces that capture cross-lingual semantic relationships, while this effect is much weaker in models with disjoint vocabularies. On XNLI and XQuAD, we find that models with overlap outperform models with disjoint vocabularies, and that transfer performance generally improves as overlap increases. Overall, our findings highlight the advantages of token overlap in multilingual models and show that substantial shared vocabulary remains a beneficial design choice for multilingual tokenizers.

Document-level event argument extraction (EAE) is a critical task in natural language processing. While most prior approaches rely on supervised training with large labeled datasets or resource-intensive fine-tuning, recent studies explore in-context learning (ICL) with LLMs to reduce data dependence and training costs. However, the performance of ICL-based methods still lags behind fully supervised models.We highlight a key reason for this shortfall: the lack of sufficient extraction rules. In this paper, we conduct a systematic study of using hierarchical rules to enhance LLMs’ ICL capabilities. We first define three types of hierarchical rules and demonstrate their effectiveness in enhancing the performance of LLMs for document-level EAE. Building on this, we further propose an LLM-driven HiErarchical Rule Optimization (HERO) framework that iteratively generates and selects optimal hierarchical rules. Specifically, in each iteration, high-value instances are selected to produce error feedback, which is used to update and expand hierarchical rule sets. This results in multiple candidate hierarchical rule sets, from which the optimal one is selected using a scoring-based mechanism. During inference, prompts are constructed using the optimal hierarchical rules to enhance ICL performance of LLMs. Extensive experiments demonstrate the effectiveness of HERO, surpassing few-shot supervised methods and outperforming state-of-the-art prompting baselines by 3.18% F1 on RAMS, 4.30% F1 on DocEE-N, and 3.17% F1 on DocEE-C.

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SOPL: A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models
Shuyang Wang | Somayeh Moazeni | Diego Klabjan

Designing effective prompts is essential to guiding large language models (LLMs) toward desired responses. Automated prompt engineering aims to reduce reliance on manual efforts by streamlining the design, refinement, and optimization of natural language prompts. This paper proposes an optimal learning framework for automated prompt engineering for black-box models, designed to sequentially identify effective prompt features under limited evaluation budgets. We introduce a feature-based method to express prompt templates, which significantly broadens the search space. Bayesian regression is employed to utilize correlations among similar prompts, accelerating the learning process. To efficiently explore the large space of prompt features, we adopt the forward-looking Knowledge-Gradient (KG) policy for sequential optimal learning efficiently by solving mixed-integer second-order cone optimization problems, making it scalable and capable of accommodating prompts characterized only through constraints. Our method significantly outperforms a set of benchmark strategies assessed on instruction induction tasks within limited iterations of prompt evaluations, showing the potential of optimal learning for efficient prompt learning.

Mixture-of-Experts (MoE) models are crucial for scaling model capacity while controlling inference costs. While integrating MoE into multimodal models like CLIP improves performance, training these models is notoriously challenging and expensive. We propose CLIP-Upcycling (CLIP-UP), an efficient alternative training strategy that converts a pre-trained dense CLIP model into a sparse MoE architecture. Through extensive experimentation with various settings and auxiliary losses, we demonstrate that CLIP-UP significantly reduces training complexity and cost. Remarkably, our sparse CLIP B/16 model, trained with CLIP-UP, outperforms its dense counterpart by 7.2% and 6.6% on COCO and Flickr30k text-to-image Recall@1 benchmarks respectively. It even surpasses the larger CLIP L/14 model on this task while using only 30% of the inference FLOPs. We further demonstrate the generalizability of our training recipe across different scales, establishing sparse upcycling as a practical and scalable approach for building efficient, high-performance CLIP models.

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A Category-Theoretic Approach to Neural-Symbolic Task Planning with Bidirectional Search
Shuhui Qu | Jie Wang | Kincho Law

We introduce a Neural-Symbolic Task Planning framework integrating Large Language Model (LLM) decomposition with category-theoretic verification for resource-aware, temporally consistent planning. Our approach represents states as objects and valid operations as morphisms in a categorical framework, ensuring constraint satisfaction through mathematical pullbacks. We employ bidirectional search that simultaneously expands from initial and goal states, guided by a learned planning distance function that efficiently prunes infeasible paths. Empirical evaluations across three planning domains demonstrate that our method improves completion rates by up to 6.6% and action accuracy by 9.1%, while eliminating resource violations compared to the existing baselines. These results highlight the synergy between LLM-based operator generation and category-theoretic verification for reliable planning in domains requiring both resource-awareness and temporal consistency.

Large language models (LLMs) are increasingly being adopted as the cognitive core of embodied agents. However, inherited hallucinations, which stem from failures to ground user instructions in the observed physical environment, can lead to navigation errors, such as searching for a refrigerator that does not exist. In this paper, we present the first systematic study of hallucinations in LLM-based embodied agents performing long-horizon tasks under scene–task inconsistencies. Our goal is to understand to what extent hallucinations occur, what types of inconsistencies trigger them, and how current models respond. To achieve these goals, we construct a hallucination probing set by building on an existing benchmark, capable of inducing hallucination rates up to 40× higher than base prompts. Evaluating 12 models across two simulation environments, we find that while models exhibit reasoning, they fail to resolve scene-task inconsistencies — highlighting fundamental limitations in handling infeasible tasks. We also provide actionable insights on ideal model behavior for each scenario, offering guidance for developing more robust and reliable planning strategies.

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Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics
Reza Sanayei | Srdjan Vesic | Eduardo Blanco | Mihai Surdeanu

Large Language Models (LLMs) excel at linear reasoning tasks but remain underexplored on non-linear structures such as those found in natural debates, which are best expressed as argument graphs. We evaluate whether LLMs can approximate structured reasoning from Computational Argumentation Theory (CAT). Specifically, we use Quantitative Argumentation Debate (QuAD) semantics, which assigns acceptability scores to arguments based on their attack and support relations. Given only dialogue-formatted debates from two NoDE datasets, models are prompted to rank arguments without access to the underlying graph. We test several LLMs under advanced instruction strategies, including Chain-of-Thought and In-Context Learning. While models show moderate alignment with QuAD rankings, performance degrades with longer inputs or disrupted discourse flow. Advanced prompting helps mitigate these effects by reducing biases related to argument length and position. Our findings highlight both the promise and limitations of LLMs in modeling formal argumentation semantics and motivate future work on graph-aware reasoning.

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How Jailbreak Defenses Work and Ensemble? A Mechanistic Investigation
Zhuohan Long | Siyuan Wang | Shujun Liu | Yuhang Lai

Jailbreak attacks, where harmful prompts bypass generative models’ built-in safety, raise serious concerns about model vulnerability. While many defense methods have been proposed, the trade-offs between safety and helpfulness, and their application to Large Vision-Language Models (LVLMs), are not well understood. This paper systematically examines jailbreak defenses by reframing the standard generation task as a binary classification problem to assess model refusal tendencies for both harmful and benign queries. We identify two key defense mechanisms: safety shift, which increases refusal rates across all queries, and harmfulness discrimination, which improves the model’s ability to differentiate between harmful and benign inputs. Using these mechanisms, we develop two ensemble defense strategies—inter-mechanism and intra-mechanism ensembles—to balance safety and helpfulness. Experiments on the MM-SafetyBench and MOSSBench datasets with LLaVA-1.5 models show that these strategies effectively improve model safety or optimize the trade-off between safety and helpfulness.

Autoregressive models excel in sequential modeling and have proven to be effective for vision-language data. However, the spatial nature of visual signals conflicts with the sequential dependencies of next-token prediction, leading to suboptimal results. This work proposes a plug-and-play refinement module to enhance the complex spatial correspondence modeling within the generated visual sequence. This module operates as a post-pretraining step tojointly refine all generated tokens of autoregressive model, enhancing vision-language modeling under a shared sequential prediction framework. By leveraging global context and relationship across the tokens, our method mitigates the error accumulation issue within the sequential generation. Experiments demonstrate that the proposed method improves the generation quality, enhancing the model’s ability to produce semantically consistent results.

Large language models have demonstrated considerable capabilities in various mathematical tasks, yet they often fall short in rigorous, proof-based reasoning essential for research-level mathematics. Retrieval-augmented generation presents a promising direction for enhancing these capabilities. This paper systematically explores RAG for natural language theorem proving, revealing that LLMs, when augmented with retrieved proofs rather than just theorems, can function as potent mimetic theorem provers: these models can effectively generalize proof techniques found in unstructured retrieved contexts to construct correct proofs for novel theorems. Building upon this finding, we introduce Dual RAG, a simple yet effective RAG framework. Dual RAG employs LLMs to identify underlying reasoning challenges within theorems, augmenting both queries and document contexts to improve retrieval performance. Our experiments show that Dual RAG achieves substantial improvements in retrieval performance, with gains of up to 34.19%. Expert evaluations further confirm that these retrieval enhancements directly translate into higher quality proof generation. Notably, when integrated with the arXiv API, Dual RAG demonstrates the ability to prove research-level theorems in theoretical machine learning, highlighting its strong potential as a foundational element for a practical mathematical copilot.

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LORE: Continual Logit Rewriting Fosters Faithful Generation
Charles Yu | Qingyun Wang | Yuting Hu | Jinjun Xiong | Heng Ji

As autonomous agents and assistants, large language models (LLMs) often struggle with “hallucinations.” Fundamentally, the problem is one of prioritization and balance: the LLM needs to understand or infer when it needs to be creative and balance that with its need to be accurate. Most efforts focus on either updating intrinsic knowledge via targeted post-training or by adding external knowledge sources which the LLM can reference neurosymbolically (e.g., via retrieval-augmented generation). However, these all eventually rely on the LLM’s implicit reasoning ability during generation, still allowing for these random hallucinations despite high-quality training examples and references. Using aspect-oriented summarization as a case study, we propose **LOgit REwriting**(**LORE**), a new controlled generation paradigm which can simultaneously be faithful to external knowledge and to the LLM’s intentions. LORE works by adding a rewriting module at left-to-right inference time, continuously reflecting on the newest prediction and trying to find a replacement that is more faithful to the source document. Then, it merges the logits of the replacement with those of the original prediction to generate the next token. We created a new long-context aspect-oriented summarization dataset, **SLPAspect**, and find that LORE generates 5.8% better summaries compared to the LLM without LORE-rewriting. All code and data from this paper will be available on GitHub after the anonymity period.

Dialogue agents based on large language models (LLMs) have shown promising performance in proactive dialogue, which requires effective strategy planning. However, existing approaches to strategy planning for proactive dialogue face several limitations: limited strategy coverage, preference bias in planning, and reliance on costly additional training. To address these, we propose PRINCIPLES: a synthetic strategy memory for proactive dialogue agents. PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference, eliminating the need for additional training and data annotation. We evaluate PRINCIPLES in both emotional support and persuasion domains, demonstrating consistent improvements over strong baselines. Furthermore, PRINCIPLES maintains its robustness across extended and more diverse evaluation settings. See our project page at https://huggingface.co/spaces/kimnamssya/Principles.

Small Language Models (SLMs) offer computational efficiency and accessibility, yet a systematic evaluation of their performance and environmental impact remains lacking. We introduce SLM-Bench, the first benchmark specifically designed to assess SLMs across multiple dimensions, including accuracy, computational efficiency, and sustainability metrics. SLM-Bench evaluates 15 SLMs on 9 NLP tasks using 23 datasets spanning 14 domains. The evaluation is conducted on 4 hardware configurations, providing a rigorous comparison of their effectiveness. Unlike prior benchmarks, SLM-Bench quantifies 11 metrics across correctness, computation, and consumption, enabling a holistic assessment of efficiency trade-offs. Our evaluation considers controlled hardware conditions, ensuring fair comparisons across models. We develop an open-source benchmarking pipeline with standardized evaluation protocols to facilitate reproducibility and further research. Our findings highlight the diverse trade-offs among SLMs, where some models excel in accuracy while others achieve superior energy efficiency. SLM-Bench sets a new standard for SLM evaluation, bridging the gap between resource efficiency and real-world applicability.

Text style transfer (TST) modifies a source sentence to match a target style while preserving its semantics. While existing models perform well on simple styles like sentiment and formality, they struggle with complex, entangled styles such as poetry and brand-specific tones, which require advanced operations to disentangle content and style. We propose a multi-agent self-check framework that contains a large language model (LLM) as a planner for disentangling subtasks and expert agents for executing the subtasks. This training-free multi-agent framework decomposes TST into manageable components, enabling iterative refinement through a self-check module that balances style adherence and content preservation. Experiments on both simple and complex style datasets show our framework significantly improves style strength and content preservation, with strong adaptability in few-shot settings.

Speculative decoding has emerged as a promising approach to accelerating large language model (LLM) generation using a fast drafter while maintaining alignment with the target model’s distribution. However, existing approaches face a trade-off: external drafters offer flexibility but can suffer from slower drafting, while self-speculation methods use drafters tailored to the target model but require re-training. In this paper, we introduce novel drafters based on Mamba, a state-of-the-art state space model (SSM), as a solution that combines the best aspects of both approaches. By leveraging the linear structure of SSMs, our approach avoids the quadratic complexity inherent in traditional Transformer-based methods, enabling faster drafting and lower memory usage while maintaining the flexibility to work across different target models. We further enhance efficiency with a novel test-time tree search algorithm for generating high-quality draft candidates. Our empirical evaluation demonstrates that Mamba-based drafters not only outperform existing external drafting methods but are also comparable to state-of-the-art self-speculation approaches while using less memory and maintaining their cross-model adaptability.

Expanding the long-context capabilities of Multi-modal Large Language Models (MLLMs) is critical for advancing video understanding and high-resolution image analysis. Achieving this requires systematic improvements in model architecture, data construction, and training strategies, particularly to address challenges such as performance degradation with increasing image counts and high computational costs. In this paper, we propose a hybrid architecture that integrates Mamba and Transformer blocks, introduce data construction methods that capture both temporal and spatial dependencies, and employ a progressive training strategy. Our released model, LongLLaVA (Long-Context Large Language and Vision Assistant), demonstrates an effective balance between efficiency and performance. LongLLaVA achieves competitive results across various benchmarks while maintaining high throughput and low memory consumption. Notably, it can process nearly one thousand images on a single A100 80GB GPU, underscoring its potential for a wide range of multi-modal applications.

Recent large language models have shown promising capabilities in long-form reasoning, following structured chains of thought before arriving at a final answer. However, we observe that these reasoning paths tend to include substantial redundancy; analyzing attention patterns reveals that attention scores are widely scattered, particularly incorrect answers exhibit greater attention sparsity. In this paper, we demonstrate that deliberately removing this redundancy in the reasoning process significantly improves the performance through clear thinking (i.e., removing distraction). Specifically, we systematically identify such redundancy by measuring token-level attention scores to a special end-of-thinking token, which is appended to an explicit instruction inserted to conclude each intermediate reasoning step. Furthermore, we propose structure-aware pruning that prioritizes removing tokens in low-contributing reasoning chunks over individual tokens. After evicting redundant tokens, we remove the injected end-of-thinking instruction, then resume the reasoning generation. We demonstrate that our method significantly improves the over all accuracy across reasoning-intensive benchmarks without any training involved. In particular, our method shows strong performance on challenging mathematics competition benchmarks such as AIME and AMC, where reasoning redundancy is more prevalent.

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A Systematic Survey of Claim Verification: Corpora, Systems, and Case Studies
Zhaxi Zerong | Chenxi Li | Xinyi Liu | Ju-hui Chen | Fei Xia

Automated Claim Verification (CV)—the task of assessing a claim’s veracity against explicitly provided evidence—is a critical tool in the fight against growing misinformation. This survey offers a comprehensive analysis of 198 studies published between January 2022 and March 2025, synthesizing recent advances in CV corpus creation and system design. Through two in-depth case studies, we illuminate persistent challenges in veracity annotation, limitations of conventional CV pipelines, and pitfalls in recent claim decomposition approaches. We conclude by identifying key unresolved challenges and proposing productive directions for future research.

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Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach
Ruizhe Li | Chiwei Zhu | Benfeng Xu | Xiaorui Wang | Zhendong Mao

Creative writing is a key capability of Large Language Models (LLMs), with potential applications in literature, storytelling, and various creative domains. However, evaluating the creativity of machine-generated texts remains a significant challenge, as existing methods either rely on costly manual annotations or fail to align closely with human assessments. In this paper, we propose an effective automated evaluation method based on the Torrance Test of Creative Writing (TTCW), which evaluates creativity as product. Our method employs a reference-based Likert-style approach, scoring generated creative texts relative to high-quality reference texts across various tests. Experimental results demonstrate that our method significantly improves the alignment between LLM evaluations and human assessments, achieving a pairwise accuracy of 0.75 (+15%).

Composing language models (LMs) into multi-step language programs and automatically optimizing their modular prompts is now a mainstream paradigm for building AI systems, but the tradeoffs in this space have only scarcely been studied before. We introduce LangProBe, the first large-scale benchmark for evaluating the architectures and optimization strategies for language programs, with over 2000 combinations of tasks, architectures, optimizers, and choices of LMs. Using LangProBe, we are the first to study the impact of program architectures and optimizers (and their compositions together and with different models) on tradeoffs of quality and cost. We find that optimized language programs offer strong cost-quality Pareto improvement over raw calls to models, but simultaneously demonstrate that human judgment (or empirical decisions) about which compositions to pursue is still necessary for best performance.

Self-disclosure can provide psychological comfort and social support, but it also carries the risk of unintentionally revealing sensitive information, leading to serious privacy concerns. Research on self-disclosure in Chinese multimodal contexts remains limited, lacking high-quality corpora, analysis, and methods for detection. This work focuses on self-disclosure behaviors on Chinese multimodal social media platforms and constructs a high-quality text-image corpus to address this critical data gap. We systematically analyze the distribution of self-disclosure types, modality preferences, and their relationship with user intent, uncovering expressive patterns unique to the Chinese multimodal context. We also fine-tune five multimodal large language models to enhance self-disclosure detection in multimodal scenarios. Among these models, the Qwen2.5-omni-7B achieved a strong performance, with a partial span F1 score of 88.2%. This study provides a novel research perspective on multimodal self-disclosure in the Chinese context.

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MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model
Sumin Ha | Jun Hyeong Kim | Yinhua Piao | Changyun Cho | Sun Kim

Deciphering molecular meaning in chemistry and biomedicine depends on context — a capability that large language models (LLMs) can enhance by aligning molecular structures with language. However, existing molecule-text models ignore complementary information in different molecular views and rely on single-view representations, limiting molecule structural understanding. Moreover, naïve multi-view alignment strategies face two challenges: (1) the aligned spaces differ across views due to inconsistent molecule-text mappings, and (2) existing loss objectives fail to preserve complementary information necessary for finegrained alignment. To enhance LLM’s ability to understand molecular structure, we propose MV-CLAM, a novel framework that aligns multi-view molecular representations into a unified textual space using a multi-querying transformer (MQ-Former). Our approach ensures cross-view consistency while the proposed token-level contrastive loss preserves diverse molecular features across textual queries. MV-CLAM enhances molecular reasoning, improving retrieval and captioning accuracy. The source code of MV-CLAM is available in https://github.com/sumin124/mv-clam.

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Mind the Style Gap: Meta-Evaluation of Style and Attribute Transfer Metrics
Amalie Brogaard Pauli | Isabelle Augenstein | Ira Assent

Large language models (LLMs) make it easy to rewrite a text in any style – e.g. to make it more polite, persuasive, or more positive – but evaluation thereof is not straightforward. A challenge lies in measuring content preservation: that content not attributable to style change is retained. This paper presents a large meta-evaluation of metrics for evaluating style and attribute transfer, focusing on content preservation. We find that meta-evaluation studies on existing datasets lead to misleading conclusions about the suitability of metrics for content preservation. Widely used metrics show a high correlation with human judgments despite being deemed unsuitable for the task – because they do not abstract from style changes when evaluating content preservation. We show that the overly high correlations with human judgment stem from the nature of the test data. To address this issue, we introduce a new, challenging test set specifically designed for evaluating content preservation metrics for style transfer. We construct the data by creating high variation in the content preservation. Using this dataset, we demonstrate that suitable metrics for content preservation for style transfer indeed are style-aware.To support efficient evaluation, we propose a new style-aware method that utilises small language models, obtaining a higher alignment with human judgements than prompting a model of a similar size as an autorater.

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ExtremeAIGC: Benchmarking LMM Vulnerability to AI-Generated Extremist Content
Bhavik Chandna | Mariam Aboujenane | Usman Naseem

Large Multimodal Models (LMMs) are increasingly vulnerable to AI-generated extremist content, including photorealistic images and text, which can be used to bypass safety mechanisms and generate harmful outputs. However, existing datasets for evaluating LMM robustness offer limited exploration of extremist content, often lacking AI-generated images, diverse image generation models, and comprehensive coverage of historical events, which hinders a complete assessment of model vulnerabilities. To fill this gap, we introduce ExtremeAIGC, a benchmark dataset and evaluation framework designed to assess LMM vulnerabilities against such content. ExtremeAIGC simulates real-world events and malicious use cases by curating diverse text and image based examples crafted using state-of-the-art image generation techniques. Our study reveals alarming weaknesses in LMMs, demonstrating that even cutting-edge safety measures fail to prevent the generation of extremist material. We systematically quantify the success rates of various attack strategies, exposing critical gaps in current defenses and emphasizing the need for more robust mitigation strategies. The code and data can be found at https://github.com/TheProParadox/ExtremeAIGC.

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Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data
Kurt Micallef | Nizar Habash | Claudia Borg

Maltese is a unique Semitic language that has evolved under extensive influence from Romance and Germanic languages, particularly Italian and English. Despite its Semitic roots, its orthography is based on the Latin script, creating a gap between it and its closest linguistic relatives in Arabic. In this paper, we explore whether Arabic-language resources can support Maltese natural language processing (NLP) through cross-lingual augmentation techniques. We investigate multiple strategies for aligning Arabic textual data with Maltese, including various transliteration schemes and machine translation (MT) approaches. As part of this, we also introduce novel transliteration systems that better represent Maltese orthography. We evaluate the impact of these augmentations on monolingual and mutlilingual models and demonstrate that Arabic-based augmentation can significantly benefit Maltese NLP tasks.

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Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs
Yang Liu | Chenhui Chu

Large language models (LLMs) can lead to undesired consequences when misaligned with human values, especially in scenarios involving complex and sensitive social biases. Previous studies have revealed the misalignment of LLMs with human values using expert-designed or agent-based emulated bias scenarios. However, it remains unclear whether the alignment of LLMs with human values differs across different types of scenarios (e.g., scenarios containing negative vs. non-negative questions). In this study, we investigate the alignment of LLMs with human values regarding social biases (HVSB) in different types of bias scenarios. Through extensive analysis of 12 LLMs from four model families and four datasets, we demonstrate that LLMs with large model parameter scales do not necessarily have lower misalignment rate and attack success rate. Moreover, LLMs show a certain degree of alignment preference for specific types of scenarios and the LLMs from the same model family tend to have higher judgment consistency. In addition, we study the understanding capacity of LLMs with their explanations of HVSB. We find no significant differences in the understanding of HVSB across LLMs. We also find LLMs prefer their own generated explanations. Additionally, we endow smaller language models (LMs) with the ability to explain HVSB.The generation results show that the explanations generated by the fine-tuned smaller LMs are more readable, but have a relatively lower agreeability.

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CoEx – Co-evolving World-model and Exploration
Minsoo Kim | Seung-won Hwang

Planning in modern LLM agents relies on the utilization of LLM as an internal world model, acquired during pretraining. However, existing agent designs fail to effectively assimilate new observations into dynamic updates of the world model. This reliance on the LLM’s static internal world model is progressively prone to misalignment with the underlying true state of the world, leading to the generation of divergent and erroneous plans. We introduce a hierarchical agent architecture, CoEx, in which hierarchical state abstraction allows LLM planning to co-evolve with a dynamically updated model of the world. CoEx plans and interacts with the world by using LLM reasoning to orchestrate dynamic plans consisting of subgoals, and its learning mechanism continuously incorporates these subgoal experiences into a persistent world model in the form of a neurosymbolic belief state, comprising textual inferences and code-based symbolic memory. We evaluate our agent across a diverse set of agent scenarios involving rich environments and complex tasks including ALFWorld, PDDL, and Jericho. Our experiments show that CoEx outperforms existing agent paradigms in planning and exploration.

Object detection is a core challenge in computer vision. Traditional methods primarily rely on intermediate modalities such as text, speech, or visual cues to interpret user intent, leading to inefficient and potentially distorted expressions of intent. Brain signals, particularly fMRI signals, emerge as a novel modality that can directly reflect user intent, eliminating ambiguities introduced during modality conversion. However, brain signal-based object detection still faces challenges in accuracy and robustness. To address these challenges, we present BrainLoc, a lightweight object detection model guided by fMRI signals. First, we employ a multi-modal alignment strategy that enhances fMRI signal feature extraction by incorporating various modalities including images and text. Second, we propose a cross-domain fusion module that promotes interaction between fMRI features and category features, improving the representation of category information in fMRI signals. Extensive experiments demonstrate that BrainLoc achieves state-of-the-art performance in brain signal-based object detection tasks, showing significant advantages in both accuracy and convenience.

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PVTNL: Prompting Vision Transformers with Natural Language for Generalizable Person Re-identification
Wangning | Lei Xie | Sanglu Lu | Shiwei Gan

Domain generalization person re-identification (DG-ReID) aims to train models on source domains and generalize to unseen target domains.While patch-based Vision Transformers have achieved success in capturing fine-grained visual features, they often overlook global semantic structure and suffer from feature entanglement, leading to overfitting across domains. Meanwhile, natural language provides high-level semantic abstraction but lacks spatial precision for fine-grained alignment.We propose PVTNL (Prompting Vision Transformers with Natural Language), a novel framework for generalizable person re-identification. PVTNL leverages the pre-trained vision-language model BLIP to extract aligned visual and textual embeddings. Specifically, we utilize body-part cues to segment images into semantically coherent regions and align them with corresponding natural language descriptions. These region-level textual prompts are encoded and injected as soft prompts into the Vision Transformer to guide localized feature learning. Notably, our language module is retained during inference, enabling persistent semantic grounding that enhances cross-domain generalization.Extensive experiments on standard DG-ReID benchmarks demonstrate that PVTNL achieves state-of-the-art performance. Ablation studies further confirm the effectiveness of body-part-level alignment, soft language prompting, and the benefit of preserving language guidance at inference time.

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RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals
Jaemu Heo | Eldor Fozilov | Hyunmin Song | Taehwan Kim

Transformers have achieved great success in effectively processing sequential data such as text. Their architecture consisting of several attention and feedforward blocks can model relations between elements of a sequence in parallel manner, which makes them very efficient to train and effective in sequence modeling. Even though they have shown strong performance in processing sequential data, the size of their parameters is considerably larger when compared to other architectures such as RNN and CNN based models. Therefore, several approaches have explored parameter sharing and recurrence in Transformer models to address their computational demands. However, such methods struggle to maintain high performance compared to the original transformer model. To address this challenge, we propose our novel approach, RingFormer, which employs one Transformer layer that processes input repeatedly in a circular, ring-like manner, while utilizing low-rank matrices to generate input-dependent level signals. This allows us to reduce the model parameters substantially while maintaining high performance in a variety of tasks such as translation and image classification, as validated in the experiments.

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TriSPrompt: A Hierarchical Soft Prompt Model for Multimodal Rumor Detection with Incomplete Modalities
Jiajun Chen | Yangyang Wu | Xiaoye Miao | Mengying Zhu | Meng Xi

The widespread presence of incomplete modalities in multimodal data poses a significant challenge to achieving accurate rumor detection. Existing multimodal rumor detection methods primarily focus on learning joint modality representations from complete multimodal training data, rendering them ineffective in addressing the common occurrence of missing modalities in real-world scenarios. In this paper, we propose a hierarchical soft prompt model TriSPrompt, which integrates three types of prompts, i.e., modality-aware (MA) prompt, modality-missing (MM) prompt, and mutual-views (MV) prompt, to effectively detect rumors in incomplete multimodal data. The MA prompt captures both heterogeneous information from specific modalities and homogeneous features from available data, aiding in modality recovery. The MM prompt models missing states in incomplete data, enhancing the model’s adaptability to missing information. The MV prompt learns relationships between subjective (i.e., text and image) and objective (i.e., comments) perspectives, effectively detecting rumors. Extensive experiments on three real-world benchmarks demonstrate that TriSPrompt achieves an accuracy gain of over 13% compared to state-of-the-art methods. The codes and datasets are available at https: //anonymous.4open.science/r/code-3E88.

Research in uncertainty quantification (UQ) for large language models (LLMs) is increasingly important towards guaranteeing the reliability of this groundbreaking technology. We explore the integration of LLM UQ methods in argumentative LLMs (ArgLLMs), an explainable LLM framework for decision-making based on computational argumentation in which UQ plays a critical role. We conduct experiments to evaluate ArgLLMs’ performance on claim verification tasks when using different LLM UQ methods, inherently performing an assessment of the UQ methods’ effectiveness. Moreover, the experimental procedure itself is a novel way of evaluating the effectiveness of UQ methods, especially when intricate and potentially contentious statements are present. Our results demonstrate that, despite its simplicity, direct prompting is an effective UQ strategy in ArgLLMs, outperforming considerably more complex approaches.

A core part of scientific peer review involves providing expert critiques that directly assess the scientific claims a paper makes. While it is now possible to automatically generate plausible (if generic) reviews, ensuring that these reviews are sound and grounded in the papers’ claims remains challenging. To facilitate LLM benchmarking on these challenges, we introduce CLAIMCHECK, an annotated dataset of NeurIPS 2023 and 2024 submissions and reviews mined from OpenReview. CLAIMCHECK is richly annotated by ML experts for weakness statements in the reviews and the paper claims that they dispute, as well as fine-grained labels of the validity, objectivity, and type of the identified weaknesses. We benchmark several LLMs on three claim-centric tasks supported by CLAIMCHECK, requiring models to (1) associate weaknesses with the claims they dispute, (2) predict fine-grained labels for weaknesses and rewrite the weaknesses to enhance their specificity, and (3) verify a paper’s claims with grounded reasoning. Our experiments reveal that cutting-edge LLMs, while capable of predicting weakness labels in (2), continue to underperform relative to human experts on all other tasks.

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From Noise to Clarity: Filtering Real and LLM-Generated Samples for Enhanced Intent Detection
Junbao Huang | Weizhen Li | Peijie Huang | Yuhong Xu

In dialogue intent detection, the challenge of acquiring sufficient corpora and the high cost of manual annotation often lead to incorrectly labeled or unrepresentative samples, which can hinder the generalization ability of classification models. Additionally, as using large language models for generating synthetic samples for data augmentation becomes more common, these synthetic samples may exacerbate the problem by introducing additional noise due to the models’ limited prior knowledge. To address this challenge, this paper proposes an interpretable Sample Filter by Topic Modeling (SFTM) framework. By evaluating the diversity and authenticity of the samples, SFTM effectively reduces the quantity of real and synthetic samples while improving the performance of the classification models. Our codes are publicly available at https://github.com/gumbouh/SFTM.

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Improving Language Model Personas via Rationalization with Psychological Scaffolds
Brihi Joshi | Xiang Ren | Swabha Swayamdipta | Rik Koncel-Kedziorski | Tim Paek

Language models prompted with a user description or persona have been used to predict the user’s preferences and opinions. However, existing approaches to building personas mostly rely on a user’s demographic attributes and/or prior judgments, but not on any underlying reasoning behind a user’s judgments. We introduce PB&J (Psychology of Behavior and Judgments), a framework that improves LM personas by incorporating potential rationales for why the user could have made a certain judgment. Our rationales are generated by a language model to explicitly reason about a user’s behavior on the basis of their experiences, personality traits, or beliefs. Our method employs psychological scaffolds: structured frameworks such as the Big 5 Personality Traits or Primal World Beliefs to help ground the generated rationales in existing theories. Experiments on public opinion and movie preference prediction tasks demonstrate that language model personas augmented with PB&J rationales consistently outperform personas conditioned only on user demographics and / or judgments, including those that use a model’s default chain-of-thought, which is not grounded in psychological theories. Additionally, our PB&J personas perform competitively with those using human-written rationales, suggesting the potential value of synthetic rationales guided by existing theories.

Large Language Models (LLMs) often struggle with dynamically changing knowledge and handling unknown static information. Retrieval-Augmented Generation (RAG) is employed to tackle these challenges and has a significant impact on improving LLM performance. In fact, we find that not all questions need to trigger RAG. By retrieving parts of knowledge unknown to the LLM and allowing the LLM to answer the rest, we can effectively reduce both time and computational costs. In our work, we propose a Knowledge Boundary Model (KBM) to express the known/unknown of a given question, and to determine whether a RAG needs to be triggered. Experiments conducted on 11 English and Chinese datasets illustrate that the KBM effectively delineates the knowledge boundary, significantly decreasing the proportion of retrievals required for optimal end-to-end performance. Furthermore, we evaluate the effectiveness of KBM in three complex scenarios: dynamic knowledge, long-tail static knowledge, and multi-hop problems, as well as its functionality as an external LLM plug-in.

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TABARD: A Novel Benchmark for Tabular Anomaly Analysis, Reasoning and Detection
Manan Roy Choudhury | Anirudh Iyengar Kaniyar Narayana Iyengar | Shikhhar Siingh | Sugeeth Puranam | Vivek Gupta

We study the capabilities of large language models (LLMs) in detecting fine-grained anomalies in tabular data. Specifically, we examine: (1) how well LLMs can identify diverse anomaly types including factual, logical, temporal, and value-based errors; (2) the impact of prompt design and prompting strategies; and (3) the effect of table structure and anomaly type on detection accuracy. To this end, we introduce TABARD, a new benchmark constructed by perturbing tables from WikiTQ, FeTaQA, Spider, and BEAVER. The dataset spans multiple domains and eight anomaly categories, including paired clean and corrupted tables. We evaluate LLMs using direct, indirect, and Chain-of-Thought (CoT) prompting. Our results reveal notable limitations in standard prompting, especially for complex reasoning tasks and longer tables. To overcome these issues, we propose a unified framework combining multi-step prompting, self-verification, and constraint-based rule execution. Our approach significantly improves precision and recall, offering a promising direction for robust and interpretable anomaly detection in tables.

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Aspect-based Sentiment Analysis via Synthetic Image Generation
Ge Chen | Zhongqing Wang | Guodong Zhou

Recent advancements in Aspect-Based Sentiment Analysis (ABSA) have shown promising results, yet the semantics derived solely from textual data remain limited. To overcome this challenge, we propose a novel approach by venturing into the unexplored territory of generating sentimental images. Our method introduce a synthetic image generation framework tailored to produce images that are highly congruent with both textual and sentimental information for aspect-based sentiment analysis. Specifically, we firstly develop a supervised image generation model to generate synthetic images with alignment to both text and sentiment information. Furthermore, we employ a visual refinement technique to substantially enhance the quality and pertinence of the generated images. After that, we propose a multi-modal model to integrate both the original text and the synthetic images for aspect-based sentiment analysis. Extensive evaluations on multiple benchmark datasets demonstrate that our model significantly outperforms state-of-the-art methods. These results highlight the effectiveness of our supervised image generation approach in enhancing ABSA.

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IntrEx: A Dataset for Modeling Engagement in Educational Conversations
Xingwei Tan | Mahathi Parvatham | Chiara Gambi | Gabriele Pergola

Engagement and motivation are crucial for second-language acquisition, yet maintaining learner interest in educational conversations remains a challenge. While prior research has explored what makes educational texts interesting, still little is known about the linguistic features that drive engagement in conversations. To address this gap, we introduce IntrEx, the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. Built upon the Teacher-Student Chatroom Corpus (TSCC), IntrEx extends prior work by incorporating sequence-level annotations, allowing for the study of engagement beyond isolated turns to capture how interest evolves over extended dialogues. We employ a rigorous annotation process with over 100 second-language learners, using a comparison-based rating approach inspired by reinforcement learning from human feedback (RLHF) to improve agreement. We investigate whether large language models (LLMs) can predict human interestingness judgments. We find that LLMs (7B/8B parameters) fine-tuned on interestingness ratings outperform larger proprietary models like GPT-4o, demonstrating the potential for specialised datasets to model engagement in educational settings. Finally, we analyze how linguistic and cognitive factors, such as concreteness, comprehensibility (readability), and uptake, influence engagement in educational dialogues.

The advancement of large language models (LLMs) has enabled the construction of multi-agent systems to solve complex tasks by dividing responsibilities among specialized agents, such as a planning agent for subgoal generation and a grounding agent for executing tool-use actions. Most existing methods typically fine-tune these agents independently, leading to capability gaps among them with poor coordination. To address this, we propose MOAT, a Multi-Agent Joint Alignment Tuning framework that improves agents collaboration through iterative alignment. MOAT alternates between two key stages: (1) Planning Agent Alignment, which optimizes the planning agent to generate subgoal sequences that better guide the grounding agent; and (2) Grounding Agent Improving, which fine-tunes the grounding agent using diverse subgoal-action pairs generated by the agent itself to enhance its generalization capablity. Theoretical analysis proves that MOAT ensures a non-decreasing and progressively convergent training process. Experiments across six benchmarks demonstrate that MOAT outperforms state-of-the-art baselines, achieving average improvements of 3.1% on held-in tasks and 4.4% on held-out tasks.

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Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models
Makesh Narsimhan Sreedhar | Traian Rebedea | Christopher Parisien

Reasoning-based language models have demonstrated strong performance across various domains, with the most notable gains seen in mathematical and coding tasks. Recent research has shown that reasoning also offers significant benefits for LLM safety and guardrail applications. In this work, we conduct a comprehensive analysis of training reasoning-based guardrail models for content moderation, with an emphasis on generalization to custom safety policies at inference time. Our study focuses on two key dimensions: data efficiency and inference efficiency. On the data front, we find that reasoning-based models exhibit strong sample efficiency, achieving competitive performance with significantly fewer training examples than their non-reasoning counterparts. This unlocks the potential to repurpose the remaining data for mining high-value, difficult samples that further enhance model performance. On the inference side, we evaluate practical trade-offs by introducing reasoning budgets, examining the impact of reasoning length on latency and accuracy, and exploring dual-mode training to allow runtime control over reasoning behavior. Our findings will provide practical insights for researchers and developers to effectively and efficiently train and deploy reasoning-based guardrails models in real-world systems.

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Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variables
Ivaxi Sheth | Sahar Abdelnabi | Mario Fritz

Scientific discovery catalyzes human intellectual advances, driven by the cycle of hypothesis generation, experimental design, evaluation, and assumption refinement. Central to this process is causal inference, uncovering the mechanisms behind observed phenomena. While randomized experiments provide strong inferences, they are often infeasible due to ethical or practical constraints. However, observational studies are prone to confounding or mediating biases. While crucial, identifying such backdoor paths is expensive and heavily depends on scientists’ domain knowledge to generate hypotheses. We introduce a novel benchmark where the objective is to complete a partial causal graph. We design a benchmark with varying difficulty levels with over 4000 queries. We show the strong ability of LLMs to hypothesize the backdoor variables between a cause and its effect. Unlike simple knowledge memorization of fixed associations, our task requires the LLM to reason according to the context of the entire graph.

While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its ability of improving models without requiring any additional training. In this paper, we propose a unified framework for model merging based on low-rank estimation of task vectors without the need for access to the base model, named LoRE-Merging. Our approach is motivated by the observation that task vectors from fine-tuned models frequently exhibit a limited number of dominant singular values, making low-rank estimations less prone to interference. We implement the method by formulating the merging problem as an optimization problem. Extensive empirical experiments demonstrate the effectiveness of our framework in mitigating interference and preserving task-specific information, thereby advancing the state-of-the-art performance in model merging techniques.

Retrieval-augmented generation (RAG) with foundation models has achieved strong performance across diverse tasks, but their capacity for expert-level reasoning—such as solving Olympiad-level physics problems—remains largely unexplored. Inspired by the way students prepare for competitions by reviewing past problems, we investigate the potential of RAG to enhance physics reasoning in foundation models. We introduce PhoPile, a high-quality multimodal dataset specifically designed for Olympiad-level physics, enabling systematic study of retrieval-based reasoning. PhoPile includes diagrams, graphs, and equations, capturing the inherently multimodal nature of physics problem solving. Using PhoPile, we benchmark RAG-augmented foundation models, covering both large language models (LLMs) and large multimodal models (LMMs) with multiple retrievers. Our results demonstrate that integrating retrieval with physics corpora can improve model performance, while also highlighting challenges that motivate further research in retrieval-augmented physics reasoning.

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FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction
Akriti Jain | Saransh Sharma | Koyel Mukherjee | Soumyabrata Pal

Auto-regressive Large Language Models (LLMs) demonstrate remarkable performance across different domains such as vision and language tasks. However, due to sequential processing through multiple transformer layers, autoregressive decoding faces significant computational challenges, particularly in resource-constrained environments like mobile and edge devices. Existing approaches in literature that aim to improve latency via skipping layers have two distinct flavors: (1) early exit, and (2) input-agnostic heuristics where tokens exit at pre-determined layers irrespective of input sequence. Both the above strategies have limitations, the former cannot be applied in the presence of KV caching, which is essential for speed-ups in modern inference frameworks, and the latter fails to capture variation in layer importance across tasks or, more generally, across input sequences. To address these limitations, we propose FiRST, a model-agnostic framework that reduces inference latency by using layer-specific routers to adaptively skip transformer layers during decoding, based on routing decisions made from the input prompt in the prefill stage. FiRST remains fully compatible with KV caching, enabling faster decoding while maintaining quality. Our method reveals that input adaptivity is essential: Different tasks rely on different subsets of layers to evolve meaningful representations. Extensive experiments show that FiRST significantly reduces latency while outperforming existing layer selection strategies in quality. It retains performance comparable to the base model without skipping. FiRST is thus a promising and efficient solution for LLM deployment in low-resource environments.

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PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels
Peyman Rostami | Vahid Rahimzadeh | Ali Adibi | Azadeh Shakery

Stance detection identifies the viewpoint expressed in text toward a specific target, such as a political figure. While previous datasets have focused primarily on tweet-level stances from established platforms, user-level stance resources—especially on emerging platforms like Bluesky—remain scarce. User-level stance detection provides a more holistic view by considering a user’s complete posting history rather than isolated posts. We present the first stance detection dataset for the 2024 U.S. presidential election, collected from Bluesky and centered on Kamala Harris and Donald Trump. The dataset comprises 16,044 user-target stance pairs enriched with engagement metadata, interaction graphs, and user posting histories. PolitiSky24 was created using a carefully evaluated pipeline combining advanced information retrieval and large language models, which generates stance labels with supporting rationales and text spans for transparency. The labeling approach achieves 81% accuracy with scalable LLMs. This resource addresses gaps in political stance analysis through its timeliness, open-data nature, and user-level perspective. The dataset is available at https://doi.org/10.5281/zenodo.15616911.

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From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse
Seunguk Yu | JungMin Yun | Jinhee Jang | YoungBin Kim

Although offensive language continually evolves over time, even recent studies using LLMs have predominantly relied on outdated datasets and rarely evaluated the generalization ability on unseen texts. In this study, we constructed a large-scale dataset of contemporary political discourse and employed three refined judgments in the absence of ground truth. Each judgment reflects a representative offensive language detection method and is carefully designed for optimal conditions. We identified distinct patterns for each judgment and demonstrated tendencies of label agreement using a leave-one-out strategy. By establishing pseudo-labels as ground trust for quantitative performance assessment, we observed that a strategically designed single prompting achieves comparable performance to more resource-intensive methods. This suggests a feasible approach applicable in real-world settings with inherent constraints.

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Misalignment Attack on Text-to-Image Models via Text Embedding Optimization and Inversion
Zhijie Du | Daizong Liu | Pan Zhou

Text embedding serves not only as a core component of modern NLP models but also plays a pivotal role in multimodal systems such as text-to-image (T2I) models, significantly facilitating user-friendly image generation through natural language instructions. However, with the convenience being brought, it also introduces additional risks. Misalignment issues of T2I models, whether caused by unintentional user inputs or targeted attacks, can negatively impact the reliability and ethics of these models. In this paper, we introduce TEOI, which fully considers the continuity and distribution characteristics of text embeddings. The framework directly optimizes the embeddings using gradient-based methods and then inverts them to obtain misaligned prompts of discrete tokens. The TEOI framework is capable of conducting both text-modal and multimodal misalignment attacks, revealing the vulnerabilities of multimodal models that rely on text embeddings. Our work highlights the potential risks associated with embedding-based text representations in prevailing T2I models and provides a foundation for further research into robust and secure text-to-image generation systems.

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Domain Pre-training Impact on Representations
Cesar Gonzalez-Gutierrez | Ariadna Quattoni

This empirical study analyzes how the choice of pre-training corpus affects the quality of learned transformer representations. We focus specifically on the representation quality achieved through pre-training alone. Our experiments demonstrate that pre-training on a small, specialized corpus can produce effective representations, and that the effectiveness of combining a generic and a specialized corpora depends on the distributional similarity between the target task and the specialized corpus.

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KoACD: The First Korean Adolescent Dataset for Cognitive Distortion Analysis via Role-Switching Multi-LLM Negotiation
Jun Seo Kim | Hye Hyeon Kim

Cognitive distortion refers to negative thinking patterns that can lead to mental health issues like depression and anxiety in adolescents. Previous studies using natural language processing (NLP) have focused mainly on small-scale adult datasets, with limited research on adolescents. This study introduces KoACD, the first large-scale dataset of cognitive distortions in Korean adolescents, containing 108,717 instances. We applied a multi-Large Language Model (LLM) negotiation method to refine distortion classification, enabling iterative feedback and role-switching between models to reduce bias and improve label consistency. In addition, we generated synthetic data using two approaches: cognitive clarification for textual clarity and cognitive balancing for diverse distortion representation. Validation through LLMs and expert evaluations showed that while LLMs classified distortions with explicit markers, they struggled with context-dependent reasoning, where human evaluators demonstrated higher accuracy. KoACD aims to enhance future research on cognitive distortion detection. The dataset and implementation details are publicly accessible.

As machine translation systems approach human-level quality, traditional evaluation methodologies struggle to detect subtle translation errors. We critically examine limitations in current gold-standard approaches (MQM and ESA), including inconsistencies from variable annotator expertise, excessive categorization complexity, coarse severity granularity, accuracy bias over fluency, and time constraints. To address this issue, we introduce a high-quality dataset consisting of human evaluations for English–Russian translations from WMT24, created by professional linguists. We show that expert assessments without time pressure yield substantially different results from standard evaluations. To enable consistent and rich annotation by these experts, we developed the RATE (Refined Assessment for Translation Evaluation) protocol. RATE provides a streamlined error taxonomy, expanded severity ratings, and multidimensional scoring balancing accuracy and fluency, facilitating deeper analysis of MT outputs. Our analysis, powered by this expert dataset, reveals that state-of-the-art MT systems may have surpassed human translations in accuracy while still lagging in fluency – a critical distinction obscured by existing accuracy-biased metrics. Our findings highlight that advancing MT evaluation requires not only better protocols but crucially, high-quality annotations from skilled linguists.

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Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue
Sangyeop Kim | Yohan Lee | Sanghwa Kim | Hyunjong Kim | Sungzoon Cho

Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response generation, making performance significantly dependent on model sizes. We introduce PREMem (Pre-storage Reasoning for Episodic Memory), a novel approach that shifts complex reasoning processes from inference to memory construction. PREMem extracts fine-grained memory fragments categorized into factual, experiential, and subjective information; it then establishes explicit relationships between memory items across sessions, capturing evolution patterns like extensions, transformations, and implications. By performing this reasoning during pre-storage rather than when generating a response, PREMem creates enriched representations while reducing computational demands during interactions. Experiments show significant performance improvements across all model sizes, with smaller models achieving results comparable to much larger baselines while maintaining effectiveness even with constrained token budgets. Code and dataset are available at https://github.com/sangyeop-kim/PREMem.

Verification is crucial for effective mathematical reasoning. We present a new temporal consistency method where verifiers iteratively refine their judgments based on the previous assessment. Unlike one-round verification or multi-model debate approaches, our method leverages consistency in a sequence of self-reflection actions to improve verification accuracy. Empirical evaluations across diverse mathematical process error identification benchmarks (Mathcheck, ProcessBench, and PRM800K) show consistent performance improvements over baseline methods. When applied to the recent DeepSeek R1 distilled models, our method demonstrates strong performance, enabling 7B/8B distilled models to outperform all 70B/72B models and GPT-4o on ProcessBench. Notably, the distilled 14B model with our method achieves performance comparable to Deepseek-R1.

For language models to generalize correctly to novel expressions, it is critical that they exploit access compositional meanings when this is justified. Even if we don’t know what a “pelp” is, we can use our knowledge of numbers to understand that “ten pelps” makes more pelps than “two pelps”. Static word embeddings such as Word2vec made strong, indeed excessive, claims about compositionality. The SOTA generative, transformer models and graph models, however, go too far in the other direction by providing no real limits on shifts in meaning due to context. To quantify the additive compositionality, we formalize a two-step, generalized evaluation that (i) measures the linearity between known entity attributes and their embeddings via canonical correlation analysis, and (ii) evaluates additive generalization by reconstructing embeddings for unseen attribute combinations and checking reconstruction metrics such as L2 loss, cosine similarity, and retrieval accuracy. These metrics also capture failure cases where linear composition breaks down. Sentences, knowledge graphs, and word embeddings are evaluated and tracked the compositionality across all layers and training stages. Stronger compositional signals are observed in later training stages across data modalities, and in deeper layers of the transformer-based model before a decline at the top layer. Code will be publicly available on GitHub upon acceptance.

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Presumed Cultural Identity: How Names Shape LLM Responses
Siddhesh Milind Pawar | Arnav Arora | Lucie-Aimée Kaffee | Isabelle Augenstein

Names are deeply tied to human identity - they can serve as markers of individuality, cultural heritage, and personal history. When interacting with LLMs, user names can enter chatbot conversations through direct user input (requested by chatbots), as part of task contexts such as CV reviews, or as built-in memory features that store user information for personalisation. In this work, we study name-based cultural bias by analyzing the adaptations that LLMs make when names are mentioned in the prompt. Our analyses demonstrate that LLMs exhibit significant cultural identity assumptions across multiple cultures based on users’ presumed backgrounds based on their names. We also show how using names as an indicator of identity can lead to misattribution and flattening of cultural identities. Our work has implications for designing more nuanced personalisation systems that avoid reinforcing stereotypes while maintaining meaningful customisation.

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I-GUARD: Interpretability-Guided Parameter Optimization for Adversarial Defense
Mamta Mamta | Oana Cocarascu

Transformer-based models are highly vulnerable to adversarial attacks, where even small perturbations can cause significant misclassifications. This paper introduces *I-Guard*, a defense framework to increase the robustness of transformer-based models against adversarial perturbations. *I-Guard* leverages model interpretability to identify influential parameters responsible for adversarial misclassifications. By selectively fine-tuning a small fraction of model parameters, our approach effectively balances performance on both original and adversarial test sets. We conduct extensive experiments on English and code-mixed Hinglish datasets and demonstrate that *I-Guard* significantly improves model robustness. Furthermore, we demonstrate the transferability of *I-Guard* in handling other character-based perturbations.

Recent advances in Emotional Support Conversation (ESC) have improved emotional support generation by fine-tuning Large Language Models (LLMs) via Supervised Fine-Tuning (SFT). However, common psychological errors still persist. While Direct Preference Optimization (DPO) shows promise in reducing such errors through pairwise preference learning, its effectiveness in ESC tasks is limited by two key challenges: (1) Entangled data structure: Existing ESC data inherently entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs; and (2) Optimization ambiguity: Applying vanilla DPO to such entangled pairwise data leads to ambiguous training objectives. To address these issues, we introduce Inferential Preference Mining (IPM) to construct high-quality preference data, forming the IPM-PrefDial dataset. Building upon this data, we propose a Decoupled ESC framework inspired by Gross’s Extended Process Model of Emotion Regulation, which decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation. Each was trained via SFT and subsequently enhanced by DPO to align with the psychological preference. Extensive experiments demonstrate that our Decoupled ESC framework outperforms baselines, reducing preference bias and improving response quality.

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Local Normalization Distortion and the Thermodynamic Formalism of Decoding Strategies for Large Language Models
Tom Kempton | Stuart Burrell

Advances in hardware and language model architecture have spurred a revolution in natural language generation. However, autoregressive models compute probability distributions over next-token choices, and sampling from these distributions, known as decoding, has received significantly less attention than other design choices. Existing decoding strategies are largely based on heuristics, resulting in methods that are difficult to apply or improve in a principled manner. We develop the theory of decoding strategies for language models by expressing popular decoding algorithms as equilibrium states in the language of ergodic theory and stating the objective functions they optimize. Using this, we analyze the effect of the local normalization step required to make probabilities sum to one in top-k, nucleus, and temperature sampling. We argue that local normalization distortion is a fundamental defect of decoding strategies and quantify the size of this distortion and its effect on mathematical proxies for the quality and diversity of generated text. This yields conclusions for the design of decoding algorithms and the detection of machine-generated text.

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BRIT: Bidirectional Retrieval over Unified Image-Text Graph
Ainulla Khan | Moyuru Yamada | Srinidhi Akella

Retrieval-Augmented Generation (RAG) has emerged as a promising technique to enhance the quality and relevance of responses generated by large language models. While recent advancements have mainly focused on improving RAG for text-based queries, RAG on multi-modal documents containing both texts and images has not been fully explored. Especially when fine-tuning does not work. This paper proposes BRIT, a novel multi-modal RAG framework that effectively unifies various text-image connections in the document into a multi-modal graph and retrieves the texts and images as a query-specific sub-graph. By traversing both image-to-text and text-to-image paths in the graph, BRIT retrieve not only directly query-relevant images and texts but also further relevant contents to answering complex cross-modal multi-hop questions. To evaluate the effectiveness of BRIT, we introduce MM-RAG test set specifically designed for multi-modal question answering tasks that require to understand the text-image relations. Our comprehensive experiments demonstrate the superiority of BRIT, highlighting its ability to handle cross-modal questions on the multi-modal documents.

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ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking
Boyoung Kim | Dosung Lee | Sumin An | Jinseong Jeong | Paul Hongsuck Seo

Recent advances in question answering have led to substantial progress in tasks such as multi-hop reasoning. However, global sensemaking—answering questions by synthesizing information from an entire corpus—remains a significant challenge. A prior graph-basedapproach to global sensemaking lacks retrieval mechanisms, topic specificity, and incurs high inference costs. To address these limitations, we propose ReTAG, a RetrievalEnhanced, Topic-Augmented Graph framework that constructs topic-specific subgraphs and retrieves the relevant summaries for response generation. Experiments show that ReTAG improves response quality while significantly reducing inference time compared to the baseline. Our code is available at https://github.com/bykimby/retag.

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Capturing Latent Modal Association For Multimodal Entity Alignment
Yongquan Ji | Jingwei Cheng | Fu Zhang | Chenglong Lu

Multimodal entity alignment aims to identify equivalent entities in heterogeneous knowledge graphs by leveraging complementary information from multiple modalities. However, existing methods often overlook the quality of input modality embeddings during modality interaction – such as missing modality generation, modal information transfer, modality fusion – which may inadvertently amplify noise propagation while suppressing discriminative feature representations. To address these issues, we propose a novel model – CLAMEA for capturing latent modal association for multimodal entity alignment. Specifically, we use a self- attention mechanism to enhance salient information while attenuating noise within individual modality embeddings. We design a dynamic modal attention flow fusion module to capture and balance latent intra- and inter-modal associations and generate fused modality embeddings. Based on both fused and available modalities, we adopt variational autoencoder (VAE) to generate high quality embeddings for the missing modality. We use a cross-modal association extraction module to extract latent modal associations from the completed modality embeddings, further enhancing embedding quality. Experimental results on two real-world datasets demonstrate the effectiveness of our approach, which achieves an absolute 3.1% higher Hits@ 1 score than the sota method.

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Explaining novel senses using definition generation with open language models
Mariia Fedorova | Andrey Kutuzov | Francesco Periti | Yves Scherrer

We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL’24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts.

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Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching
Seoyeon Kim | Huiseo Kim | Chanjun Park | Jinyoung Yeo | Dongha Lee

Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora. The limited resource for low-resource languages remains a crucial challenge. Code-switching (CS), a phenomenon where multilingual speakers alternate between languages in a discourse, can convey subtle cultural and linguistic nuances that can be otherwise lost in translation and elicits language-specific knowledge in human communications. In light of this, we investigate whether code-switching can activate, or identify and leverage knowledge for reasoning when LLMs solve low-resource language tasks. To facilitate the research, we first present EnKoQA, a synthetic English-Korean CS question-answering dataset. We provide comprehensive analysis on a variety of multilingual LLMs by subdividing activation process into knowledge identification and knowledge leveraging. Our results demonstrate that compared to English text, CS can faithfully activate knowledge inside LLMs especially on language-specific domains, suggesting the potential of code-switching on low-resource language tasks.

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Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation
Armel Randy Zebaze | Benoît Sagot | Rachel Bawden

The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. Machine Translation (MT) has been shown to benefit from in-context examples, in particular when they are semantically similar to the sentence to translate. In this paper, we propose a new LLM-based translation paradigm, compositional translation, to replace naive few-shot MT with similarity-based demonstrations. An LLM is used to decompose a sentence into simpler phrases, and then to translate each phrase with the help of retrieved demonstrations. Finally, the LLM is prompted to translate the initial sentence with the help of the self-generated phrase-translation pairs. Our intuition is that this approach should improve translation because these shorter phrases should be intrinsically easier to translate and easier to match with relevant examples. This is especially beneficial in low-resource scenarios, and more generally whenever the selection pool is small or out of domain. We show that compositional translation boosts LLM translation performance on a wide range of popular MT benchmarks, including FLORES200, NTREX 128 and TICO-19. Code and outputs will be made freely available.

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TopXGen: Topic-Diverse Parallel Data Generation for Low-Resource Machine Translation
Armel Randy Zebaze | Benoît Sagot | Rachel Bawden

LLMs have been shown to perform well in machine translation (MT) with the use of in-context learning, rivalling supervised models when translating into high-resource languages (HRLs). However, they lag behind when dealing with low-resource language (LRLs). Example selection via similarity search and supervised fine-tuning help. However the improvements they give are limited by the size, quality and diversity of existing parallel datasets. A common technique in low-resource MT is synthetic parallel data creation, the most frequent of which is backtranslation, whereby existing target-side texts are automatically translated into the source language. However, it also relies on the existence of good quality and relevant target-side texts, which are not readily available for many LRLs. In this paper, we present a new approach, TopXGen, which involves using an LLM to automatically generate topic-specific target-side data in the LRL, which can then be backtranslated to produce useful and diverse parallel texts for ICL and fine-tuning. Our intuition is that while LLMs struggle to translate into LRLs, their ability to translate well into HRLs and their multilinguality enable them to generate good quality, natural-sounding target-side texts, which can be translated well into a high-resource source language. We show that TopXGen boosts LLM translation performance during fine-tuning and in-context learning. Our code and outputs will be made freely available.

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Fast, Not Fancy: Rethinking G2P with Rich Data and Statistical Models
Mahta Fetrat Qharabagh | Zahra Dehghanian | Hamid R. Rabiee

Homograph disambiguation remains a significant challenge in grapheme-to-phoneme (G2P) conversion, especially for low-resource languages. This challenge is twofold: (1) creating balanced and comprehensive homograph datasets is labor-intensive and costly, and (2) specific disambiguation strategies introduce additional latency, making them unsuitable for real-time applications such as screen readers and other accessibility tools. In this paper, we address both issues. First, we propose a semi-automated pipeline for constructing homograph-focused datasets, introduce the HomoRich dataset generated through this pipeline, and demonstrate its effectiveness by applying it to enhance a state-of-the-art deep learning-based G2P system for Persian. Second, we advocate for a paradigm shift—utilizing rich offline datasets to inform the development of fast, statistical methods suitable for latency-sensitive accessibility applications like screen readers. To this end, we improve one of the most well-known rule-based G2P systems, eSpeak, into a fast homograph-aware version, HomoFast eSpeak. Our results show an approximate 30 percentage-point improvement in homograph disambiguation accuracy for the deep learning-based and eSpeak systems.

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Personalized open world plan generation for safety-critical human centered autonomous systems: A case study on Artificial Pancreas
Ayan Banerjee | Sandeep Gupta

Design-time safety guarantees for human-centered autonomous systems (HCAS) often break down in open-world deployment due to uncertain human interaction. In practice, HCAS must follow a user-personalized safety plan, with the human providing external inputs to handle out-of-distribution events. Open-world safety planning for HCAS demands modeling dynamical systems, exploring novel actions, and rapid replanning when plans are invalidated or dynamics shift. No single state-of-the-art planner meets all these needs. We introduce an LLM-based architecture that automatically generates personalized safety plans. By itself, the LLM fares poorly at producing safe usage plans, but coupling it with a safety verifier—which evaluates plan safety over the planning horizon and feeds back quality scores—enables the discovery of safe plans. Moreover, fine-tuning the LLM on personalized models inferred from open-world data further enhances plan quality. We validate our approach by generating safe usage plans for artificial pancreas systems in automated insulin delivery for Type 1 Diabetes patients. Code: https://github.com/ImpactLabASU/LLMOpen

Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural context in multimodal translation. We introduce CaMMT, a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. Using this dataset, we evaluate five Vision Language Models (VLMs) in text-only and text+image settings. Through automatic and human evaluations, we find that visual context generally improves translation quality, especially in handling Culturally-Specific Items (CSIs), disambiguation, and correct gender marking. By releasing CaMMT, our objective is to support broader efforts to build and evaluate multimodal translation systems that are better aligned with cultural nuance and regional variations.

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Training Text-to-Molecule Models with Context-Aware Tokenization
Seojin Kim | Hyeontae Song | Jaehyun Nam | Jinwoo Shin

Recently, text-to-molecule models have shown great potential across various chemical applications, e.g., drug-discovery. These models adapt language models to molecular data by representing molecules as sequences of atoms. However, they rely on atom-level tokenizations, which primarily focus on modeling local connectivity, thereby limiting the ability of models to capture the global structural context within molecules. To tackle this issue, we propose a novel text-to-molecule model, coined Context-Aware Molecular T5 (CAMT5). Inspired by the significance of the substructure-level contexts in understanding molecule structures, e.g., ring systems, we introduce substructure-level tokenization for text-to-molecule models. Building on our tokenization scheme, we develop an importance-based training strategy that prioritizes key substructures, enabling CAMT5 to better capture the molecular semantics. Extensive experiments verify the superiority of CAMT5 in various text-to-molecule generation tasks. Intriguingly, we find that CAMT5 outperforms the state-of-the-art methods using only 2% of training tokens. In addition, we propose a simple yet effective ensemble strategy that aggregates the outputs of text-to-molecule models to further boost the generation performance.

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Challenging the Evaluator: LLM Sycophancy Under User Rebuttal
Sung Won Kim | Daniel Khashabi

Large Language Models (LLMs) often exhibit sycophancy, distorting responses to align with user beliefs, notably by readily agreeing with user counterarguments. Paradoxically, LLMs are increasingly adopted as successful evaluative agents for tasks such as grading and adjudicating claims. This research investigates that tension: why do LLMs show sycophancy when challenged in subsequent conversational turns, yet perform well when evaluating conflicting arguments presented simultaneously? We empirically tested these contrasting scenarios by varying key interaction patterns. We find that state-of-the-art models: (1) are more likely to endorse a user’s counterargument when framed as a follow-up from a user, rather than when both responses are presented simultaneously for evaluation; (2) show increased susceptibility to persuasion when the user’s rebuttal includes detailed reasoning, even when the conclusion of the reasoning is incorrect; and (3) are more readily swayed by casually phrased feedback than by formal critiques, even when the casual input lacks justification. Our results highlight the risk of relying on LLMs for judgment tasks without accounting for conversational framing.

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Perspective-driven Preference Optimization with Entropy Maximization for Diverse Argument Generation
Yilin Cao | Ruike Zhang | Penghui Wei | Qingchao Kong | Wenji Mao

In subjective natural language generation tasks, generating diverse perspectives is essential for fostering balanced discourse and mitigating bias. Argument generation with diverse perspectives plays a vital role in advancing the understanding of controversial claims. Despite the strong generative capabilities of large language models (LLMs), the diversity of perspectives remains insufficiently explored within argument generation task. Moreover, there remains a significant research gap in developing methods that explicitly generate multi-perspective arguments under the quality control of claim-stance alignment constraints. In this paper, we propose POEM, a Perspective-aware Preference Optimization with Entropy Maximization framework for diverse argument generation. It enhances perspective diversity through preference optimization based on the constructed preference dataset via perspective mining and diversity measuring. It further introduces entropy maximization to promote perspective diversity by encouraging dispersed semantic representations among the generated arguments. Experimental results on claim-stance argument generation benchmarks show that POEM is capable of generating diverse arguments while maintaining comparable performances in claim and stance controllability as well as text quality compared to the state-of-the-art baselines and human evaluation.

Speakers of unwritten languages have the potential to benefit from speech-based automatic information retrieval systems. This paper proposes a speech embedding technique that facilitates such a system that we can be used in a zero-shot manner on the target language. After conducting development experiments on several written Indic languages, we evaluate our method on a corpus of Gormati – an unwritten language – that was previously collected in partnership with an agrarian Banjara community in Maharashtra State, India, specifically for the purposes of information retrieval. Our system achieves a Top 5 retrieval rate of 87.9% on this data, giving the hope that it may be useable by unwritten language speakers worldwide.

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M-Help: Using Social Media Data to Detect Mental Health Help-Seeking Signals
Msvpj Sathvik | Zuhair Hasan Shaik | Vivek Gupta

Mental health disorders are a global crisis. While various datasets exist for detecting such disorders, there remains a critical gap in identifying individuals actively seeking help. This paper introduces a novel dataset, M-Help, specifically designed to detect help-seeking behavior on social media. The dataset goes beyond traditional labels by identifying not only help-seeking activity but also specific mental health disorders and their underlying causes, such as relationship challenges or financial stressors. AI models trained on M-Help can address three key tasks: identifying help-seekers, diagnosing mental health conditions, and uncovering the root causes of issues.

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Brittle Minds, Fixable Activations: Understanding Belief Representations in Language Models
Matteo Bortoletto | Constantin Ruhdorfer | Lei Shi | Andreas Bulling

Despite growing interest in Theory of Mind (ToM) tasks for evaluating language models (LMs), little is known about how LMs internally represent mental states of self and others. Understanding these internal mechanisms is critical - not only to move beyond surface-level performance, but also for model alignment and safety, where subtle misattributions of mental states may go undetected in generated outputs. In this work, we present the first systematic investigation of belief representations in LMs by probing models across different scales, training regimens, and prompts - using control tasks to rule out confounds. Our experiments provide evidence that both model size and fine‐tuning substantially improve LMs’ internal representations of others’ beliefs, which are structured - not mere by-products of spurious correlations - yet brittle to prompt variations. Crucially, we show that these representations can be strengthened: targeted edits to model activations can correct wrong ToM inferences.

As large language models (LLMs) increasingly permeate the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. Existing financial benchmarks often suffer from limited language and task coverage, low-quality datasets, and inadequate adaptability for LLM evaluation. To address these limitations, we introduce Golden Touchstone, a comprehensive bilingual benchmark for financial LLMs, encompassing eight core financial NLP tasks in both Chinese and English. Developed from extensive open-source data collection and industry-specific demands, this benchmark thoroughly assesses models’ language understanding and generation capabilities. Through comparative analysis of major models such as GPT-4o, Llama3, FinGPT, and FinMA, we reveal their strengths and limitations in processing complex financial information. Additionally, we open-source Touchstone-GPT, a financial LLM trained through continual pre-training and instruction tuning, which demonstrates strong performance on the bilingual benchmark but still has limitations in specific tasks. This research provides a practical evaluation tool for financial LLMs and guides future development and optimization.The source code for Golden Touchstone and model weight of Touchstone-GPT have been made publicly available at https://github.com/IDEA-FinAI/Golden-Touchstone.

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Quantifying the Risks of LLM- and Tool-assisted Rephrasing to Linguistic Diversity
Mengying Wang | Andreas Spitz

Writing assistants and large language models see widespread use in the creation of text content. While their effectiveness for individual users has been evaluated in the literature, little is known about their proclivity to change language or reduce its richness when adopted by a large user base. In this paper, we take a first step towards quantifying this risk by measuring the semantic and vocabulary change enacted by the use of rephrasing tools on a multi-domain corpus of human-generated text.

Recent advancements in 2D multimodal large language models (MLLMs) have significantly improved performance in vision-language tasks. However, extending these capabilities to 3D environments remains a distinct challenge due to the complexity of spatial reasoning. Nevertheless, existing 3D benchmarks often lack fine-grained numerical reasoning task annotations, limiting MLLMs’ ability to perform precise spatial measurements and complex numerical reasoning. To address this gap, we introduce NUMINA, the first Natural Understanding benchmark for Multi-dimensional Intelligence and Numerical reasoning Abilities to enhance multimodal indoor perceptual understanding. NUMINA features multi-scale annotations and various question-answer pairs, generated using NUMINA-Flow, an automated annotation pipeline that integrates LLM rewriting and rule-based self-verification. We evaluate the performance of various state-of-the-art LLMs on NUMINA following the Chat-Scene framework, demonstrating that current LLMs struggle with multimodal numerical reasoning, particularly in performing precise computations such as distance and volume estimation, highlighting the need for further advancements in 3D models. The dataset and source codes can be obtained from https://github.com/fengshun124/NUMINA.

Understanding Theory of Mind is essential for building socially intelligent multimodal agents capable of perceiving and interpreting human behavior. We introduce MoMentS (Multimodal Mental States), a comprehensive benchmark designed to assess the ToM capabilities of multimodal large language models (LLMs) through realistic, narrative-rich scenarios presented in short films. MoMentS includes over 2,300 multiple-choice questions spanning seven distinct ToM categories. The benchmark features long video context windows and realistic social interactions that provide deeper insight into characters’ mental states. We evaluate several MLLMs and find that although vision generally improves performance, models still struggle to integrate it effectively. For audio, models that process dialogues as audio do not consistently outperform transcript-based inputs. Our findings highlight the need to improve multimodal integration and point to open challenges that must be addressed to advance AI’s social understanding.

In medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures. We introduce ‘Code Like Humans’: a new agentic framework for medical coding with large language models. It implements official coding guidelines for human experts, and it is the first solution that can support the full ICD-10 coding system (+70K labels). It achieves the best performance to date on rare diagnosis codes. Fine-tuned discriminative classifiers retain an advantage for high-frequency codes, to which they are limited. Towards future work, we also contribute an analysis of system performance and identify its ‘blind spots’ (codes that are systematically undercoded).

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Can Out-of-Distribution Evaluations Uncover Reliance on Prediction Shortcuts? A Case Study in Question Answering
Michal Štefánik | Timothee Mickus | Michal Spiegel | Marek Kadlčík | Josef Kuchař

A large body of recent work assesses models’ generalization capabilities through the lens of performance on out-of-distribution (OOD) datasets. Despite their practicality, such evaluations build upon a strong assumption: that OOD evaluations can capture and reflect upon possible failures in a real-world deployment. In this work, we challenge this assumption and confront the results obtained from OOD evaluations with a set of specific failure modes documented in existing question-answering (QA) models, referred to as a reliance on spurious features or prediction shortcuts.We find that different datasets used for OOD evaluations in QA provide an estimate of models’ robustness to shortcuts that have a vastly different quality, some largely under-performing even a simple, in-distribution evaluation. We partially attribute this to the observation that spurious shortcuts are shared across ID+OOD datasets, but also find cases where a dataset’s quality for training and evaluation is largely disconnected. Our work underlines limitations of commonly-used OOD-based evaluations of generalization, and provides methodology and recommendations for evaluating generalization within and beyond QA more robustly.

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MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation
Shoubin Yu | Yue Zhang | Ziyang Wang | Jaehong Yoon | Mohit Bansal

Combining pre-trained expert models offers substantial potential for scalable multimodal reasoning, but building a unified framework remains challenging due to the increasing diversity of input modalities and task complexity. For instance, medical diagnosis requires precise reasoning over structured clinical tables, while financial forecasting depends on interpreting plot-based data to make informed predictions. To tackle this challenge, we introduce MEXA, a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse and distinct domains. MEXA dynamically selects expert models based on the input modality and the task-specific reasoning demands (i.e., skills). Each expert model, specialized in a modality task pair, generates interpretable textual reasoning outputs. MEXA then aggregates and reasons over these outputs using a Large Reasoning Model (LRM) to produce the final answer. This modular design allows flexible and transparent multimodal reasoning across diverse domains without additional training overhead. We extensively evaluate our approach on diverse multimodal benchmarks, including Video Reasoning, Audio Reasoning, 3D Understanding, and Medical QA. MEXA consistently delivers performance improvements over strong multimodal baselines, highlighting the effectiveness and broad applicability of our expert-driven selection and aggregation in diverse multimodal reasoning tasks.

Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. In this paper, we formalize the popular locate-then-edit methods as a two-step fine-tuning process, allowing us to precisely identify the root cause of this degradation. We show that model degradation occurs due to (1) over-optimization of internal activations and (2) continuous norm-growth of edited matrices. To mitigate these issues, we introduce two regularization techniques: (1) Most-Probable Early Stopping (MPES) and (2) explicit Frobenius norm-constraint. We demonstrate that applying these simple yet effective regularization techniques at key points in the editing process can substantially mitigate model degradation. Combining these regularization methods enables scaling locate-then-edit methods to 10,000 edits while reducing editing time by 42-61%. These results show that targeted regularization is essential for lifelong knowledge editing.

Vision–language models (VLMs) often process visual inputs through a pretrained vision encoder, followed by a projection into the language model’s embedding space via a connector component. While crucial for modality fusion, the potential information loss induced by this projection step and its direct impact on model capabilities remain understudied. We introduce two complementary approaches to examine and quantify this loss by analyzing the latent representation space. First, we evaluate semantic information preservation by analyzing changes in k-nearest neighbor relationships between image representations, before and after projection. Second, we directly measure information loss by reconstructing visual embeddings from the projected representation, localizing loss at an image patch level. Experiments reveal that connectors substantially distort the local geometry of visual representations, with k-nearest neighbors diverging by 40–60% post-projection, correlating with degradation in retrieval performance. The patch-level embedding reconstruction provides interpretable insights for model behavior on visually grounded question-answering tasks, finding that areas of high information loss reliably predict instances where models struggle.

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Assessing the Role of Data Quality in Training Bilingual Language Models
Skyler Seto | Maartje Ter Hoeve | Maureen de Seyssel | David Grangier

Bilingual and multilingual language models offer a promising path toward scaling NLP systems across diverse languages and users. However, their performance often varies wildly between languages as prior works show that adding more languages can degrade performance for some languages (such as English), while improving others (typically more data constrained languages). In this work, we investigate causes of these inconsistencies by comparing bilingual and monolingual language models. Our analysis reveals that unequal data quality, not just data quantity, is a major driver of performance degradation in bilingual settings. We propose a simple yet effective data filtering strategy to select higher-quality bilingual training data with only high quality English data. Applied to French, German, and Chinese, our approach improves monolingual performance by 2–4% and reduces bilingual model performance gaps to 1%. These results highlight the overlooked importance of data quality in multilingual pretraining and offer a practical recipe for balancing performance.

Aligning large language models (LLMs) with human preferences relies heavily on high-quality reward models. However, existing approaches struggle with two critical challenges: noisy preference labels and the varying importance of preference samples. We introduce DORM, a method that enhances reward modeling by learning to dynamically weigh preference data.DORM initializes data importance using a combination of model uncertainty and prediction disagreement, then iteratively refines them via bilevel optimization to maximize validation performance. Using only 50k samples, DORM trains a 12B reward model that achieves 90.5% accuracy on RewardBench, matching the performance of models trained on significantly larger datasets. Furthermore, downstream alignment tasks show that fine-tuned LLMs with DORM achieve a 61.2% win rate against baseline methods, highlighting its data efficiency and generalizability.

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Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them
Marc Felix Brinner | Tarek Al Mustafa | Sina Zarrieß

We investigate the use of LLM-generated data for continual pretraining of transformer encoder models in specialized domains with limited training data, using the scientific domain of invasion biology as a case study. To this end, we leverage domain-specific ontologies by enriching them with LLM-generated data and pretraining the encoder model as an ontology-informed embedding model for concept definitions. To evaluate the effectiveness of this method, we compile a benchmark specifically designed for assessing model performance in invasion biology. After demonstrating substantial improvements over standard MLM pretraining, we investigate the feasibility of applying the proposed approach to domains without comprehensive ontologies by substituting ontological concepts with concepts automatically extracted from a small corpus of scientific abstracts and establishing relationships between concepts through distributional statistics. Our results demonstrate that this automated approach achieves comparable performance using only a small set of scientific abstracts, resulting in a fully automated pipeline for enhancing domain-specific understanding of small encoder models that is especially suited for application in low-resource settings and achieves performance comparable to masked language modeling pretraining on much larger datasets.

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Aligning Dialogue Agents with Global Feedback via Large Language Model Multimodal Reward Decomposition
Dong Won Lee | Hae Won Park | Cynthia Breazeal | Louis-Philippe Morency

We propose a large language model based reward decomposition framework for aligning dialogue agents using only a single session-level feedback signal. We leverage the reasoning capabilities of a frozen, pretrained large language model (LLM) to infer fine-grained local implicit rewards by decomposing global, session-level feedback. Our first text-only variant prompts the LLM to perform reward decomposition using only the dialogue transcript. The second multimodal variant incorporates additional behavioral cues, such as pitch, gaze, and facial affect, expressed as natural language descriptions. These inferred turn-level rewards are distilled into a lightweight reward model, which we utilize for RL-based fine-tuning for dialogue generation. We evaluate both text-only and multimodal variants against state-of-the-art reward decomposition methods and demonstrate notable improvements in human evaluations of conversation quality, suggesting that LLMs are strong reward decomposers that obviate the need for manual reward shaping and granular human feedback.

The rapid adoption of Large Language Models (LLMs) has raised important concerns about the factual reliability of their outputs, particularly in low-resource languages such as Urdu. Existing automated fact-checking systems are predominantly developed for English, leaving a significant gap for the more than 200 million Urdu speakers worldwide. In this work, we present UrduFactBench and UrduFactQA, two novel hand-annotated benchmarks designed to enable fact-checking and factual consistency evaluation in Urdu. While UrduFactBench focuses on claim verification, UrduFactQA targets the factuality of LLMs in question answering. These resources, the first of their kind for Urdu, were developed through a multi-stage annotation process involving native Urdu speakers. To complement these benchmarks, we introduce UrduFactCheck, a modular fact-checking framework that incorporates both monolingual and translation-based evidence retrieval strategies to mitigate the scarcity of high-quality Urdu evidence. Leveraging these resources, we conduct an extensive evaluation of twelve LLMs and demonstrate that translation-augmented pipelines consistently enhance performance compared to monolingual ones. Our findings reveal persistent challenges for open-source LLMs in Urdu and underscore the importance of developing targeted resources. All code and data are publicly available at https://github.com/mbzuai-nlp/UrduFactCheck.

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Echoes of Agreement: Argument Driven Sycophancy in Large Language models
Avneet Kaur

Existing evaluation of political biases in Large Language Models (LLMs) outline the high sensitivity to prompt formulation. Furthermore, Large Language Models are known to exhibit sycophancy, a tendency to align their outputs with a user’s stated belief, which is often attributed to human feedback during fine-tuning. However, such bias in the presence of explicit argumentation within a prompt remains underexplored. This paper investigates how argumentative prompts induce sycophantic behaviour in LLMs in a political context. Through a series of experiments, we demonstrate that models consistently alter their responses to mirror the stance present expressed by the user. This sycophantic behaviour is observed in both single and multi-turn interactions, and its intensity correlates with argument strength. Our findings establish a link between user stance and model sycophancy, revealing a critical vulnerability that impacts model reliability. Thus has significant implications for models being deployed in real-world settings and calls for developing robust evaluations and mitigations against manipulative or biased interactions.

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Rethinking NLP for Chemistry: A Critical Look at the USPTO Benchmark
Derin Ozer | Nicolas Gutowski | Benoit Da Mota | Thomas Cauchy | Sylvain Lamprier

Natural Language Processing (NLP) has catalyzed a paradigm shift in Computer-Aided Synthesis Planning (CASP), reframing chemical synthesis prediction as a sequence-to-sequence modeling problem over molecular string representations like SMILES. This framing has enabled the direct application of language models to chemistry, yielding impressive benchmark scores on the USPTO dataset, a large text corpus of reactions extracted from US patents. However, we show that USPTO’s patent-derived data are both industrially biased and incomplete. They omit many fundamental transformations essential for practical real-world synthesis. Consequently, models trained exclusively on USPTO perform poorly on simple, pharmaceutically relevant reactions despite high benchmark scores. Our findings highlight a broader concern in applying standard NLP pipelines to scientific domains without rethinking data and evaluation: models may learn dataset artifacts rather than domain reasoning. We argue for the development of chemically meaningful benchmarks, greater data diversity, and interdisciplinary dialogue between the NLP community and domain experts to ensure real-world applicability.

Like most languages, sign languages evolve over time. It is important that sign language dictionaries’ vocabularies are updated over time to reflect these changes, such as by adding new signs. However, most dictionary retrieval methods based upon machine learning models only work with fixed vocabularies, and it is unclear how they might support dictionary expansion without retraining. In this work, we explore the feasibility of dictionary expansion for sign language dictionaries using a simple representation-based method. We explore a variety of dictionary expansion scenarios, e.g., varying number of signs added as well as amount of data for these newly added signs. Through our results, we show how performance varies significantly across different scenarios, many of which are reflective of real-world data challenges. Our findings offer implications for the development & maintenance of video-based sign language dictionaries, and highlight directions for future research on dictionary expansion.

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From Insight to Exploit: Leveraging LLM Collaboration for Adaptive Adversarial Text Generation
Najrin Sultana | Md Rafi Ur Rashid | Kang Gu | Shagufta Mehnaz

LLMs can provide substantial zero-shot performance on diverse tasks using a simple task prompt, eliminating the need for training or fine-tuning. However, when applying these models to sensitive tasks, it is crucial to thoroughly assess their robustness against adversarial inputs. In this work, we introduce Static Deceptor (StaDec) and Dynamic Deceptor (DyDec), two innovative attack frameworks designed to systematically generate dynamic and adaptive adversarial examples by leveraging the understanding of the LLMs. We produce subtle and natural-looking adversarial inputs that preserve semantic similarity to the original text while effectively deceiving the target LLM. By utilizing an automated, LLM-driven pipeline, we eliminate the dependence on external heuristics. Our attacks evolve with the advancements in LLMs, while demonstrating a strong transferability across models unknown to the attacker. Overall, this work provides a systematic approach for self-assessing the robustness of the LLMs. We release our code and data at https://github.com/Shukti042/AdversarialExample.

Although synthetic data has changed various aspects of information retrieval (IR) pipelines, the main training paradigm remains: contrastive learning with binary relevance labels, where one positive document is compared against several negatives using the InfoNCE loss. This objective treats all documents that are not explicitly annotated as relevant on an equally negative footing, regardless of their actual degree of relevance, thus missing subtle nuances useful for ranking. To overcome this limitation, in this work, we forgo real documents and annotations and use large language models to directly generate synthetic documents that answer the MS MARCO queries according to _several different levels of relevance_. We also propose using Wasserstein distance as a more effective loss function for training transformer-based retrievers with graduated relevance labels. Our experiments on MS MARCO and BEIR benchmark show that our proposed approach outperforms conventional training with InfoNCE by a large margin. Without using any real documents, our method significantly improves self-supervised retrievers and is more robust to distribution shift compared to contrastive learning using real data. Our method also successfully integrates existing real data into the synthetic ranking context, further boosting the performance. Overall, we show that generating multi-level ranking contexts is a better approach to synthetic data generation for IR than just generating the standard positive and negative documents.

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Instability in Downstream Task Performance During LLM Pretraining
Yuto Nishida | Masaru Isonuma | Yusuke Oda

When training large language models (LLMs), it is common practice to track downstream task performance throughout the training process and select the checkpoint with the highest validation score.However, downstream metrics often exhibit substantial fluctuations, making it difficult to identify the checkpoint that truly represents the best-performing model.In this study, we empirically analyze the stability of downstream task performance in an LLM trained on diverse web-scale corpora.We find that task scores frequently fluctuate throughout training, both at the aggregate and example levels.To address this instability, we investigate two post-hoc checkpoint integration methods: checkpoint averaging and ensemble, motivated by the hypothesis that aggregating neighboring checkpoints can reduce performance volatility.We demonstrate both empirically and theoretically that these methods improve downstream performance stability without requiring any changes to the training procedure.

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A Comparison of Independent and Joint Fine-tuning Strategies for Retrieval-Augmented Generation
Neal Gregory Lawton | Alfy Samuel | Anoop Kumar | Daben Liu

Retrieval augmented generation (RAG) is a popular framework for question answering that is powered by two large language models (LLMs): an embedding model that retrieves context documents from a database that are relevant to a given question, and a generator model that uses the retrieved context to generate an answer to the question. Both the embedding and generator models can be fine-tuned to increase performance of a RAG pipeline on a new task, but multiple fine-tuning strategies exist with different costs and benefits. In this paper, we evaluate and compare several RAG fine-tuning strategies, including independent, joint, and two-phase fine-tuning. In our experiments, we observe that all of these strategies achieve about equal improvement in EM and F1 generation quality metrics, although they have significantly different computational costs. We conclude the optimal fine-tuning strategy to use depends on whether the training dataset includes context labels and whether a grid search over the learning rates for the embedding and generator models is required.

In collaborative creation tasks, people steer artifacts towards specific goals by _refining_ them with _multimodal_ communication over multiple rounds of interaction. In contrast, generative AI excels at creating artifacts in a single turn but can struggle to make precise refinements that match our design intent. To close this gap, we present mrCAD, a dataset of multi-turn interactions in which pairs of humans iteratively created and refined computer-aided designs (CADs). In each game, a _Designer sent instructions to a _Maker_, explaining how to create and subsequently refine a CAD to match a target design that only the _Designer_ could see. mrCAD consists of 6,082 communication games, 15,163 instruction-execution rounds, played between 1,092 pairs of human players. Crucially, _Designers_ had access to two communication modalities – text and drawing. Analysis finds that players relied more on text in refinement than in initial generation instructions, and used different linguistic elements for refinement than for generation. We also find that state-of-the-art VLMs are better at following generation instructions than refinement instructions. These results lay the foundation for modeling multi-turn, multimodal communication not captured in prior datasets.

Recent advancements in Large Language Models (LLMs) have significantly enhanced their code generation capabilities. However, their robustness against adversarial misuse, particularly through multi-turn malicious coding prompts, remains underexplored. In this work, we introduce code decomposition attacks, where a malicious coding task is broken down into a series of seemingly benign subtasks across multiple conversational turns to evade safety filters. To facilitate systematic evaluation, we introduce MOCHA, a large-scale benchmark designed to evaluate the robustness of code LLMs against both single-turn and multi-turn malicious prompts. Empirical results across open- and closed-source models reveal persistent vulnerabilities, especially under multi-turn scenarios. Fine-tuning on MOCHA improves rejection rates while preserving coding ability, and importantly, enhances robustness on external adversarial datasets with up to 32.4% increase in rejection rates without any additional supervision.

Recent advances in reasoning and planning capabilities of large language models (LLMs) have enabled their potential as autonomous agents capable of tool use in dynamic environments. However, in multi-turn conversational environments like 𝜏‐bench, these agents often struggle with consistent reasoning, adherence to domain-specific policies, and extracting correct information over a long horizon of tool-calls and conversation. To capture and mitigate these failures, we conduct a comprehensive manual analysis of the common errors occurring in the conversation trajectories. We then experiment with reformulations of inputs to the tool-calling agent for improvement in agent decision making. Finally, we propose the Input-Reformulation Multi-Agent (IRMA) framework, which automatically reformulates user queries augmented with relevant domain rules and tool suggestions for the tool-calling agent to focus on. The results show that IRMA significantly outperforms ReAct, Function Calling, and Self-Reflection by 16.1%, 12.7%, and 19.1%, respectively, in overall pass^5 scores. These findings highlight the superior reliability and consistency of IRMA compared to other methods in dynamic environments.

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Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts
Xuyang Wu | Yuan Wang | Hsin-Tai Wu | Zhiqiang Tao | Yi Fang

Large vision-language models (LVLMs) have recently achieved significant progress, demonstrating strong capabilities in open-world visual understanding. However, it is not yet clear how LVLMs address demographic biases in real life, especially the disparities across attributes such as gender, skin tone, age and race. In this paper, We empirically investigate visual fairness in several mainstream LVLMs by auditing their performance disparities across demographic attributes using public fairness benchmark datasets (e.g., FACET, UTKFace). Our fairness evaluation framework employs direct and single-choice question prompt on visual question-answering/classification tasks. Despite advancements in visual understanding, our zero-shot prompting results show that both open-source and closed-source LVLMs continue to exhibit fairness issues across different prompts and demographic groups. Furthermore, we propose a potential multi-modal Chain-of-thought (CoT) based strategy for unfairness mitigation, applicable to both open-source and closed-source LVLMs. This approach enhances transparency and offers a scalable solution for addressing fairness, providing a solid foundation for future research and practical efforts in unfairness mitigation. The dataset and code used in this study are publicly available at this GitHub Repository.

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VIBE: Can a VLM Read the Room?
Tania Chakraborty | Eylon Caplan | Dan Goldwasser

Understanding human social behavior such as recognizing emotions and the social dynamics causing them is an important and challenging problem. While LLMs have made remarkable advances, they are limited to the textual domain and cannot account for the major role that non-verbal cues play in understanding social situations. Vision Language Models (VLMs) can potentially account for this gap, however their ability to make correct inferences over such social cues has received little attention. In this paper, we explore the capabilities of VLMs at social reasoning. We identify a previously overlooked limitation in VLMs: the Visual Social-Pragmatic Inference gap. To target this gap, we propose a new task for VLMs: Visual Social-Pragmatic Inference. We construct a high quality dataset to test the abilities of a VLM for this task and benchmark the performance of several VLMs on it.

Backdoor attacks are powerful and effective, but distributing LLMs without a proven track record like ‘meta-llama‘ or ‘qwen‘ rarely gains community traction. We identify LoRA sharing as a unique scenario where users are more willing to try unendorsed assets, since such shared LoRAs allow them to enjoy personalized LLMs with negligible investment. However, this convenient share-and-play ecosystem also introduces a new attack surface, where attackers can distribute malicious LoRAs to an undefended community. Despite the high-risk potential, no prior art has comprehensively explored LoRA’s attack surface under the downstream-enhancing share-and-play context. In this paper, we investigate how backdoors can be injected into task-enhancing LoRAs and examine the mechanisms of such infections. We find that with a simple, efficient, yet specific recipe, **a backdoor LoRA can be trained once and then seamlessly merged (in a training-free fashion) with multiple task-enhancing LoRAs, retaining both its malicious backdoor and benign downstream capabilities.** This allows attackers to scale the distribution of compromised LoRAs with minimal effort by leveraging the rich pool of existing shared LoRA assets. We note that such merged LoRAs are particularly *infectious* — because their malicious intent is cleverly concealed behind improved downstream capabilities, creating a strong incentive for voluntary download — and *dangerous* — because under local deployment, no safety measures exist to intervene when things go wrong. Our work is among the first to study this new threat model of training-free distribution of downstream-capable-yet-backdoor-injected LoRAs, highlighting the urgent need for heightened security awareness in the LoRA ecosystem. **Warning: This paper contains offensive content and involves a real-life tragedy.**

Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a large-scale Arabic multimodal dataset and benchmark explicitly designed for cultural understanding. Constructed through advanced agentic workflows and extensive human-in-the-loop annotations by 37 annotators from across the Arab world, PEARL comprises over 309K multimodal examples spanning ten culturally significant domains covering all Arab countries. We further provide two robust evaluation benchmarks (PEARL and PEARL-LITE) along with a specialized subset (PEARL-X) explicitly developed to assess nuanced cultural variations. Comprehensive evaluations on state-of-the-art open and proprietary LVLMs demonstrate that reasoning-centric instruction alignment substantially improves models’ cultural grounding compared to conventional scaling methods. PEARL establishes a foundational resource for advancing culturally-informed multimodal modeling research. All datasets and benchmarks are publicly available.

Protein-specific large language models (ProteinLLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design. While existing surveys focus on specific aspects or applications, this work provides the first comprehensive overview of ProteinLLMs, covering their architectures, training datasets, evaluation metrics, and diverse applications. Through a systematic analysis of over 100 articles, we propose a structured taxonomy of state-of-the-art ProteinLLMs, analyze how they leverage large-scale protein sequence data for improved accuracy, and explore their potential in advancing protein engineering and biomedical research. Additionally, we discuss key challenges and future directions, positioning ProteinLLMs as essential tools for scientific discovery in protein science. Resources are maintained at https://github.com/Yijia-Xiao/Protein-LLM-Survey.

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MAKIEval: A Multilingual Automatic WiKidata-based Framework for Cultural Awareness Evaluation for LLMs
Raoyuan Zhao | Beiduo Chen | Barbara Plank | Michael A. Hedderich

Large language models (LLMs) are used globally across many languages, but their English-centric pretraining raises concerns about cross-lingual disparities for cultural awareness, often resulting in biased outputs. However, comprehensive multilingual evaluation remains challenging due to limited benchmarks and questionable translation quality. To better assess these disparities, we introduce MAKIEval, an automatic multilingual framework for evaluating cultural awareness in LLMs across languages, regions, and topics. MAKIEval evaluates open-ended text generation, capturing how models express culturally grounded knowledge in natural language. Leveraging Wikidata’s multilingual structure as a cross-lingual anchor, it automatically identifies cultural entities in model outputs and links them to structured knowledge, enabling scalable, language-agnostic evaluation without manual annotation or translation. We then introduce four metrics that capture complementary dimensions of cultural awareness: granularity, diversity, cultural specificity, and consensus across languages. We assess 7 LLMs developed from different parts of the world, encompassing both open-source and proprietary systems, across 13 languages, 19 countries and regions, and 6 culturally salient topics (e.g., food, clothing). Notably, we find that models tend to exhibit stronger cultural awareness in English, suggesting that English prompts more effectively activate culturally grounded knowledge. We publicly release our code and data.

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Looking Beyond the Pixels: Evaluating Visual Metaphor Understanding in VLMs
Manishit Kundu | Sumit Shekhar | Pushpak Bhattacharyya

Visual metaphors are a complex vision–language phenomenon that requires both perceptual and conceptual reasoning to understand. They provide a valuable test of a model’s ability to interpret visual input and reason about it with creativity and coherence. We introduce ImageMet, a visual metaphor dataset, featuring 2177 synthetic and 350 human-annotated images. We benchmark several SOTA VLMs on two tasks: Visual Metaphor Captioning (VMC) and Visual Metaphor VQA (VM-VQA). We establish strong baselines by fine-tuning on ImageMet, which yields substantial performance gains in VMC (+4.67% SBERT-Similarity, +4.84% task-specific metric) and VM-VQA (+9.3% Accuracy on average). Additionally, we introduce a task-specific CoT prompting strategy that outperforms standard few-shot baselines (+1.99% in VMC, +5.21% in VM-VQA). We observe that despite strong performance on the VMC task, VLMs still significantly lag behind humans in understanding visual metaphors, indicating that their success often relies on learned associations rather than genuine analytical reasoning. We note that this gap is often obscured in metaphor captioning tasks where the automatic metrics correlate only moderately at best with human judgment (Pearson r < 0.6), highlighting the need for careful, holistic evaluation of the visual metaphor understanding of the models.

There emerges a critical security risk of LLM agents: indirect prompt injection, a sophisticated attack vector that compromises thecore of these agents, the LLM, by manipulating contextual information rather than direct user prompts. In this work, we propose a generic black-box optimization framework, AGENTVIGIL, designed to automatically discover and exploit indirect prompt injection vulnerabilities across diverse LLM agents. Our approach starts by constructing a high-quality initial seed corpus, then employs a seed selectionalgorithm based on Monte Carlo Tree Search (MCTS) to iteratively refine inputs, therebymaximizing the likelihood of uncovering agent weaknesses. We evaluate AGENTVIGIL on twopublic benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o, respectively, nearly doubling the performance of handcrafted baseline attacks. Moreover, AGENTVIGIL exhibits strong transferability across unseen tasks and internal LLMs, as well as promising results against defenses. Beyondbenchmark evaluations, we apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs,including malicious sites.

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Improving LLM-as-a-Judge Inference with the Judgment Distribution
Victor Wang | Michael JQ Zhang | Eunsol Choi

Using language models to scalably approximate human preferences on text quality (LLM-as-a-judge) has become a standard practice applicable to many tasks. A judgment is often extracted from the judge’s textual output alone, typically with greedy decoding. However, LLM judges naturally provide distributions over judgment tokens, inviting a breadth of inference methods for extracting fine-grained preferences. We find that taking the mean of the judgment distribution consistently outperforms taking the mode (i.e. greedy decoding) in all evaluation settings (i.e. pointwise, pairwise, and listwise). We further explore novel methods of deriving preferences from judgment distributions, and find that methods incorporating risk aversion often improve performance. Lastly, we analyze LLM-as-a-judge paired with chain-of-thought (CoT) prompting, showing that CoT can collapse the spread of the judgment distribution, often harming performance. Our findings show that leveraging distributional output improves LLM-as-a-judge, as opposed to using the text interface alone.

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Learning Is Not A Race: Improving Retrieval in Language Models via Equal Learning
Wanqian Yang | Aahlad Manas Puli | Rajesh Ranganath

Many applications that modern large language models (LLMs) are deployed on are retrieval tasks: the answer can be recovered from context and success is a matter of learning generalizable features from data. However, this is easier said than done. Overparametrized models trained on cross-entropy loss can overfit on noise. We argue that such overfitting is prone to happen when the model can identify mechanisms that rapidly drive down the loss of certain tokens early on in training. Fitting some tokens early reduce gradient signals in later iterations, as such, remaining tokens are more vulnerable to noise overfitting. We dub this phenomenon unequal learning and show that LLMs with longer contexts or larger embedding sizes are prone to this failure mode. In this work, we argue that learning training samples at an equal rate helps counter such biases. We highlight two mechanisms that promote equal learning: (i) loss functions that regularize uniform margins across training samples, (ii) small learning rates (e.g. by warming up) at the start of training. We demonstrate these approaches on various synthetic and natural language datasets.

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The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models
Marlene Lutz | Indira Sen | Georg Ahnert | Elisa Rogers | Markus Strohmaier

Persona prompting is increasingly used in large language models (LLMs) to simulate views of various sociodemographic groups. However, how a persona prompt is formulated can significantly affect outcomes, raising concerns about the fidelity of such simulations. Using five open-source LLMs, we systematically examine how different persona prompt strategies, specifically role adoption formats and demographic priming strategies, influence LLM simulations across 15 intersectional demographic groups in both open- and closed-ended tasks. Our findings show that LLMs struggle to simulate marginalized groups but that the choice of demographic priming and role adoption strategy significantly impacts their portrayal. Specifically, we find that prompting in an interview-style format and name-based priming can help reduce stereotyping and improve alignment. Surprisingly, smaller models like OLMo-2-7B outperform larger ones such as Llama-3.3-70B.Our findings offer actionable guidance for designing sociodemographic persona prompts in LLM-based simulation studies.

The Spiral of Silence (SoS) theory holds that individuals with minority views often refrain from speaking out for fear of social isolation, enabling majority positions to dominate public discourse. When the “agents” are large language models (LLMs), however, the classical psychological explanation is not directly applicable, since SoS was developed for human societies. This raises a central question: can SoS-like dynamics nevertheless emerge from purely statistical language generation in LLM collectives? We propose an evaluation framework for examining SoS in LLM agents. Specifically, we consider four controlled conditions that systematically vary the availability of “History” and “Persona” signals. Opinion dynamics are assessed using trend tests such as Mann–Kendall and Spearman’s rank, along with concentration measures including kurtosis and interquartile range. Experiments across open-source and closed-source models show that history and persona together produce strong majority dominance and replicate SoS patterns; history signals alone induce strong anchoring; and persona signals alone foster diverse but uncorrelated opinions, indicating that without historical anchoring, SoS dynamics cannot emerge. The work bridges computational sociology and responsible AI design, highlighting the need to monitor and mitigate emergent conformity in LLM-agent systems.

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Do We Know What LLMs Don’t Know? A Study of Consistency in Knowledge Probing
Raoyuan Zhao | Abdullatif Köksal | Ali Modarressi | Michael A. Hedderich | Hinrich Schuetze

The reliability of large language models (LLMs) is greatly compromised by their tendency to hallucinate, underscoring the need for precise identification of knowledge gaps within LLMs. Various methods for probing such gaps exist, ranging from calibration-based to prompting-based methods. To evaluate these probing methods, in this paper, we propose a new process based on using input variations and quantitative metrics. Through this, we expose two dimensions of inconsistency in knowledge gap probing. (1) **Intra-method inconsistency:** Minimal non-semantic perturbations in prompts lead to considerable variance in detected knowledge gaps within the same probing method; e.g., the simple variation of shuffling answer options can decrease agreement to around 40%. (2) **Cross-method inconsistency:** Probing methods contradict each other on whether a model knows the answer. Methods are highly inconsistent – with decision consistency across methods being as low as 7% – even though the model, dataset, and prompt are all the same. These findings challenge existing probing methods and highlight the urgent need for perturbation-robust probing frameworks.

Large language models (LLMs) often fail to scale their performance on long-context tasks performance in line with the context lengths they support. This gap is commonly attributed to retrieval failures—the models’ inability to identify information in the long inputs that is relevant to the task they are solving. Accordingly, recent efforts often focus on evaluating and improving LLMs’ retrieval performance: if retrieval is perfect, a model should, in principle, perform just as well on a long input as it does on a short one—or should it? This paper presents findings that the answer to this question may be negative. Our systematic experiments across 5 open- and closed-source LLMs on math, question answering, and coding tasks reveal that, even when models can perfectly retrieve all relevant information, their performance still degrades substantially (13.9%–85%) as input length increases but remains well within their claimed context lengths. This failure occurs even when the irrelevant tokens are replaced with minimally distracting whitespace, and, more surprisingly, when they are all masked and the models are forced to attend only to the relevant tokens. A similar performance drop is observed when all relevant evidence is placed immediately before the question. Our findings reveal a previously-unrealized limitation: the sheer length of the input alone can hurt LLM performance, independent of retrieval quality and without any distraction. They motivate our simple, model-agnostic mitigation strategy that transforms a long-context task into a short-context one by prompting the model to recite the retrieved evidence before attempting to solve the problem. On RULER, we observe a consistent improvement of GPT-4o up to 4% on an already strong baseline.

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DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics
Luke Yoffe | Alfonso Amayuelas | William Yang Wang

Multi-agent debates have been introduced to improve the accuracy of Large Language Models (LLMs) by having multiple agents discuss solutions to a problem over several rounds of debate. However, models often generate incorrect yet confident-sounding responses, which can mislead the others. This issue arises partly because agents do not consider how confident their peers are. To address this, we propose DebUnc, a debate framework that uses uncertainty metrics to assess agent confidence. Confidence is then conveyed through textual prompts or via a modified attention mechanism that adjusts token weights. Evaluations across benchmarks show that attention-based methods are particularly effective and that performance continues to improve as uncertainty estimation becomes more reliable. The code is available at https://github.com/lukeyoffe/debunc.

Vision-Language Models have shown both impressive capabilities and notable failures in data visualization understanding tasks, but we have limited understanding on how specific properties within a visualization type affect model performance. We present ProcVQA, a benchmark designed to analyze how VLM performance can be affected by structure type and structural density of visualizations depicting frequent patterns mined from sequence data. ProcVQA consists of mined process visualizations spanning three structure types (linear sequences, tree, graph) with varying levels of structural density (quantified using the number of nodes and edges), with expert-validated QA pairs on these visualizations. We evaluate 21 proprietary and open-source models on the dataset on two major tasks: visual data extraction (VDE) and visual question answering (VQA) (with four categories of questions). Our analysis reveals three key findings. First, models exhibit steep performance drops on multi-hop reasoning, with question type and structure type impacting the degradation. Second, structural density strongly affects VDE performance: hallucinations and extraction errors increase with edge density, even in frontier models. Third, extraction accuracy does not necessarily translate into strong reasoning ability. By isolating structural factors through controlled visualization generation, ProcVQA enables precise identification of VLM limitations. ProcVQA is available at: https://github.com/kzintas/ProcVQA.

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Probing Political Ideology in Large Language Models: How Latent Political Representations Generalize Across Tasks
Tianyi Zhang

Large language models (LLMs) encode rich internal representations of political ideology, but it remains unclear how these representations contribute to model decision-making, and how these latent dimensions interact with one another. In this work, we investigate whether ideological directions identified via linear probes—specifically, those predicting DW-NOMINATE scores from attention head activations—influence model behavior in downstream political tasks. We apply inference-time interventions to steer a decoder-only transformer along learned ideological directions, and evaluate their effect on three tasks: political bias detection, voting preference simulation, and bias neutralization via rewriting. Our results show that learned ideological representations generalize well to bias detection, but not as well to voting simulations, suggesting that political ideology is encoded in multiple, partially disentangled latent structures. We also observe asymmetries in how interventions affect liberal versus conservative outputs, raising concerns about pretraining-induced bias and post-training alignment effects. This work highlights the risks of using biased LLMs for politically sensitive tasks, and calls for deeper investigation into the interaction of social dimensions in model representations, as well as methods for steering them toward fairer, more transparent behavior.

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Understanding GUI Agent Localization Biases through Logit Sharpness
Xingjian Tao | Yiwei Wang | Yujun Cai | Zhicheng Yang | Jing Tang

Multimodal large language models (MLLMs) have enabled GUI agents to interact with operating systems by grounding language into spatial actions. Despite their promising performance, these models frequently exhibit hallucinations—systematic localization errors that compromise reliability. We propose a fine-grained evaluation framework that categorizes model predictions into four distinct types, revealing nuanced failure modes beyond traditional accuracy metrics. To better quantify model uncertainty, we introduce the Peak Sharpness Score (PSS), a metric that evaluates the alignment between semantic continuity and logits distribution in coordinate prediction. Building on this insight, we further propose Context-Aware Cropping, a training-free technique that improves model performance by adaptively refining input context. Extensive experiments demonstrate that our framework and methods provide actionable insights and enhance the interpretability and robustness of GUI agent behavior.

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The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions
Sophie Wu | Jan Philip Wahle | Saif M. Mohammad

This paper is the first investigation of the connection between emotion, embodiment, and everyday language in a large sample of natural language data. We created corpora of body part mentions (BPMs) in online English text (blog posts and tweets). This includes a subset featuring human annotations for the emotions of the person whose body part is mentioned in the text. We show that BPMs are common in personal narratives and tweets (~5% to 10% of posts include BPMs) and that their usage patterns vary markedly by time and location. Using word–emotion association lexicons and our annotated data, we show that text containing BPMs tends to be more emotionally charged, even when the BPM is not explicitly used to describe a physical reaction to the emotion in the text. Finally, we discover a strong and statistically significant correlation between body-related language and a variety of poorer health outcomes. In sum, we argue that investigating the role of body-part related words in language can open up valuable avenues of future research at the intersection of NLP, the affective sciences, and the study of human wellbeing.

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HomoGraphAdapter: A Homogeneous Graph Neural Network as an Effective Adapter for Vision-Language Models
Chuan He | Zhuozhao Li | Song Guo | Xiaocheng Lu | Jinxiang Lai

Vision-Language Models (VLMs), such as CLIP, have exhibited significant advancements in recognizing visual concepts through natural language guidance. However, adapting these models to downstream tasks remains challenging. Existing adaptation methods either overlook the structural knowledge between the text and image modalities or create overly complex graphs containing redundant information for alignment, leading to suboptimal classification performance and increased computational overhead. This paper proposes a novel adapter-tuning methodology named Homogeneous Graph Adapter (HomoGraphAdapter), which transforms diverse textual and visual descriptions into a unified set of node representations and establishes edges between nodes for inter-modal and cross-modal semantic alignment. We leverage a straightforward homogeneous Graph Neural Network (GNN) to adapt positive and negative classifiers across text and image modalities. The classifiers comprehensively enhance the performance for few-shot classification and OOD generalization. Compared with the SOTA approach HeGraphAdapter, HomoGraphAdapter improves classification accuracy by an average of 1.51% for 1-shot and 0.74% for 16-shot on 11 datasets, while also reducing both precomputation time and training time.

Deep learning models trained on extensive Electronic Health Records (EHR) data have achieved high accuracy in diagnosis prediction, offering the potential to assist clinicians in decision-making and treatment planning. However, these models lack two crucial features that clinicians highly value: interpretability and interactivity. The “black-box” nature of these models makes it difficult for clinicians to understand the reasoning behind predictions, limiting their ability to make informed decisions. Additionally, the absence of interactive mechanisms prevents clinicians from incorporating their own knowledge and experience into the decision-making process. To address these limitations, we propose II-KEA, a knowledge-enhanced agent-driven causal discovery framework that integrates personalized knowledge databases and agentic LLMs. II-KEA enhances interpretability through explicit reasoning and causal analysis, while also improving interactivity by allowing clinicians to inject their knowledge and experience through customized knowledge bases and prompts. II-KEA is evaluated on both MIMIC-III and MIMIC-IV, demonstrating superior performance along with enhanced interpretability and interactivity, as evidenced by its strong results from extensive case studies.

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PROOD: A Simple LLM Out-of-Distribution Guardrail Leveraging Response Semantics
Joshua Tint

Out-of-distribution (OOD) detection is a key safeguard for large language models, especially when they’re deployed in real-world applications. However, existing OOD methods often struggle with prompts that are deliberately obfuscated, context-dependent, or superficially benign—making it hard to distinguish between harmless queries and adversarial or dangerous ones. These methods typically assess prompts in isolation, missing important semantic cues from the model’s response. We introduce PROOD, prompt-response OOD detection, a framework that jointly analyzes LLM prompts *and their corresponding outputs* to improve semantic understanding. PROOD supports zero-shot multiclass detection using synthetic data generation and it offers a tunable probabilistic classification output. We validate PROOD on three challenging benchmarks—TrustLLM, OR-Bench, and AdvBench—where consistently outperforms prior OOD techniques, improving F1 scores by up to 6.3 points, from 0.871 to 0.934. Our results show that incorporating model responses enables more accurate, context-aware OOD detection in complex and adversarial prompt environments.

Measuring the relevance between user queries and advertisements is a critical task for advertisement (ad) recommendation systems, such as Microsoft Bing Ads and Google Ads. Traditionally, this requires expert data labeling, which is both costly and time-consuming. Recent advances have explored using Large Language Models (LLMs) for labeling, but these models often lack domain-specific knowledge. In-context learning (ICL), which involves providing a few demonstrations, is a common practice to enhance LLM performance on domain-specific tasks. However, retrieving high-quality demonstrations in a vast exploration space remains challenging. In this paper, we introduce ICL-Bandit, a practical and effective approach that leverages ICL to enhance the query-ad relevance labeling capabilities of LLMs. We develop a novel bandit learning method to identify and provide superior demonstrations for ICL, thereby improving labeling performance. Experimental results demonstrate that ICL-Bandit achieves state-of-the-art performance compared to existing methods. Additionally, ICL-Bandit has been deployed in Company X, that serves billions of users worldwide, confirming its robustness and effectiveness.

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Intent-aware Schema Generation and Refinement for Literature Review Tables
Vishakh Padmakumar | Joseph Chee Chang | Kyle Lo | Doug Downey | Aakanksha Naik

The increasing volume of academic literature makes it essential for researchers to organize, compare, and contrast collections of documents. Large language models (LLMs) can support this process by generating schemas defining shared aspects along which to compare papers. However, progress on schema generation has been slow due to: (i) ambiguity in reference-based evaluations, and (ii) lack of editing/refinement methods. Our work is the first to address both issues. First, we present an approach for augmenting unannotated table corpora with synthesized intents, and apply it to create a dataset for studying schema generation conditioned on a given information need, thus reducing ambiguity. With this dataset, we show how incorporating table intents significantly improves baseline performance in reconstructing reference schemas. We start by comprehensively benchmarking several single-shot schema generation methods, including prompted LLM workflows and fine-tuned models, showing that smaller, open-weight models can be fine-tuned to be competitive with state-of-the-art prompted LLMs. Next, we propose several LLM-based schema refinement techniques and show that these can further improve schemas generated by these methods.

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NLP Needs Diversity outside of ‘Diversity’
Joshua Tint

This position paper argues that recent progress with diversity in NLP is disproportionately concentrated on a small number of areas surrounding fairness. We further argue that this is the result of a number of incentives, biases, and barriers which come together to disenfranchise marginalized researchers in non-fairness fields, or to move them into fairness-related fields. We substantiate our claims with an investigation into the demographics of NLP researchers by subfield, using our research to support a number of recommendations for ensuring that all areas within NLP can become more inclusive and equitable. In particular, we highlight the importance of breaking down feedback loops that reinforce disparities, and the need to address geographical and linguistic barriers that hinder participation in NLP research.

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Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models
Mohammad Saim | Phan Anh Duong | Cat Luong | Aniket Bhanderi | Tianyu Jiang

The embodiment of emotional reactions from body parts contains rich information about our affective experiences. We propose a framework that utilizes state-of-the-art large vision language models (LVLMs) to generate Embodied LVLM Emotion Narratives (ELENA). These are well-defined, multi-layered text outputs, primarily comprising descriptions that focus on the salient body parts involved in emotional reactions. We also employ attention maps and observe that contemporary models exhibit a persistent bias towards the facial region. Despite this limitation, we observe that our employed framework can effectively recognize embodied emotions in face-masked images, outperforming baselines without any fine-tuning. ELENA opens a new trajectory for embodied emotion analysis across the modality of vision and enriches modeling in an affect-aware setting.

Large language models (LLMs) have achieved promising results in tabular data generation. However, inherent historical biases in tabular datasets often cause LLMs to exacerbate fairness issues, particularly when multiple advantaged and protected features are involved. In this work, we introduce a universal debiasing framework that minimizes group-level dependencies by simultaneously reducing the mutual information between advantaged and protected attributes. By leveraging the autoregressive structure and analytic sampling distributions of LLM-based tabular data generators, our approach efficiently computes mutual information, reducing the need for cumbersome numerical estimations. Building on this foundation, we propose two complementary methods: a direct preference optimization (DPO)-based strategy, namely UDF-DPO, that integrates seamlessly with existing models, and a targeted debiasing technique, namely UDF-MIX, that achieves debiasing without tuning the parameters of LLMs. Extensive experiments demonstrate that our framework effectively balances fairness and utility, offering a scalable and practical solution for debiasing in high-stakes applications.

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Beyond Linear Steering: Unified Multi-Attribute Control for Language Models
Narmeen Fatimah Oozeer | Luke Marks | Fazl Barez | Amir Abdullah

Controlling multiple behavioral attributes in large language models (LLMs) at inference time is a challenging problem due to interference between attributes and the limitations of linear steering methods, which assume additive behavior in activation space and require per-attribute tuning. We introduce K-Steering, a unified and flexible approach that trains a single non-linear multi-label classifier on hidden activations and computes intervention directions via gradients at inference time. This avoids linearity assumptions, removes the need for storing and tuning separate attribute vectors, and allows dynamic composition of behaviors without retraining. To evaluate our method, we propose two new benchmarks, TONEBANK and DEBATEMIX, targeting compositional behavioral control. Empirical results across 3 model families, validated by both activation-based classifiers and LLM-based judges, demonstrate that K-Steering outperforms strong baselines in accurately steering multiple behaviors.

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Unequal Scientific Recognition in the Age of LLMs
Yixuan Liu | Abel Elekes | Jianglin Lu | Rodrigo Dorantes-Gilardi | Albert-Laszlo Barabasi

Large language models (LLMs) are reshaping how scientific knowledge is accessed and represented. This study evaluates the extent to which popular and frontier LLMs including GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro recognize scientists, benchmarking their outputs against OpenAlex and Wikipedia. Using a dataset focusing on 100,000 physicists from OpenAlex to evaluate LLM recognition, we uncover substantial disparities: LLMs exhibit selective and inconsistent recognition patterns. Recognition correlates strongly with scholarly impact such as citations, and remains uneven across gender and geography. Women researchers, and researchers from Africa, Asia, and Latin America are significantly underrecognized. We further examine the role of training data provenance, identifying Wikipedia as a potential sources that contributes to recognition gaps. Our findings highlight how LLMs can reflect, and potentially amplify existing disparities in science, underscoring the need for more transparent and inclusive knowledge systems.

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Zero-Shot Fine-Grained Image Classification Using Large Vision-Language Models
Md. Atabuzzaman | Andrew Zhang | Chris Thomas

Large Vision-Language Models (LVLMs) have demonstrated impressive performance on vision-language reasoning tasks. However, their potential for zero-shot fine-grained image classification, a challenging task requiring precise differentiation between visually similar categories, remains underexplored. We present a novel method that transforms zero-shot fine-grained image classification into a visual question-answering framework, leveraging LVLMs’ comprehensive understanding capabilities rather than relying on direct class name generation. We enhance model performance through a novel attention intervention technique. We also address a key limitation in existing datasets by developing more comprehensive and precise class description benchmarks. We validate the effectiveness of our method through extensive experimentation across multiple fine-grained image classification benchmarks. Our proposed method consistently outperforms the current state-of-the-art (SOTA) approach, demonstrating both the effectiveness of our method and the broader potential of LVLMs for zero-shot fine-grained classification tasks. Code and Datasets: https://github.com/Atabuzzaman/Fine-grained-classification

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Using tournaments to calculate AUROC for zero-shot classification with LLMs
WonJin Yoon | Ian Bulovic | Timothy A. Miller

Large language models perform surprisingly well on many zero-shot classification tasks, but are difficult to fairly compare to supervised classifiers due to the lack of a modifiable decision boundary. In this work, we propose and evaluate a method that transforms binary classification tasks into pairwise comparisons between instances within a dataset, using LLMs to produce relative rankings of those instances. Repeated pairwise comparisons can be used to score instances using the Elo rating system (used in chess and other competitions), inducing a confidence ordering over instances in a dataset. We evaluate scheduling algorithms for their ability to minimize comparisons, and show that our proposed algorithm leads to improved classification performance, while also providing more information than traditional zero-shot classification.

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Exploration-Driven Reinforcement Learning for Expert Routing Improvement in Mixture-of-Experts Language Models
Gyunyeop Kim | Sangwoo Kang

The performance of MoE-based LLMs depends on the router’s ability to select suitable experts; however, the router is typically not explicitly supervised to acquire this routing ability. We propose Exploration-Driven Reinforcement Learning (ERL), which explicitly optimizes the router by exploration of alternative routing paths. For every input, ERL evaluates by (i) the original routing path and (ii) paths in which an 𝛼-fraction of routing decisions is randomly perturbed, and treats their performance gap as an advantage signal in a reinforcement learning. Moreover, MoE-ERL_wPL mitigates the risk of performance collapse caused by routing reinforcement learning–induced expert over-specialization by intentionally enforcing overlap in experts’ knowledge. Without adding parameters or external reward models, our method improves summarization (SAMSum, XSUM), question answering (SQuAD), and language modeling (WikiText-2), and raises routing quality, delivering up to 8.9 × higher MRR than baselines over 100 perturbed routing paths. Code is available at our github.

Knowledge discovery from large-scale, heterogeneous textual corpora presents a significant challenge. Document clustering offers a practical solution by organizing unstructured texts into coherent groups based on content and thematic similarity. However, clustering does not inherently ensure thematic consistency. Here, we propose a novel framework that constructs a similarity graph over document embeddings and applies iterative graph-based clustering algorithms to partition the corpus into initial clusters. To overcome the limitations of conventional methods in producing semantically consistent clusters, we incorporate iterative feedback from a large language model (LLM) to guide the refinement process. The LLM is used to assess cluster quality and adjust edge weights within the graph, promoting better intra-cluster cohesion and inter-cluster separation. The LLM guidance is based on a set of success Rate metrics that we developed to measure the semantic coherence of clusters. Experimental results on multiple benchmark datasets demonstrate that the iterative process and additional user-supplied a priori edges improve the summaries’ consistency and fluency, highlighting the importance of known connections among the documents. The removal of very rare or very frequent sentences has a mixed effect on the quality scores.Our full code is available here: https://github.com/D2CS-sub/D2CS

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GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
Sahiti Yerramilli | Nilay Pande | Rynaa Grover | Jayant Sravan Tamarapalli

This paper introduces GeoChain, a large-scale benchmark for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs). Leveraging 1.46 million Mapillary street-level images, GeoChain pairs each image with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs). These sequences guide models from coarse attributes to fine-grained localization across four reasoning categories - visual, spatial, cultural, and precise geolocation - annotated by difficulty. Images are also enriched with semantic segmentation (150 classes) and a visual locatability score. Our benchmarking of frontier MLLMs on a diverse 2,088-image subset reveals consistent challenges: models frequently exhibit weaknesses in visual grounding, display erratic reasoning, and struggle to achieve accurate localization, especially as the reasoning complexity escalates. GeoChain offers a robust diagnostic methodology, critical for fostering significant advancements in complex geographic reasoning within MLLMs.

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SteerVLM: Robust Model Control through Lightweight Activation Steering for Vision Language Models
Anushka Sivakumar | Andrew Zhang | Zaber Ibn Abdul Hakim | Chris Thomas

This work introduces SteerVLM, a lightweight steering module designed to guide Vision-Language Models (VLMs) towards outputs that better adhere to desired instructions. Our approach learns from the latent embeddings of paired prompts encoding target and converse behaviors to dynamically adjust activations connecting the language modality with image context. This allows for fine-grained, inference-time control over complex output semantics without modifying model weights while preserving performance on off-target tasks. Our steering module requires learning parameters equal to 0.14% of the original VLM’s size. Our steering module gains model control through dimension-wise activation modulation and adaptive steering across layers without requiring pre-extracted static vectors or manual tuning of intervention points. Furthermore, we introduce VNIA (Visual Narrative Intent Alignment), a multimodal dataset specifically created to facilitate the development and evaluation of VLM steering techniques. Our method outperforms existing intervention techniques on steering and hallucination mitigation benchmarks for VLMs and proposes a robust solution for multimodal model control through activation engineering.

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FractalLLM: Lossless Self-Speculative Decoding with Layer Embedded Self-Compression
Juhyeong Kim | Sangyeon Yu | Gyunyeop Kim | Sangwoo Kang

Autoregressive decoding in large language models (LLMs) necessitates a full forward pass for each generated token, significantly increasing inference latency. To address this limitation, we propose Fractal-LLM, a lossless self-speculative decoding method that embeds a compressed model within selected decoder layers of the original model. Specifically, our approach generates multiple draft tokens in parallel by injecting compressed layers into selected decoder layers. These draft tokens are subsequently verified through a single forward pass of the original model, ensuring the final outputs exactly match those produced by the original model. Experimental results across diverse benchmarks—including GSM8K, XSUM, CNN/DailyMail, and HumanEval—demonstrate that our method achieves substantial inference speed-ups (up to 2.47×) compared to standard autoregressive decoding, without requiring any additional training.

The efficient implementation of large language models (LLMs) is crucial for deployment on resource-constrained devices. Low-rank tensor compression techniques, such as tensor-train (TT) networks, have been widely studied for over-parameterized neural networks. However, their applications to compress pre-trained LLMs for downstream tasks (post-training) remains challenging due to the high-rank nature of pre-trained LLMs and the lack of access to pretraining data. In this study, we investigate low-rank tensorized LLMs during fine-tuning and propose sparse augmented tensor networks (Saten) to enhance their performance. The proposed Saten framework enables full model compression. Experimental results demonstrate that Saten enhances both accuracy and compression efficiency in tensorized language models, achieving state-of-the-art performance.

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Third-Person Appraisal Agent: Simulating Human Emotional Reasoning in Text with Large Language Models
Simin Hong | Jun Sun | Hongyang Chen

Emotional reasoning is essential for improving human-AI interactions, particularly in mental health support and empathetic systems. However, current approaches, which primarily map sensory inputs to fixed emotion labels, fail to understand the intricate relationships between motivations, thoughts, and emotions, thereby limiting their ability to generalize across flexible emotional reasoning tasks. To address this, we propose a novel third-person appraisal agent that simulates human-like emotional reasoning through three phases: Primary Appraisal, Secondary Appraisal, and Reappraisal. In the Primary Appraisal phase, a third-person generator powered by a large language model (LLM) infers emotions based on cognitive appraisal theory. The Secondary Appraisal phase uses an evaluator LLM to provide feedback, guiding the generator in refining its predictions. The generator then uses counterfactual reasoning to adjust its process and explore alternative emotional responses. The Reappraisal phase utilizes reinforced fine-tuning (ReFT) by employing a reflective actor-critic framework to further enhance the model’s performance and generalization. This process uses reward signals and learns from appraisal trajectories without human annotations. Our approach outperforms baseline LLMs in various emotional reasoning tasks, demonstrating superior generalization and interpretability. To the best of our knowledge, this is the first cognition-based architecture designed to enhance emotional reasoning in LLMs, advancing AI towards human-like emotional understanding.

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Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents
Hanxu Hu | Jannis Vamvas | Rico Sennrich

LLMs have paved the way for truly simple document-level machine translation, but challenges such as omission errors remain. In this paper, we study a simple method for handling document-level machine translation, by leveraging previous contexts in a multi-turn conversational manner. Specifically, by decomposing documents into segments and iteratively translating them while maintaining previous turns, this method ensures coherent translations without additional training, and can fully re-use the KV cache of previous turns thus minimizing computational overhead. We further propose a ‘source-primed’ method that first provides the whole source document before multi-turn translation. We empirically show this multi-turn method outperforms both translating entire documents in a single turn and translating each segment independently according to multiple automatic metrics in representative LLMs, establishing a strong baseline for document-level translation using LLMs.

Identifying causal relationships rather than spurious correlations between words and class labels plays a crucial role in building robust text classifiers. Previous studies proposed using causal effects to distinguish words that are causally related to the sentiment, and then building robust text classifiers using words with high causal effects. However, we find that when a sentence has multiple causally related words simultaneously, the magnitude of causal effects will be significantly reduced, which limits the applicability of previous causal effect-based methods in distinguishing causally related words from spuriously correlated ones. To fill this gap, in this paper, we introduce both the probability of necessity (PN) and probability of sufficiency (PS), aiming to answer the counterfactual question that ‘if a sentence has a certain sentiment in the presence/absence of a word, would the sentiment change in the absence/presence of that word?’. Specifically, we first derive the identifiability of PN and PS under different sentiment monotonicities, and calibrate the estimation of PN and PS via the estimated average treatment effect. Finally, the robust text classifier is built by identifying the words with larger PN and PS as causally related words, and other words as spuriously correlated words, based on a contrastive learning approach name CPNS is proposed to achieve robust sentiment classification. Extensive experiments are conducted on public datasets to validate the effectiveness of our method.

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The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems
Chanwoo Choi | Jinsoo Kim | Sukmin Cho | Soyeong Jeong | Buru Chang

With the growing adoption of retrieval-augmented generation (RAG) systems, various attack methods have been proposed to degrade their performance. However, most existing approaches rely on unrealistic assumptions in which external attackers have access to internal components such as the retriever. To address this issue, we introduce a realistic black-box attack based on the RAG paradox, a structural vulnerability that emerges from the system’s effort to enhance trust by revealing both the retrieved documents and their sources to users. This transparency enables attackers to observe which sources are used and how information is phrased, allowing them to craft poisoned documents that are more likely to be retrieved and upload them to the identified sources. Moreover, as RAG systems directly provide retrieved content to users, these documents must not only be retrievable but also appear natural and credible to prevent users from questioning the search results. Unlike prior work that focuses solely on improving document retrievability, our attack method explicitly considers both retrievability and user trust in the retrieved content. Through extensive offline and online experiments, we demonstrate that our method significantly degrades system performance without internal access, while generating natural-looking poisoned documents.

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Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding
Zhenxi Lin | Ziheng Zhang | Jian Wu | Yefeng Zheng | Xian Wu

Biomedical entity linking (BioEL) aims at mapping biomedical mentions to pre-defined entities. While extensive research efforts have been devoted to BioEL, applying large language models (LLMs) for BioEL has not been fully explored. Previous attempts have revealed difficulties when directly applying LLMs to the task of BioEL. Possible errors include generating non-entity sentences, invalid entities, or incorrect answers. To this end, we introduce LLM4BioEL, a concise yet effective framework that enables LLMs to adapt well to the BioEL task. LLM4BioEL employs restrictive decoding to ensure the generation of valid entities and utilizes entropy-based contrastive decoding to incorporate additional biomedical knowledge without requiring further tuning. Besides, we implement few-shot prompting to maximize the in-context learning capabilities of LLM. Extensive experiments demonstrate the effectiveness and applicability of LLM4BioEL across different BioEL tasks and with different LLM backbones, and the best-performing LLM4BioEL variant outperforms the traditional and LLM-based BioEL baselines.

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Cut the Deadwood Out: Backdoor Purification via Guided Module Substitution
Yao Tong | Weijun Li | Xuanli He | Haolan Zhan | Qiongkai Xu

Model NLP models are commonly trained (or fine-tuned) on datasets from untrusted platforms like HuggingFace, posing significant risks of data poisoning attacks. A practical yet underexplored challenge arises when such backdoors are discovered after model deployment, making retraining-required defenses less desirable due to computational costs and data constraints. In this work, we propose Guided Module Substitution (GMS), an effective retraining-free method based on guided merging of the victim model with a single proxy model. Specifically, GMS selectively replaces modules in the victim model based on a trade-off signal between utility and backdoor. GMS offers four desirable properties: (1) robustness to the choice and trustworthiness of the proxy model, (2) applicability under relaxed data assumptions, (3) stability across hyperparameters, and (4) transferability across different attacks. Extensive experiments on encoder models and decoder LLMs demonstrate the strong effectiveness of GMS. GMS significantly outperforms even the strongest defense baseline, particularly against challenging attacks like LWS.

Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair, which may substantially reduce the time consumption of developers and enhance their efficiency. Significant advancements in debugging datasets have been made to promote the development of code debugging. However, these datasets primarily focus on assessing the LLM’s function-level code repair capabilities, neglecting the more complex and realistic repository-level scenarios, which leads to an incomplete understanding of the LLM’s challenges in repository-level debugging. While several repository-level datasets have been proposed, they often suffer from limitations such as limited diversity of tasks, languages, and error types. To mitigate this challenge, this paper introduces RepoDebug, a multi-task and multi-language repository-level code debugging dataset with 22 subtypes of errors that supports 8 commonly used programming languages and 3 debugging tasks. Furthermore, we conduct evaluation experiments on 10 LLMs, where Claude 3.5 Sonnect, the best-performing model, still cannot perform well in repository-level debugging.

Evaluating the factuality of long-form generations from Large Language Models (LLMs) remains challenging due to accuracy issues and costly human assessment. Prior evaluation pipelines attempt this by decomposing text into claims, searching for evidence, and verifying claims, but suffer from critical drawbacks: (1) inefficiency due to complex pipeline components unsuitable for long LLM outputs, and (2) ineffectiveness stemming from inaccurate claim sets and insufficient evidence collection of one-line SERP snippets. To address these limitations, we adapt the existing decompose-then-verify evaluation framework and propose **FaStFact**, a fast and strong evaluation pipeline that achieves the highest alignment with human evaluation and efficiency among existing baselines. FaStFact first employs chunk-level claim extraction integrated with confidence-based pre-verification, significantly reducing the cost of web searching and inference calling while ensuring reliability. For searching and verification, it gathers document-level evidence from crawled website pages for retrieval during verification, addressing the evidence insufficiency problem in previous pipelines. Extensive experiments based on an aggregated and manually annotated benchmark demonstrate the reliability of FaStFact in both efficiently and effectively evaluating the factuality of long-form LLM generations. We submit the paper with code and benchmark, and will make them publicly available to facilitate research.

There has been significant research on propagandistic content detection across different modalities and languages. However, most studies have primarily focused on detection, with little attention given to explanations justifying the predicted label. This is largely due to the lack of resources that provide explanations alongside annotated labels. To address this issue, we propose a multilingual (i.e., Arabic and English) explanation-enhanced dataset, the first of its kind. Additionally, we introduce an explanation-enhanced LLM for both label detection and rationale-based explanation generation. Our findings indicate that the model performs comparably while also generating explanations. We will make the dataset and experimental resources publicly available for the research community (https://github.com/firojalam/PropXplain).

Knowledge-based complex reasoning remains a significant challenge for large language models (LLMs) with in-context learning. To tackle this issue, previous studies focus on ensuring behavior fidelity, factuality, or reliability in generated reasoning processes that guide LLMs to produce solutions. However, these studies often neglect the simultaneous optimization on all these three aspects for each thought. The main challenges are the lack of comprehensive assessment mechanisms and the difficulty of efficient thought-level optimization. This paper introduces the Evolution of Thoughts (EoT) framework, which enhances the factuality, fidelity, and reliability of each thought in the reasoning process through a few LLM inferences. We propose a thought assessment method that is sensitive to knowledge and LLM behaviors, using three scorers to evaluate each thought by considering domain context, semantic alignment, and behavior impact. Additionally, we establish a self-reflective evolution mechanism to facilitate each reasoning process generation in a single-forward inference. Extensive experiments demonstrate that, for knowledge-based complex tasks, EoT improves the factuality and fidelity of reasoning processes by approximately 16.5% and 48.8%, respectively, while enhancing LLM reasoning capability by about 6.2%, outperforming advanced approaches.

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Reveal and Release: Iterative LLM Unlearning with Self-generated Data
Linxi Xie | Xin Teng | Shichang Ke | Hongyi Wen | Shenji Wan

Large language model (LLM) unlearning has demonstrated effectiveness in removing the influence of undesirable data (also known as forget data). Existing approaches typically assume full access to the forget dataset, overlooking two key challenges: (1) Forget data is often privacy-sensitive, rare, or legally regulated, making it expensive or impractical to obtain (2) The distribution of available forget data may not align with how that information is represented within the model. To address these limitations, we propose a “Reveal-and-Release” method to unlearn with self-generated data, where we prompt the model to reveal what it knows using optimized instructions. To fully utilize the self-generated forget data, we propose an iterative unlearning framework, where we make incremental adjustments to the model’s weight space with parameter-efficient modules trained on the forget data. Experimental results demonstrate that our method balances the tradeoff between forget quality and utility preservation.

Current fine-grained error analyses by LLMs gain more and more attention in machine translation, but these analyses do not ground the errors to the reasons why the annotated text spans are erroneous. If LLMs do not know such reasons, the corrections or refinements by LLMs will be untrustworthy.In this paper, we check whether LLMs know such reasons in translation error grounding task. We manually build an evaluation resource through a bi-directional grounding scheme. In the forward direction, we annotate the explanation of the reason for each error span. In the backward direction, we annotate the error span given its explanation, in which the error span is masked. If the error spans of both directions are consistent, we deem the explanation is valid. Such grounding process can regulate the explanation so as to avoid the subjective bias. The evaluation results on this resource show that LLMs perform significantly worse than human in both directions. Furthermore, we apply the error grounding for filtering false alarmed errors, and achieve significant improvement in translation error detection.

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Enhancing Time Awareness in Generative Recommendation
Sunkyung Lee | Seongmin Park | Jonghyo Kim | Mincheol Yoon | Jongwuk Lee

Generative recommendation has emerged as a promising paradigm that formulates the recommendations into a text-to-text generation task, harnessing the vast knowledge of large language models. However, existing studies focus on considering the sequential order of items and neglect to handle the temporal dynamics across items, which can imply evolving user preferences. To address this limitation, we propose a novel model, Generative Recommender Using Time awareness (GRUT), effectively capturing hidden user preferences via various temporal signals. We first introduce Time-aware Prompting, consisting of two key contexts. The user-level temporal context models personalized temporal patterns across timestamps and time intervals, while the item-level transition context provides transition patterns across users. We also devise Trend-aware Inference, a training-free method that enhances rankings by incorporating trend information about items with generation likelihood. Extensive experiments demonstrate that GRUT outperforms state-of-the-art models, with gains of up to 15.4% and 14.3% in Recall@5 and NDCG@5 across four benchmark datasets. The source code is available at https://github.com/skleee/GRUT.

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Adaptive LLM Routing under Budget Constraints
Pranoy Panda | Raghav Magazine | Chaitanya Devaguptapu | Sho Takemori | Vishal Sharma

Large Language Models (LLMs) have revolutionized natural language processing, but their varying capabilities and costs pose challenges in practical applications. LLM routing addresses this by dynamically selecting the most suitable LLM for each query/task. Previous approaches treat this as a supervised learning problem, assuming complete knowledge of optimal query-LLM pairings. However, real-world scenarios lack such comprehensive mappings and face evolving user queries. We thus propose to study LLM routing as a contextual bandit problem, enabling adaptive decision-making using bandit feedback without requiring exhaustive inference across all LLMs for all queries (in contrast to supervised routing). To address this problem, we develop a shared embedding space for queries and LLMs, where query and LLM embeddings are aligned to reflect their affinity. This space is initially learned from offline human preference data and refined through online bandit feedback. We instantiate this idea through Preference-prior Informed Linucb fOr adaptive rouTing (PILOT), a novel extension of LinUCB. To handle diverse user budgets for model routing, we introduce an online cost policy modeled as a multi-choice knapsack problem, ensuring resource-efficient routing.

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Promptception: How Sensitive Are Large Multimodal Models to Prompts?
Mohamed Insaf Ismithdeen | Muhammad Uzair Khattak | Salman Khan

Despite the success of Large Multimodal Models (LMMs) in recent years, prompt design for LMMs in Multiple‐Choice Question Answering (MCQA) remains poorly understood. We show that even minor variations in prompt phrasing and structure can lead to accuracy deviations of up to 15% for certain prompts and models. This variability poses a challenge for transparent and fair LMM evaluation, as models often report their best-case performance using carefully selected prompts. To address this, we introduce **Promptception**, a systematic framework for evaluating prompt sensitivity in LMMs. It consists of 61 prompt types, spanning 15 categories and 6 supercategories, each targeting specific aspects of prompt formulation, and is used to evaluate 10 LMMs ranging from lightweight open‐source models to GPT-4o and Gemini 1.5 Pro, across 3 MCQA benchmarks: MMStar, MMMU‐Pro, MVBench. Our findings reveal that proprietary models exhibit greater sensitivity to prompt phrasing, reflecting tighter alignment with instruction semantics, while open‐source models are steadier but struggle with nuanced and complex phrasing. Based on this analysis, we propose Prompting Principles tailored to proprietary and open-source LMMs, enabling more robust and fair model evaluation.

Fine-tuning large language models (LLMs) with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteristics in data across different organizations, the idea of collaboratively fine-tuning an LLM using data from multiple sources presents an appealing opportunity. However, organizations are often reluctant to share local data, making centralized fine-tuning impractical. Federated learning (FL), a privacy-preserving framework, enables clients to retain local data while sharing only model parameters for collaborative training, offering a potential solution. While fine-tuning LLMs on centralized datasets risks data leakage through next-token prediction, the iterative aggregation process in FL results in a global model that encapsulates generalized knowledge, which some believe protects client privacy. In this paper, however, we present contradictory findings through extensive experiments. We show that attackers can still extract training data from the global model, even using straightforward generation methods, with leakage increasing as the model size grows. Moreover, we introduce an enhanced attack strategy tailored to FL, which tracks global model updates during training to intensify privacy leakage. To mitigate these risks, we evaluate privacy-preserving techniques in FL, including differential privacy, regularization-constrained updates and adopting LLMs with safety alignment. Our results provide valuable insights and practical guidelines for reducing privacy risks when training LLMs with FL.

Agents powered by large language models (LLMs) have demonstrated strong planning and decision-making capabilities in complex embodied environments. However, such agents often suffer from inefficiencies in multi-turn interactions, frequently trapped in repetitive loops or issuing ineffective commands, leading to redundant computational overhead. Instead of relying solely on learning from trajectories, we take a first step toward exploring the early-exit behavior for LLM-based agents. We propose two complementary approaches, 1. an **intrinsic** method that injects exit instructions during generation, and 2. an **extrinsic** method that verifies task completion to determine when to halt an agent’s trial. To evaluate early-exit mechanisms, we introduce two metrics: one measures the reduction of **redundant steps** as a positive effect, and the other evaluates **progress degradation** as a negative effect. Experiments with 4 different LLMs across 5 embodied environments show significant efficiency improvements, with only minor drops in agent performance. We also validate a practical strategy where a stronger agent assists after an early-exit agent, achieving better performance with the same total steps. We will release our code to support further research.

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AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels
Lei Li | Xiangxu Zhang | Xiao Zhou | Zheng Liu

Medical information retrieval (MIR) is vital for accessing knowledge from electronic health records, scientific literature, and medical databases, supporting applications such as medical education, patient queries, and clinical diagnosis. However, effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. To address this challenge, we propose **S**elf-**L**earning **Hy**pothetical **D**ocument **E**mbeddings (**SL-HyDE**), a framework that leverages large language models (LLMs) to generate hypothetical documents conditioned on a query. These documents encapsulate essential medical context, guiding dense retrievers toward the most relevant results. SL-HyDE further employs a self-learning mechanism that iteratively improves pseudo-document generation and retrieval using unlabeled corpora, eliminating the need for labeled data. In addition, we introduce the Chinese Medical Information Retrieval Benchmark (CMIRB), a comprehensive evaluation suite reflecting real-world medical scenarios, comprising five tasks and ten datasets. By benchmarking ten models on CMIRB, we provide a rigorous standard for evaluating MIR systems. Experimental results demonstrate that SL-HyDE significantly outperforms HyDE in retrieval accuracy, while exhibiting strong generalization and scalability across diverse LLM and retriever configurations. Our code and data are publicly available at: https://github.com/ll0ruc/AutoMIR.

In this paper, we propose a method to improve the reasoning capabilities of Visual Question Answering (VQA) systems by integrating Dense Passage Retrievers (DPRs) with Vision Language Models (VLMs). While recent works focus on the application of knowledge graphs and chain-of-thought reasoning, we recognize that the complexity of graph neural networks and end-to-end training remain significant challenges. To address these issues, we introduce **R**elevance **G**uided **VQA** (**RG-VQA**), a retriever-generator pipeline that uses DPRs to efficiently extract relevant information from structured knowledge bases. Our approach ensures scalability to large graphs without significant computational overhead. Experiments on the ScienceQA dataset show that RG-VQA achieves state-of-the-art performance, surpassing human accuracy and outperforming GPT-4 by more than . This demonstrates the effectiveness of RG-VQA in boosting the reasoning capabilities of VQA systems and its potential for practical applications.

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Enhancing RAG Efficiency with Adaptive Context Compression
Shuyu Guo | Shuo Zhang | Zhaochun Ren

Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but incurs significant inference costs due to lengthy retrieved contexts. While context compression mitigates this issue, existing methods apply fixed compression rates—over-compressing simple queries or under-compressing complex ones. We propose Adaptive Context Compression for RAG (ACC-RAG), a framework that dynamically adjusts compression rates based on input complexity, optimizing inference efficiency without loss of accuracy. ACC-RAG combines a hierarchical compressor (for multi-granular embeddings) with a context selector to retain minimal sufficient information, akin to human skimming. Evaluated on Wikipedia and five QA datasets, ACC-RAG outperforms fixed-rate methods and unlocks >4× faster inference versus standard RAG while maintaining or improving accuracy.

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Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection
Debajyoti Mazumder | Aakash Kumar | Jasabanta Patro

In this paper, we reported our experiments with various strategies to improve code-mixed humour and sarcasm detection. Particularly, we tried three approaches: (i) native sample mixing, (ii) multi-task learning (MTL), and (iii) prompting and instruction finetuning very large multilingual language models (VMLMs). In native sample mixing, we added monolingual task samples to code-mixed training sets. In MTL learning, we relied on native and code-mixed samples of a semantically related task (hate detection in our case). Finally, in our third approach, we evaluated the efficacy of VMLMs via few-shot context prompting and instruction finetuning. Some interesting findings we got are (i) adding native samples improved humor (raising the F1-score up to 6.76%) and sarcasm (raising the F1-score up to 8.64%) detection, (ii) training MLMs in an MTL framework boosted performance for both humour (raising the F1-score up to 10.67%) and sarcasm (increment up to 12.35% in F1-score) detection, and (iii) prompting and instruction finetuning VMLMs couldn’t outperform the other approaches. Finally, our ablation studies and error analysis discovered the cases where our model is yet to improve. We provided our code for reproducibility.

Mathematical reasoning poses significant challenges for Large Language Models (LLMs) due to its demand for multi-step reasoning and abstract conceptual integration. While recent test-time scaling techniques rely heavily on high-quality, challenging problems, the scarcity of Olympiad-level math problems remains a bottleneck. We introduce CogAtom, a novel cognitive atom-based framework for synthesizing mathematically rigorous and cognitively diverse problems. Unlike prior approaches, CogAtom models problem construction as a process of selecting and recombining fundamental reasoning units, cognitive atoms, extracted from human-authored solutions. A diversity-promoting random walk algorithm enables exploration of the cognitive atom space, while a constraint-based recombination mechanism ensures logical soundness and structural validity. The combinatorial nature of the graph structure provides a near-infinite space of reasoning paths, and the walk algorithm systematically explores this space to achieve large-scale synthesis of high-quality problems; meanwhile, by controlling the number of cognitive atoms, we can precisely adjust problem difficulty, ensuring diversity, scalability, and controllability of the generated problems. Experimental results demonstrate that CogAtom outperforms existing methods in accuracy, reasoning depth, and diversity, generating problems that closely match the difficulty of AIME while exceeding it in structural variation. Our work offers a cognitively grounded pathway toward scalable, high-quality math problem generation.Our code is publicly available at https://github.com/Icarus-1111/CogAtom.

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Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs
Sungjae Lee | Hoyoung Kim | Jeongyeon Hwang | Eunhyeok Park | Jungseul Ok

Scaling test-time computation, generating and analyzing multiple or sequential outputs for a single input, has become a promising strategy for improving the reliability and quality of large language models (LLMs), as evidenced by advances in uncertainty quantification and multi-step reasoning. A key shared component is semantic clustering, which groups outputs that differ in form but convey the same meaning. Semantic clustering enables estimation of the distribution over the semantics of outputs and helps avoid redundant exploration of reasoning paths. However, existing approaches typically rely on external models, which introduce substantial computational overhead and often fail to capture context-aware semantics. We propose Latent Semantic Clustering (LSC), a lightweight and context-sensitive method that leverages the generator LLM’s internal hidden states for clustering, eliminating the need for external models. Our extensive experiment across various LLMs and datasets shows that LSC significantly improves the computational efficiency of test-time scaling while maintaining or exceeding the performance of existing methods.

Web banner advertisements, which are placed on websites to guide users to a targeted landing page (LP), are still often selected manually because human preferences are important in selecting which ads to deliver. To automate this process, we propose a new benchmark, BannerBench, to evaluate the human preference-driven banner selection process using vision-language models (VLMs). This benchmark assesses the degree of alignment with human preferences in two tasks: a ranking task and a best-choice task, both using sets of five images derived from a single LP. Our experiments show that VLMs are moderately correlated with human preferences on the ranking task. In the best-choice task, most VLMs perform close to chance level across various prompting strategies. These findings suggest that although VLMs have a basic understanding of human preferences, most of them struggle to pinpoint a single suitable option from many candidates.

Natural Language to SQL (NL2SQL) provides a new model-centric paradigm that simplifies database access for non-technical users by converting natural language queries into SQL commands. Recent advancements, particularly those integrating Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) reasoning, have made significant strides in enhancing NL2SQL performance. However, challenges such as inaccurate task decomposition and keyword extraction by LLMs remain major bottlenecks, often leading to errors in SQL generation. While existing datasets aim to mitigate these issues by fine-tuning models, they struggle with over-fragmentation of tasks and lack of domain-specific keyword annotations, limiting their effectiveness.To address these limitations, we present DeKeyNLU, a novel dataset which contains 1,500 meticulously annotated QA pairs aimed at refining task decomposition and enhancing keyword extraction precision for the RAG pipeline. Fine-tuned with DeKeyNLU, we propose DeKeySQL, a RAG-based NL2SQL pipeline that employs three distinct modules for user question understanding, entity retrieval, and generation to improve SQL generation accuracy. We benchmarked multiple model configurations within DeKeySQL RAG pipeline. Experimental results demonstrate that fine-tuning with DeKeyNLU significantly improves SQL generation accuracy on both BIRD (62.31% to 69.10%) and Spider (84.2% to 88.7%) dev datasets.

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Facilitating Cross-lingual Transfer of Empathy through Language-independent Latent Diffusion: A Case Study in Chinese
Junlin Li | Peng Bo | Yu-Yin Hsu

Human empathy builds on the shared pragmatic common ground among different languages. However, existing human empathy data is limited to English. Inspired by multilingual coactivation as the neurocognitive underpinning of human bilingual proficiency, which predicts empathy, we integrate language-independent diffusion processes to facilitate the cross-lingual transfer of empathy. Taking Chinese language varieties as the target domain, automatic and human evaluations demonstrate successful transfers of source empathy into target contexts without compromising linguistic naturalness. The results of this work offer empirical clues on the importance of pragmatic transferability of empathy and its cross-lingual effects in conversation.

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Evaluating Compound AI Systems through Behaviors, Not Benchmarks
Pranav Bhagat | K N Ajay Shastry | Pranoy Panda | Chaitanya Devaguptapu

Compound AI (CAI) systems, also referred to as LLM Agents, combine LLMs with retrievers and tools to enable information-seeking applications in the real-world. Thus, ensuring these systems perform reliably is critical. However, traditional evaluation using benchmark datasets and aggregate metrics often fails to capture their true operational performance. This is because understanding the operational efficacy of these information-seeking systems requires the ability to probe their behavior across a spectrum of simulated scenarios to identify potential failure modes. Thus, we present a behavior-driven evaluation framework that generates test specifications - explicit descriptions of expected system behaviors in specific scenarios - aligned with real usage contexts. These test specifications serve as formal declarations of system requirements that are then automatically transformed into concrete test cases. Specifically, our framework operates in two phases: (1) generating diverse test specifications via submodular optimization over semantic diversity and document coverage of the tests, and (2) implementing these specifications through graph-based pipelines supporting both tabular and textual sources. Evaluations on QuAC & HybriDialogue datasets, across SoTA LLMs, reveal that our framework identifies failure modes missed by traditional metrics, demonstrating failure rates twice as high as human-curated datasets.

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SciCompanion: Graph-Grounded Reasoning for Structured Evaluation of Scientific Arguments
Joshua Alan Flashner | Adithya Kulkarni | Dawei Zhou

The exponential growth of scientific publications has overwhelmed reviewers and researchers, with top conferences receiving thousands of submissions annually. Reviewers must assess feasibility, novelty, and impact under tight deadlines, often lacking tools to identify relevant prior work. Early-career researchers face similar challenges, with limited support to navigate fast-evolving fields. Existing LLM-based systems struggle with static retrieval, surface-level features, and lack multi-hop reasoning, leading to shallow or hallucinated assessments. Scientific evaluation requires a deep, relational understanding, which current retrieval-augmented generation (RAG) methods fail to achieve. We introduce SciCompanion, a graph-grounded reasoning framework for structured scientific evaluation. Given a paper or abstract-like input, SciCompanion builds a dynamic knowledge graph from recent publications, domain-specific databases, and curated metadata. It employs multi-hop reasoning to iteratively construct contextual graphs and generate structured critiques, enabling deeper exploration of scientific literature. Unlike sentiment-biased LLM evaluations, SciCompanion directly optimizes retrieval and graph refinement using Group Relative Policy Optimization (GRPO), producing reviews aligned with expert judgments. Experiments on ICLR and ACL datasets show that SciCompanion reduces evaluation error by over 30% compared to prompting-only baselines and allows smaller models to outperform larger ones. Evaluations across three datasets, using metrics for retrieval accuracy, semantic overlap, and multi-hop sensitivity, along with a case study, demonstrate SciCompanion’s robustness and versatility.

Infodemics and health misinformation have significant negative impact on individuals and society, exacerbating confusion and increasing hesitancy in adopting recommended health measures. Recent advancements in generative AI, capable of producing realistic, human-like text and images, have significantly accelerated the spread and expanded the reach of health misinformation, resulting in an alarming surge in its dissemination. To combat the infodemics, most existing work has focused on developing misinformation datasets from social media and fact-checking platforms, but has faced limitations in topical coverage, inclusion of AI-generation, and accessibility of raw content. To address these gaps, we present MM-Health, a large scale multimodal misinformation dataset in the health domain consisting of 34,746 news article encompassing both textual and visual information. MM-Health includes human-generated multimodal information (5,776 articles) and AI-generated multimodal information (28,880 articles) from various SOTA generative AI models. Additionally, We benchmarked our dataset against three tasks—reliability checks, originality checks, and fine-grained AI detection—demonstrating that existing SOTA models struggle to accurately distinguish the reliability and origin of information. Our dataset aims to support the development of misinformation detection across various health scenarios, facilitating the detection of human and machine-generated content at multimodal levels.

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Estimating Machine Translation Difficulty
Lorenzo Proietti | Stefano Perrella | Vilém Zouhar | Roberto Navigli | Tom Kocmi

Machine translation quality has steadily improved over the years, achieving near-perfect translations in recent benchmarks.These high-quality outputs make it difficult to distinguish between state-of-the-art models and to identify areas for future improvement.In this context, automatically identifying texts where machine translation systems struggle holds promise for developing more discriminative evaluations and guiding future research.In this work, we address this gap by formalizing the task of translation difficulty estimation, defining a text’s difficulty based on the expected quality of its translations.We introduce a new metric to evaluate difficulty estimators and use it to assess both baselines and novel approaches.Finally, we demonstrate the practical utility of difficulty estimators by using them to construct more challenging benchmarks for machine translation. Our results show that dedicated models outperform both heuristic-based methods and LLM-as-a-judge approaches, with sentinel-src achieving the best performance.Thus, we release two improved models for difficulty estimation, sentinel-src-24 and sentinel-src-25, which can be used to scan large collections of texts and select those most likely to challenge contemporary machine translation systems.

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TIU-Bench: A Benchmark for Evaluating Large Multimodal Models on Text-rich Image Understanding
Kun Zhang | Liqiang Niu | Zhen Cao | Fandong Meng | Jie Zhou

Text-rich images are ubiquitous in real-world applications, serving as a critical medium for conveying complex information and facilitating accessibility.Despite recent advances driven by Multimodal Large Language Models (MLLMs), existing benchmarks suffer from limited scale, fragmented scenarios, and evaluation protocols that fail to fully capture holistic image understanding.To address these gaps, we present TIU-Bench, a large-scale, multilingual benchmark comprising over 100,000 full-image annotations and 22,000 rigorously validated question-answer (QA) pairs that span 18 subtasks across diverse real-world scenarios.TIU-Bench introduces a novel full-image structured output format that jointly models geometric, textual, and relational information, enabling fine-grained evaluation of perception and reasoning capabilities. Furthermore, we propose a two-stage understanding framework named T2TIU, which first generates a structured representation of the entire image and subsequently conducts reasoning on this representation to address complex visual-textual queries.Extensive experiments on 10 state-of-the-art generative models highlight the challenges and opportunities in advancing text-rich image understanding.Our benchmark and framework provide a comprehensive platform for developing and evaluating next-generation multimodal AI systems.

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Breaking Token Into Concepts: Exploring Extreme Compression in Token Representation Via Compositional Shared Semantics
Kavin R V | Pawan Goyal

Standard language models employ unique, monolithic embeddings for each token, potentially limiting their ability to capture the multifaceted nature of word meanings. We investigate whether tokens can be more effectively represented through a compositional structure that accumulates diverse semantic facets. To explore this, we propose Aggregate Semantic Grouping (ASG), a novel approach leveraging Product Quantization (PQ). We apply ASG to standard transformer architectures (mBERT, XLM-R, mT5) and evaluate this representational scheme across diverse tasks (NLI, NER, QA), as well as a biomedical domain-specific benchmark (BC5CDR) using BioBERT. Our findings demonstrate that representing tokens compositionally via ASG achieves extreme compression in embedding parameters (0.4–0.5%) while maintaining >95% task performance relative to the base model, even in generative tasks and extends to both cross lingual transfer and domain-specific settings. These results validate the principle that tokens can be effectively modeled as combinations of shared semantic building blocks. ASG offers a simple yet concrete method for achieving this, showcasing how compositional representations can capture linguistic richness while enabling compact yet semantically rich models.

Recent text-to-SQL models have achieved strong performance, but their effectiveness remains largely confined to SQLite due to dataset limitations. However, real-world applications require SQL generation across multiple dialects with varying syntax and specialized features, which remains a challenge for current models. The main obstacle in building a dialect-aware model lies in acquiring high-quality dialect-specific data. Data generated purely through static prompting—without validating SQLs via execution—tends to be noisy and unreliable. Moreover, the lack of real execution environments in the training loop prevents models from grounding their predictions in executable semantics, limiting generalization despite surface-level improvements from data filtering. This work introduces ExeSQL, a text-to-SQL framework with execution-driven, agentic bootstrapping. The method consists of iterative query generation, execution-based filtering (e.g., rejection sampling), and preference-based training, enabling the model to adapt to new SQL dialects through verifiable, feedback-guided learning. Experiments show that ExeSQL bridges the dialect gap in text-to-SQL, achieving average improvements of 15.2%, 10.38%, and 4.49% over GPT-4o on PostgreSQL, MySQL, and Oracle, respectively, across multiple datasets of varying difficulty.

Language is not only a tool for communication but also a medium for human cognition and reasoning. If, as linguistic relativity suggests, the structure of language shapes cognitive patterns, then large language models (LLMs) trained on human language may also internalize the habitual logical structures embedded in different languages. To examine this hypothesis, we introduce BICAUSE, a structured bilingual dataset for causal reasoning, which includes semantically aligned Chinese and English samples in both forward and reversed causal forms. Our study reveals three key findings: (1) LLMs exhibit typologically aligned attention patterns, focusing more on causes and sentence-initial connectives in Chinese, while showing a more balanced distribution in English. (2) Models internalize language-specific preferences for causal components order and often rigidly apply them to atypical inputs, leading to degraded performance, especially in Chinese. (3) When causal reasoning succeeds, model representations converge toward semantically aligned abstractions across languages, indicating a shared understanding beyond surface form. Overall, these results suggest that LLMs not only mimic surface linguistic forms but also internalize the reasoning biases shaped by language. Rooted in cognitive linguistic theory, this phenomenon is for the first time empirically verified through structural analysis of model internals.

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Think Right, Not More: Test-Time Scaling for Numerical Claim Verification
Primakov Chungkham | Venktesh V | Vinay Setty | Avishek Anand

Fact-checking real-world claims, particularly numerical claims, is inherently complex that require multistep reasoning and numerical reasoning for verifying diverse aspects of the claim. Although large language models (LLMs) including reasoning models have made tremendous advances, they still fall short on fact-checking real-world claims that require a combination of compositional and numerical reasoning. They are unable to understand nuance of numerical aspects, and are also susceptible to the reasoning drift issue, where the model is unable to contextualize diverse information resulting in misinterpretation and backtracking of reasoning process. In this work, we systematically explore scaling test-time compute (TTS) for LLMs on the task of fact-checking complex numerical claims, which entails eliciting multiple reasoning paths from an LLM. We train a verifier model (VERIFIERFC) to navigate this space of possible reasoning paths and select one that could lead to the correct verdict. We observe that TTS helps mitigate the reasoning drift issue, leading to significant performance gains for fact-checking numerical claims. To improve compute efficiency in TTS, we introduce an adaptive mechanism that performs TTS selectively based on the perceived complexity of the claim. This approach achieves 1.8x higher efficiency than standard TTS, while delivering a notable 18.8% performance improvement over single-shot claim verification methods. Our code and data can be found at https://github.com/VenkteshV/VerifierFC

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Nexus: Adaptive Upcycling to Efficiently Pretrain Mixture of Experts
Nikolas Gritsch | Qizhen Zhang | Acyr Locatelli | Sara Hooker | Ahmet Üstün

Frontier language models are increasingly based on the Mixture of Experts (MoE) architecture, boosting the efficiency of training and inference by sparsely activating parameters. Nevertheless, training from scratch on trillions of tokens remains so expensive that most users can only finetune these models. In this work, we combine parameter reuse of dense models for the MoE layers ("*upcycling*”) with a novel, *adaptive* Nexus router that can integrate new experts into an existing trained model without hurting the performance on previous domains. Our router leverages the knowledge of each expert’s training data distribution via domain embeddings to initialize the router, improving specialization and allowing it to adapt faster to new domains than a standard MoE router. Nexus overturns the strict sequential separation between training and finetuning in classical approaches, allowing more powerful improvements to existing models at a later stage through long token-horizon trainings on new pretraining data. Our experiments show that Nexus achieves a relative gain of up to 2.1% over the baseline for initial upcycling, and an 18.8% relative gain for extending the MoE to a new domain with a new expert by using limited finetuning data. This flexibility of Nexus can power an open-source ecosystem where every user continuously assembles their own MoE-mix from a multitude of dense models.

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Exploring Context Strategies in LLMs for Discourse-Aware Machine Translation
Ritvik Choudhary | Rem Hida | Masaki Hamada | Hayato Futami | Toshiyuki Sekiya

While large language models (LLMs) excel at machine translation (MT), the impact of how LLMs utilize different forms of contextual information on discourse-level phenomena remains underexplored. We systematically investigate how different forms of context such as prior source sentences, models’ generated hypotheses, and reference translations influence standard MT metrics and specific discourse phenomena (formality, pronoun selection, and lexical cohesion). Evaluating multiple LLMs across multiple domains and language pairs, our findings consistently show that context boosts both translation and discourse-specific performance. Notably, the context strategy of combining source text with the model’s own prior hypotheses effectively improves discourse consistency without gold references, demonstrating effective use of model’s own imperfect generations as diverse contextual cues.

Political campaigns increasingly rely on targeted strategies to influence voters on social media. Often, such campaigns have been studied by analysing coordinated behaviour to identify communities of users who exhibit similar patterns. While these analyses are typically conducted on static networks, recent extensions to temporal networks allow tracking users who change communities over time, opening new opportunities to quantitatively study influence in social networks. As a first step toward this goal, we analyse the messages users were exposed to during the UK 2019 election, comparing those received by users who shifted communities with others covering the same topics.Our findings reveal 54 statistically significant linguistic differences and show that a subset of persuasion techniques, including loaded language, exaggeration and minimization, doubt, and flag-waving, are particularly relevant to users’ shifts. This work underscores the importance of analysing coordination from a temporal and dynamic perspective to infer the drivers of users’ shifts in online debate.

Large Reasoning Models (LRMs) demonstrate strong performance on complex tasks through chain-of-thought (CoT) reasoning. However, they suffer from high inference latency due to lengthy reasoning chains. In this paper, we propose SpecCoT, a collaborative framework that combines large and small models for effective yet efficient reasoning. Unlike traditional speculative decoding, which operates at the token level, SpecCoT adopts a step-level verification strategy: the large model first establishes the reasoning direction, and for each intermediate step, the small model generates multiple candidate drafts in parallel. The large model then verifies these drafts, either selecting the most suitable one or rejecting them all and generating its own. SpecCoT approach balances reasoning quality with inference efficiency through fine-grained model cooperation. Experiments across diverse tasks show SpecCoT reduces inference latency by 1.7-4.1× while maintaining comparable accuracy to standard large model inference.

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A Similarity Measure for Comparing Conversational Dynamics
Sang Min Jung | Kaixiang Zhang | Cristian Danescu-Niculescu-Mizil

The quality of a conversation goes beyond the individual quality of each reply, and instead emerges from how these combine into interactional dynamics that give the conversation its distinctive overall “shape”. However, there is no robust automated method for comparing conversations in terms of their overall dynamics. Such methods could enhance the analysis of conversational data and help evaluate conversational agents more holistically.In this work, we introduce a similarity measure for comparing conversations with respect to their dynamics. We design a validation procedure for testing the robustness of the metric in capturing differences in conversation dynamics and for assessing its sensitivity to the topic of the conversations. To illustrate the measure’s utility, we use it to analyze conversational dynamics in a large online community, bringing new insights into the role of situational power in conversations.

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AgentDrug: Utilizing Large Language Models in an Agentic Workflow for Zero-Shot Molecular Optimization
Le Huy Khiem | Ting Hua | Nitesh V Chawla

Molecular optimization—modifying a given molecule to improve desired properties—is a fundamental task in drug discovery. While LLMs hold the potential to solve this task using natural language to drive the optimization, straightforward prompting achieves limited accuracy. In this work, we propose AgentDrug, an agentic workflow that leverages LLMs in a structured refinement process to achieve significantly higher accuracy. AgentDrug defines a nested refinement loop: the inner loop uses feedback from cheminformatics toolkits to validate molecular structures, while the outer loop guides the LLM with generic feedback and a gradient-based objective to steer the molecule toward property improvement. We evaluate AgentDrug on benchmarks with both single- and multi-property optimization under loose and strict thresholds. Results demonstrate significant performance gains over previous methods. With Qwen-2.5-3B, AgentDrug improves accuracy by 20.7% (loose) and 16.8% (strict) on six single-property tasks, and by 7.0% and 5.3% on eight multi-property tasks. With larger model Qwen-2.5-7B, AgentDrug further improves accuracy on 6 single-property objectives by 28.9% (loose) and 29.0% (strict), and on 8 multi-property objectives by 14.9% (loose) and 13.2% (strict).

Large language models (LLMs) develop the in-context learning capability through pretraining and instruction tuning, enabling task adaptation without parameter updates. Self-refinement is a manifestation of this capability, which allows LLMs to iteratively refine the output using self-generated feedback. However, empirical observations reveal Inference-Free Self-Refinement (IFSR) in preference alignment: LLMs generate preference-improved output via fixed instructions, requiring no specific feedback, even no initial responses. There are two key components of the IFSR in preference alignment. The refining instruction is a fixed instruction that constrains the output distribution from a preference-semantic perspective. During training, it facilitates joint learning of preference-related semantic representations and data distribution alignment. The pseudo reference response is constructed from paired preference data and serves as a demonstration to guide the output distribution. It mitigates off-policy distributional bias while enhancing token-level preference learning in training. Experiments across multiple datasets demonstrate that incorporating IFSR into preference alignment yields performance improvement over 10%. Further ablation studies reveal additional characteristics and potential principles of IFSR.

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Guaranteed Guess: A Language Modeling Approach for CISC-to-RISC Transpilation with Testing Guarantees
Ahmed Heakl | Sarim Hashmi | Chaimaa Abi | Celine Lee | Abdulrahman Mahmoud

The hardware ecosystem is rapidly evolving, with increasing interest in translating low-level programs across different *instruction set architectures* (ISAs) in a quick, flexible, and correct way to enhance the portability and longevity of existing code. A particularly challenging class of this transpilation problem is translating between complex- (CISC) and reduced- (RISC) hardware architectures, due to fundamental differences in instruction complexity, memory models, and execution paradigms. In this work, we introduce GG (**G**uaranteed **G**uess), an ISA-centric transpilation pipeline that combines the translation power of pre-trained large language models (LLMs) with the rigor of established software testing constructs. Our method generates candidate translations using an LLM from one ISA to another, and embeds such translations within a software-testing framework to build quantifiable confidence in the translation. We evaluate our GG approach over two diverse datasets, enforce high code coverage (>98%) across unit tests, and achieve functional/semantic correctness of 99% on HumanEval programs and 49% on BringupBench programs, respectively. Further, we compare our approach to the state-of-the-art Rosetta 2 framework on Apple Silicon, showcasing 1.73× faster runtime performance, 1.47× better energy efficiency, and 2.41× better memory usage for our transpiled code, demonstrating the effectiveness of GG for real-world CISC-to-RISC translation tasks. We will open-source our codes, data, models, and benchmarks to establish a common foundation for ISA-level code translation research.

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StructuThink: Reasoning with Task Transition Knowledge for Autonomous LLM-Based Agents
Haiyu Zhao | Zhenyu Guo | Chunhong Zhang | Ziyu Zhou | Zheng Hu

Decision-making tasks have highlighted fundamental challenges in grounding decisions within real-world contexts. Traditional decision knowledge utilization methods often struggle to effectively integrate structured decision constraints, limiting their ability to decompose high-level tasks, maintain logical consistency, and adapt to dynamic environments. To bridge this gap, we introduce StructuThink, a knowledge-structured reasoning framework that enhances LLM-based agents with explicit decision constraints. Specifically, we propose the Task Transition Knowledge Graph (TTKG) that learning decision knowledge in embodied scenarios. Leveraging this knowledge, we propose the StructuThink framework, comprising a subtask chain constructor for grounding natural language instructions and a constraint-based executor for adaptive and consistent decision-making. We validate StructuThink across multiple benchmarks, including ALFWorld and WebShop, where it achieves higher task success rates (improving by up to 7%) and more efficient action sequences (requiring up to 15% fewer steps) than baseline methods. Our approach enables LLMs to more effectively ground decision-making in domain-specific scenarios, enhancing both interpretability and reliability, thus paving the way for more reliable and adaptable decision-making systems.

Most recommender systems focus on short-term objectives such as click-through rate, often at the expense of long-term user satisfaction. This can lead to echo chambers, where users are repeatedly exposed to redundant content. While recent efforts integrate Large Language Models (LLMs) into recommendation, they typically inherit this short-sighted focus. In this work, we highlight unpaired feedback—implicit signals such as continued engagement (positive) or silent disengagement (negative) that lack explicit contrastive labels—as a key challenge for long-term recommendation. Effectively learning from such feedback is crucial for improving LLM-based recommenders in dynamic user environments. To this end, we propose ULRec (Unpaired Feedback for Long-Term LLM-based Recommendation Tuning), a simple framework that fine-tunes LLMs using both positive and negative unpaired feedback. ULRec leverages the KTO algorithm to incorporate these signals without requiring paired supervision. Despite its simplicity, ULRec consistently improves long-term recommendation performance, demonstrating the value of modeling unpaired user feedback.

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Investigating Multi-layer Representations for Dense Passage Retrieval
Zhongbin Xie | Thomas Lukasiewicz

Dense retrieval models usually adopt vectors from the last hidden layer of the document encoder to represent a document, which is in contrast to the fact that representations in different layers of a pre-trained language model usually contain different kinds of linguistic knowledge, and behave differently during fine-tuning. Therefore, we propose to investigate utilizing representations from multiple encoder layers to make up the representation of a document, which we denote Multi-layer Representations (MLR). We first investigate how representations in different layers affect MLR’s performance under the multi-vector retrieval setting, and then propose to leverage pooling strategies to reduce multi-vector models to single-vector ones to improve retrieval efficiency. Experiments demonstrate the effectiveness of MLR over dual encoder, ME-BERT and ColBERT in the single-vector retrieval setting, as well as demonstrate that it works well with other advanced training techniques such as retrieval-oriented pre-training and hard negative mining.

Large language models (LLMs) face challenges with internal knowledge inaccuracies and outdated information. Knowledge editing has emerged as a pivotal approach to mitigate these issues. Although current knowledge editing techniques exhibit promising performance in single-hop reasoning tasks, they show limitations when applied to multi-hop reasoning. Drawing on cognitive neuroscience and the operational mechanisms of LLMs, we hypothesize that the residual single-hop knowledge after editing causes edited models to revert to their original answers when processing multihop questions, thereby undermining their performance in multi-hop reasoning tasks. To validate this hypothesis, we conduct a series of experiments that empirically confirm our assumptions. Building on the validated hypothesis, we propose a novel knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE). Specifically, we design an erasure function for residual knowledge and an injection function for new knowledge. Through joint optimization, we derive the optimal recall vector, which is subsequently utilized within a rank-one editing framework to update the parameters of targeted model layers. Extensive experiments on GPT-J (6B) and LLaMA-2 (7B) demonstrate that KELE substantially enhances the multi-hop reasoning capability of edited LLMs.

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Dissecting Persona-Driven Reasoning in Language Models via Activation Patching
Ansh Poonia | Maeghal Jain

Large language models (LLMs) exhibit remarkable versatility in adopting diverse personas. In this study, we examine how assigning a persona influences a model’s reasoning on an objective task. Using activation patching, we take a first step toward understanding how key components of the model encode persona-specific information. Our findings reveal that the early Multi-Layer Perceptron (MLP) layers attend not only to the syntactic structure of the input but also process its semantic content. These layers transform persona tokens into richer representations, which are then used by the middle Multi-Head Attention (MHA) layers to shape the model’s output. Additionally, we identify specific attention heads that disproportionately attend to racial and color-based identities.

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PUER: Boosting Few-shot Positive-Unlabeled Entity Resolution with Reinforcement Learning
Yaoshu Wang | Mengyi Yan | Wei Wang

Entity resolution is a fundamental problem in data management that aims to identify all duplicate entries within collections of multi-attribute tuples. Most existing works focus on supervised learning, relying on large amounts of high-quality labeled data, including both positive and negative tuple pairs that are meticulously prepared. However, in reality, the manual annotation process is labor-intensive; in particular, selecting high-quality negative data for labeling is both important and challenging. In this paper, we propose an end-to-end ER solution, PUER, to address low-resource entity resolution (ER) by leveraging Large Language Models (LLMs) in a Positive-Unlabeled (PU) learning setting, where only a small number of positively labeled examples, e.g., 50, and unlabeled data are provided. Unlike directly fine-tuning LLMs in a supervised manner, we solve the entity matching task using reinforcement learning and propose a self-adaptive reward function in the process of RL. To enhance performance, we design an iterative workflow based on the co-training mechanism that fully utilizes entity blocking component to assist the entity matching. This workflow aims to improve the robustness and quality of pseudo-labels so that the performance of entity matching improves. Comprehensive experimental results on various benchmark datasets demonstrate the superiority of PUER. Full version and code are available.

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Toward the Automatic Detection of Word Meaning Negotiation Indicators in Conversation
Aina Garí Soler | Matthieu Labeau | Chloé Clavel

Word Meaning Negotiations (WMN) are sequences in conversation where speakers collectively discuss and shape word meaning. These exchanges can provide insight into conversational dynamics and word-related misunderstandings, but they are hard to find in corpora. In order to facilitate data collection and speed up the WMN annotation process, we introduce the task of detecting WMN indicators – utterances where a speaker signals the need to clarify or challenge word meaning. We train a wide range of models and reveal the difficulty of the task. Our models have better precision than previous regular-expression based approaches and show some generalization abilities, but have moderate recall. However, this constitutes a promising first step toward an iterative process for obtaining more data.

Prompt tuning for Large Language Models (LLMs) is vulnerable to backdoor attacks. Existing methods find backdoor attacks to be a significant threat in data-rich scenarios. However, in data-limited scenarios, these methods have difficulty capturing precise backdoor patterns, leading to weakened backdoor attack capabilities and significant side effects for the LLMs, which limits their practical relevance. To explore this problem, we propose a backdoor attacks through contrastive-enhanced machine unlearning in data-limited scenarios, called BCU. Specifically, BCU introduces a multi-objective machine unlearning method to capture precise backdoor patterns by forgetting the association between non-trigger data and the backdoor patterns, reducing side effects. Moreover, we design a contrastive learning strategy to enhance the association between triggers and backdoor patterns, improving the capability of backdoor attacks. Experimental results on 6 NLP datasets and 4 LLMs show that BCU exhibits strong backdoor attack capabilities and slight side effects, whether the training data is rich or limited. Our findings highlight practical security risks of backdoor attacks against LLMs, necessitating further research for security purposes. Our code is available at https://github.com/AHU-YangSJ/BCU.

While large language models (LLMs) demonstrate impressive capabilities, their reliance on parametric knowledge often leads to factual inaccuracies. Retrieval-Augmented Generation (RAG) mitigates this by leveraging external documents, yet existing approaches treat retrieved passages as isolated chunks, ignoring valuable structure that is crucial for document organization. Motivated by this gap, we propose Retrieve-DocumentRoute-Read (RDR2), a novel framework that explicitly incorporates structural information throughout the RAG process. RDR2 employs an LLM-based router to dynamically navigate document structure trees, jointly evaluating content relevance and hierarchical relationships to assemble optimal evidence. Our key innovation lies in formulating document routing as a trainable task, with automatic action curation and structure-aware passage selection inspired by human reading strategies. Through comprehensive evaluation on five challenging datasets, RDR2 achieves state-of-the-art performance, demonstrating that explicit structural awareness significantly enhances RAG systems’ ability to acquire and utilize knowledge, particularly in complex scenarios requiring multi-document synthesis.

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QEVA: A Reference-Free Evaluation Metric for Narrative Video Summarization with Multimodal Question Answering
Woojun Jung | Junyeong Kim

Video-to-text summarization remains underexplored in terms of comprehensive evaluation methods. Traditional n-gram overlap-based metrics and recent large language model (LLM)-based approaches depend heavily on human-written reference summaries, limiting their practicality and sensitivity to nuanced semantic aspects. In this paper, we propose QEVA, a reference-free metric evaluating candidate summaries directly against source videos through multimodal question answering. QEVA assesses summaries along three clear dimensions: Coverage, Factuality, and Temporal Coherence. We also introduce MLVU(VS)-Eval, a new annotated benchmark derived from the MLVU dataset, comprising 800 summaries generated from 200 videos using state-of-the-art video-language multimodal models. This dataset establishes a transparent and consistent framework for evaluation. Experimental results demonstrate that QEVA shows higher correlation with human judgments compared to existing approaches, as measured by Kendall’s 𝜏_b, 𝜏_c, and Spearman’s 𝜌. We hope that our benchmark and metric will facilitate meaningful progress in video-to-text summarization research and provide valuable insights for the development of future evaluation methods.

Large Language Models (LLMs) often exhibit deficiencies with complex reasoning tasks, such as maths, which we attribute to the discrepancy between human reasoning patterns and those presented in the LLMs’ training data. When dealing with complex problems, humans tend to think carefully before expressing solutions. However, they often do not articulate their inner thoughts, including their intentions and chosen methodologies. Consequently, critical insights essential for bridging reasoning steps may be absent in training data collected from human sources. To bridge this gap, we proposes inserting insights between consecutive reasoning steps, which review the status and initiate the next reasoning steps. Unlike prior prompting strategies that rely on a single or a workflow of static prompts to facilitate reasoning, insights are proactively generated to guide reasoning processes. We implement our idea as a reasoning framework, named Thinking Before You Speak (TBYS), and design a pipeline for automatically collecting and filtering in-context examples for the generation of insights, which alleviates human labeling efforts and fine-tuning overheads. Experiments on challenging mathematical datasets verify the effectiveness of TBYS. Project website: https://gitee.com/jswrt/TBYS

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Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation
Wei-Hsiang Lin | Sheng-Lun Wei | Hen-Hsen Huang | Hsin-Hsi Chen

LLM-as-Judge frameworks are increasingly popular for AI evaluation, yet research findings on the relationship between models’ generation and judgment abilities remain inconsistent. We investigate this relationship through systematic dataset- and instance-level analyses across 11 models and 21 diverse tasks. Despite both capabilities relying on the same underlying knowledge, our analyses reveal they are only weakly correlated, primarily due to LLMs’ sensitivity to the responses being judged. To address this, we propose a self-reference-guided evaluation strategy that leverages a model’s own answers as references. This approach significantly strengthens the correlation between generation and judgment abilities, offering a practical path to align these skills and providing a reliable proxy for model selection in evaluation tasks.

Research on bias in Text-to-Image (T2I) models has primarily focused on demographic representation and stereotypical attributes, overlooking a fundamental question: how does grammatical gender influence visual representation across languages? We introduce a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations (e.g., “une sentinelle” - grammatically feminine in French but referring to the stereotypically masculine concept “guard”). Our dataset spans five gendered languages (French, Spanish, German, Italian, Russian) and two gender-neutral control languages (English, Chinese), comprising 800 unique prompts that generated 28,800 images across three state-of-the-art T2I models. Our analysis reveals that grammatical gender dramatically influences image generation: masculine grammatical markers increase male representation to 73% on average (compared to 22% with gender-neutral English), while feminine grammatical markers increase female representation to 38% (compared to 28% in English). These effects vary systematically by language resource availability and model architecture, with high-resource languages showing stronger effects. Our findings establish that language structure itself, not just content, shapes AI-generated visual outputs, introducing a new dimension for understanding bias and fairness in multilingual, multimodal systems.

Large language models (LLMs) have demonstrated strong capabilities in using external tools to address user inquiries. However, most existing evaluations assume tool use in short contexts, offering limited insight into model behavior during realistic long-term interactions. To fill this gap, we introduce ToolHaystack, a benchmark for testing the tool use capabilities in long-term interactions. Each test instance in ToolHaystack includes multiple tasks execution contexts and realistic noise within a continuous conversation, enabling assessment of how well models maintain context and handle various disruptions. By applying this benchmark to 14 state-of-the-art LLMs, we find that while current models perform well in standard multi-turn settings, they often significantly struggle in ToolHaystack, highlighting critical gaps in their long-term robustness not revealed by previous tool benchmarks.

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GraphCheck: Multipath Fact-Checking with Entity-Relationship Graphs
Hyewon Jeon | Jay-Yoon Lee

Automated fact-checking aims to assess the truthfulness of textual claims based on relevant evidence. However, verifying complex claims that require multi-hop reasoning remains a significant challenge. We propose **GraphCheck**, a novel framework that transforms claims into entity-relationship graphs for structured and systematic fact-checking. By explicitly modeling both explicit and latent entities and exploring multiple reasoning paths, GraphCheck enhances verification robustness. While GraphCheck excels in complex scenarios, it may be unnecessarily elaborate for simpler claims. To address this, we introduce **DP-GraphCheck**, a variant that employs a lightweight strategy selector to choose between direct prompting and GraphCheck adaptively. This selective mechanism improves both accuracy and efficiency by applying the appropriate level of reasoning to each claim. Experiments on the HOVER and EX-FEVER datasets demonstrate that our approach outperforms existing methods in verification accuracy, while achieving strong computational efficiency despite its multipath exploration. Moreover, the strategy selection mechanism in DP-GraphCheck generalizes well to other fact-checking pipelines, highlighting the broad applicability of our framework.

Recent works improving LLM math reasoning with synthetic data have used unique setups, making comparison of data synthesis strategies impractical. This leaves many unanswered questions about the roles of different factors in the synthetic data pipeline, such as the impact of filtering low-quality problems. To address this gap, we introduce FLAMES, a Framework for LLM Assessment of Math rEasoning Data Synthesis, and perform a systematic study of 10 existing data synthesis strategies and multiple other factors impacting the performance of synthetic math reasoning data. Our FLAMES experiments provide several valuable insights about the optimal balance of difficulty and diversity of synthetic data. First, data agents designed to increase problem complexity lead to best improvements on most math metrics. Second, with a fixed data generation budget, keeping higher problem coverage is more important than keeping only problems with reliable solutions. Third, GSM8K- and MATH-based synthetic data can lead to improvements on competition-level benchmarks, showcasing easy-to-hard generalization. Leveraging insights from our FLAMES experiments, we design two novel data synthesis strategies for improving out-of-domain generalization and robustness. Further, we develop the FLAMES dataset, an effective blend of our novel and existing data synthesis strategies, outperforming public datasets on OlympiadBench (+15.7), CollegeMath (+4.5), GSMPlus (+6.5), and MATH (+3.1). Fine-tuning Qwen2.5-Math-7B on the FLAMES dataset achieves 81.4% on MATH, surpassing larger Llama3 405B, GPT-4o and Claude 3.5 Sonnet.

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POW: Political Overton Windows of Large Language Models
Leif Azzopardi | Yashar Moshfeghi

Political bias in Large Language Models (LLMs) presents a growing concern for the responsible deployment of AI systems. Traditional audits often attempt to locate a model’s political position as a point estimate, masking the broader set of ideological boundaries that shape what a model is willing or unwilling to say. In this paper, we draw upon the concept of the Overton Window as a framework for mapping these boundaries: the range of political views that a given LLM will espouse, remain neutral on, or refuse to endorse. To uncover these windows, we applied an auditing-based methodology, called PRISM, that probes LLMs through task-driven prompts designed to elicit political stances indirectly. Using the Political Compass Test, we evaluated twenty-eight LLMs from eight providers to reveal their distinct Overton Windows. While many models default to economically left and socially liberal positions, we show that their willingness to express or reject certain positions varies considerably, where DeepSeek models tend to be very restrictive in what they will discuss and Gemini models tend to be most expansive. Our findings demonstrate that Overton Windows offer a richer, more nuanced view of political bias in LLMs and provide a new lens for auditing their normative boundaries.

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Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Models
Ting Cai | Stephen Sheen | AnHai Doan

Expanding the abbreviated column names of tables, such as “esal” to “employee salary”, is critical for many downstream NLP tasks for tabular data, such as NL2SQL, table QA, and keyword search. This problem arises in enterprises, domain sciences, government agencies, and more. In this paper, we make three contributions that significantly advance the state of the art. First, we show that the synthetic public data used by prior work has major limitations, and we introduce four new datasets in enterprise/science domains, with real-world abbreviations. Second, we show that accuracy measures used by prior work seriously undercount correct expansions, and we propose new synonym-aware measures that capture accuracy much more accurately. Finally, we develop Columbo, a powerful LLM-based solution that exploits context, rules, chain-of-thought reasoning, and token-level analysis. Extensive experiments show that Columbo significantly outperforms NameGuess, the current most advanced solution, by 4-29%, over five datasets. Columbo has been used in production on EDI, a major data lake for environmental sciences.

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RTTC: Reward-Guided Collaborative Test-Time Compute
Juan Pablo Munoz | Jinjie Yuan

Test-Time Compute (TTC) has emerged as a powerful paradigm for enhancing the performance of Large Language Models (LLMs) at inference, leveraging strategies such as Test-Time Training (TTT) and Retrieval-Augmented Generation (RAG). However, the optimal adaptation strategy varies across queries, and indiscriminate application of TTC strategy incurs substantial computational overhead. In this work, we introduce Reward-Guided Test-Time Compute (RTTC), a novel framework that adaptively selects the most effective TTC strategy for each query via a pretrained reward model, maximizing downstream accuracy across diverse domains and tasks. RTTC operates in a distributed server-client architecture, retrieving relevant samples from a remote knowledge base and applying RAG or lightweight fine-tuning on client devices only when necessary. To further mitigate redundant computation, we propose Query-State Caching, which enables the efficient reuse of historical query states at both retrieval and adaptation levels. Extensive experiments across multiple LLMs and benchmarks demonstrate that RTTC consistently achieves superior accuracy compared to vanilla RAG or TTT, validating the necessity of adaptive, reward-guided TTC selection and the potential of RTTC for scalable, high-performance language model adaptation.

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AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering
Ziqing Wang | Chengsheng Mao | Xiaole Wen | Yuan Luo | Kaize Ding

Medical Multimodal Large Language Models (Med-MLLMs) have shown great promise in medical visual question answering (Med-VQA). However, when deployed in low-resource settings where abundant labeled data are unavailable, existing Med-MLLMs commonly fail due to their medical reasoning capability bottlenecks: (i) the intrinsic reasoning bottleneck that ignores the details from the medical image; (ii) the extrinsic reasoning bottleneck that fails to incorporate specialized medical knowledge. To address those limitations, we propose AMANDA, a training-free agentic framework that performs medical knowledge augmentation via LLM agents. Specifically, our intrinsic medical knowledge augmentation focuses on coarse-to-fine question decomposition for comprehensive diagnosis, while extrinsic medical knowledge augmentation grounds the reasoning process via biomedical knowledge graph retrieval. Extensive experiments across eight Med-VQA benchmarks demonstrate substantial improvements in both zero-shot and few-shot Med-VQA settings. The code is available at https://github.com/REAL-Lab-NU/AMANDA.

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Mixed Signals: Decoding VLMs’ Reasoning and Underlying Bias in Vision-Language Conflict
Pouya Pezeshkpour | Moin Aminnaseri | Estevam Hruschka

Vision-language models (VLMs) have demonstrated impressive performance by effectively integrating visual and textual information to solve complex tasks. However, it is not clear how these models reason over the visual and textual data together, nor how the flow of information between modalities is structured. In this paper, we examine how VLMs reason by analyzing their biases when confronted with scenarios that present conflicting image and text cues—a common occurrence in real-world applications. To uncover the extent and nature of these biases, we build upon existing benchmarks to create five datasets containing mismatched image-text pairs, covering topics in mathematics, science, and visual descriptions. Our analysis shows that VLMs favor text in simpler queries but shift toward images as query complexity increases. This bias correlates with model scale, with the difference between the percentage of image- and text-preferred responses ranging from +56.8% (image favored) to -85.1% (text favored), depending on the task and model. In addition, we explore three mitigation strategies: simple prompt modifications, modifications that explicitly instruct models on how to handle conflicting information (akin to chain-of-thought prompting), and a task decomposition strategy that analyzes each modality separately before combining their results. Our findings indicate that the effectiveness of these strategies in identifying and mitigating bias varies significantly and is closely linked to the model’s overall performance on the task and the specific modality in question. We released our dataset and code.

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Mitigating Hallucination in Large Vision-Language Models through Aligning Attention Distribution to Information Flow
Jianfei Zhao | Feng Zhang | Xin Sun | Chong Feng

Due to the unidirectional masking mechanism, Decoder-Only models propagate information from left to right. LVLMs (Large Vision-Language Models) follow the same architecture, with visual information gradually integrated into semantic representations during forward propagation. Through systematic analysis, we observe that over 80% of the visual information is absorbed into the semantic representations. However, the model’s attention still predominantly focuses on the visual representations. This misalignment between the attention distribution and the actual information flow undermines the model’s visual understanding ability and contributes to hallucinations.To address this issue, we enhance the model’s visual understanding by leveraging the core information embedded in semantic representations. Specifically, we identify attention heads that focus on core semantic representations based on their attention distributions. Then, through a two-stage optimization paradigm, we propagate the advantages of these attention heads across the entire model, aligning the attention distribution with the actual information flow.We evaluate our method on three image captioning benchmarks using five different LVLMs,demonstrating its effectiveness in significantly reducing hallucinations. Further experiments reveal a trade-off between reduced hallucinations and richer details. Notably, our method allows for manual adjustment of the model’s conservativeness, enabling flexible control to meet diverse real-world requirements.

Developers using LLMs and LLM-based agents in their applications have provided plenty of anecdotal evidencethat in-context-learning (ICL) is fragile. In this paper, we show that in addition to the quantity and quality of examples, the order in which the in-context examples are listed in the prompt affects the output of the LLM and, consequently, their performance. While prior work has explored improving ICL through dataset-dependent techniques, we introduce , a purely inference-time, dataset-free optimization method that efficiently determines the best example order. OptiSeq leverages log probabilities of LLM-generated outputs to systematically prune the search space of possible orderings and recommend the best order(s) by distinguishing orderings that yield high levels of accuracy and those that underperform. Extensive empirical evaluation on multiple LLMs, datasets, and prompts demonstrates that OptiSeq improves accuracy by 5.5 - 10.5 percentage points across multiple tasks.

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Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts
Devvrat Joshi | Islem Rekik

Extracting entities and relations from scientific text is challenging due to long sentences with densely packed entities. Pipeline approaches address this by first extracting entities and then predicting relations between all possible entity pairs. Since the relation extraction phase operates over this exhaustive set, the inclusion of candidate pairs that may be semantically related but lack syntactic proximity introduces precision errors, ultimately reducing Rel+ F1 metric. We propose a simple yet effective syntactic filtering method based on dependency parsing to prune unlikely entity pairs before relation prediction. By leveraging syntactic proximity in the dependency parse tree, our approach retains structurally plausible pairs and reduces false positives in downstream relation classification. Our method is grounded in consistent statistical patterns observed across all evaluated datasets, reinforcing its generalizability and effectiveness. We integrate this filtering step into architectures such as PL-Marker and HGERE, and evaluate its impact across multiple datasets. Our method improves Rel+ F1 scores significantly by an absolute increase of 3.5–10.3% on SciERC, SciER, and ACE05 datasets. These results highlight the importance of syntactic cues for accurate relation extraction in complex domains like scientific literature.

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DIPLomA: Efficient Adaptation of Instructed LLMs to Low-Resource Languages via Post-Training Delta Merging
Ixak Sarasua Antero | Ander Corral | Xabier Saralegi

This paper investigates how open-weight instruction-tuned large language models (LLMs) can be efficiently adapted to low-resource languages without requiring costly large-scale post-training. We introduce DIPLomA (Decoupled Instruction-Preserving Language Adaptation), a lightweight delta-based transfer strategy that provides a practical and effective solution for this scenario. DIPLomA decouples language adaptation from post-training alignment by first continually pretraining a foundational LLM on a modest amount of monolingual target-language data while anchoring on English replay, and then injecting instruction-following capabilities via delta-based weight merging from the instructed counterpart of the base LLM. We evaluate DIPLomA on Basque and validate its generality on Welsh and Swahili, demonstrating consistent and substantial gains in instruction-following, linguistic proficiency, and safety. Compared to strong baselines, our method achieves average relative improvements of 50 points in Basque, 63 in Welsh, and 51 in Swahili, while preserving the original model’s multilingual performance. These results highlight DIPLomA as an effective, resource-efficient strategy for bringing high-quality instruction alignment to underrepresented languages at scale.

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Reliability Crisis of Reference-free Metrics for Grammatical Error Correction
Takumi Goto | Yusuke Sakai | Taro Watanabe

Reference-free evaluation metrics for grammatical error correction (GEC) have achieved high correlation with human judgments.However, these metrics are not designed to evaluate adversarial systems that aim to obtain unjustifiably high scores. The existence of such systems undermines the reliability of automatic evaluation, as it can mislead users in selecting appropriate GEC systems. In this study, we propose adversarial attack strategies for four reference-free metrics: SOME, Scribendi, IMPARA, and LLM-based metrics, and demonstrate that our adversarial systems outperform the current state-of-the-art. These findings highlight the need for more robust evaluation methods.

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Who Speaks Matters: Analysing the Influence of the Speaker’s Linguistic Identity on Hate Classification
Ananya Malik | Kartik Sharma | Shaily Bhatt | Lynnette Hui Xian Ng

Large Language Models (LLMs) offer a lucrative promise for scalable content moderation, including hate speech detection. However, they are also known to be brittle and biased against marginalised communities and dialects. This requires their applications to high-stakes tasks like hate speech detection to be critically scrutinized. In this work, we investigate the robustness of hate speech classification using LLMs particularly when explicit and implicit markers of the speaker’s ethnicity are injected into the input. For explicit markers, we inject a phrase that mentions the speaker’s linguistic identity. For the implicit markers, we inject dialectal features. By analysing how frequently model outputs flip in the presence of these markers, we reveal varying degrees of brittleness across 3 LLMs and 1 LM and 5 linguistic identities. We find that the presence of implicit dialect markers in inputs causes model outputs to flip more than the presence of explicit markers. Further, the percentage of flips varies across ethnicities. Finally, we find that larger models are more robust. Our findings indicate the need for exercising caution in deploying LLMs for high-stakes tasks like hate speech detection.

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Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model’s Empathy
Ananya Malik | Nazanin Sabri | Melissa M. Karnaze | Mai ElSherief

Large Language Models’ (LLMs) ability to converse naturally is empowered by their ability to empathetically understand and respond to their users. However, emotional experiences are shaped by demographic and cultural contexts. This raises an important question: Can LLMs demonstrate equitable empathy across diverse user groups? We propose a framework to investigate how LLMs’ cognitive and affective empathy vary across user personas defined by intersecting demographic attributes. Our study introduces a novel intersectional analysis spanning 315 unique personas, constructed from combinations of age, culture, and gender, across four LLMs. Results show that attributes profoundly shape a model’s empathetic responses. Interestingly, we see that adding multiple attributes at once can attenuate and reverse expected empathy patterns. We show that they broadly reflect real-world empathetic trends, with notable misalignments for certain groups, such as those from Confucian culture. We complement our quantitative findings with qualitative insights to uncover model behaviour patterns across different demographic groups. Our findings highlight the importance of designing empathy-aware LLMs that account for demographic diversity to promote more inclusive and equitable model behaviour.

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Active Learning for Multidialectal Arabic POS Tagging
Diyam Akra | Mohammed Khalilia | Mustafa Jarrar

Multidialectal Arabic POS tagging is challenging due to the morphological richness and high variability among dialects. While POS tagging for MSA has advanced thanks to the availability of annotated datasets, creating similar resources for dialects remains costly and labor-intensive. Increasing the size of annotated datasets does not necessarily result in better performance. Active learning offers a more efficient alternative by prioritizing annotating the most informative samples. This paper proposes an active learning approach for multidialectal Arabic POS tagging. Our experiments revealed that annotating approximately 15,000 tokens is sufficient for high performance. We further demonstrate that using a fine-tuned model from one dialect to guide the selection of initial samples from another dialect accelerates convergence—reducing the annotation requirement by about 2,000 tokens. In conclusion, we propose an active learning pipeline and demonstrate that, upon reaching its defined stopping point of 16,000 annotated tokens, it achieves an accuracy of 97.6% on the Emirati Corpus.

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Embedding-Free RAG
Jessica Maghakian | Raunak Sinha | Max Schettewi | Gunkirat Kaur

Retrieval-Augmented Generation (RAG) is the current state-of-the-art method for mitigating the shortcomings of large language models (LLMs) by incorporating external knowledge sources to provide more relevant and accurate responses to user queries. However building performant RAG systems for real use-cases typically requires heavy investment from NLP experts, such as fine-tuning embedding models for specialized domains, experimenting with text chunking strategies and other niche hyperparameter tunings. We propose Embedding-Free RAG, a model-agnostic approach that enables the deployment of a one-size-fits-all RAG pipeline for user-provided grounding documents. Unlike traditional RAG, which relies on embedding models for information retrieval, Embedding-Free RAG leverages the generalized reasoning abilities of LLMs in a novel algorithmic framework during the retrieval stage. Extensive experiments demonstrate that Embedding-Free RAG outperforms existing state-of-the-art methods, achieving up to 4.6x higher F1 scores and up to 2x better question answering accuracy across a wide range of challenging domains.

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Rating Roulette: Self-Inconsistency in LLM-As-A-Judge Frameworks
Rajarshi Haldar | Julia Hockenmaier

As Natural Language Generation (NLG) continues to be widely adopted, properly assessing it has become quite difficult. Lately, using large language models (LLMs) for evaluating these generations has gained traction, as they tend to align more closely with human preferences than conventional n-gram or embedding-based metrics. In our experiments, we show that LLM judges have low intra-rater reliability in their assigned scores across different runs. This variance makes their ratings inconsistent, almost arbitrary in the worst case, making it difficult to measure how good their judgments actually are. We quantify this inconsistency across different NLG tasks and benchmarks and see if judicious use of LLM judges can still be useful following proper guidelines.

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Quantifying Uncertainty in Natural Language Explanations of Large Language Models for Question Answering
Yangyi Li | Mengdi Huai

Large language models (LLMs) have shown strong capabilities, enabling concise, context-aware answers in question answering (QA) tasks. The lack of transparency in complex LLMs has inspired extensive research aimed at developing methods to explain large language behaviors. Among existing explanation methods, natural language explanations stand out due to their ability to explain LLMs in a self-explanatory manner and enable the understanding of model behaviors even when the models are closed-source. However, despite these promising advancements, there is no existing work studying how to provide valid uncertainty guarantees for these generated natural language explanations. Such uncertainty quantification is critical in understanding the confidence behind these explanations. Notably, generating valid uncertainty estimates for natural language explanations is particularly challenging due to the auto-regressive generation process of LLMs and the presence of noise in medical inquiries. To bridge this gap, in this work, we first propose a novel uncertainty estimation framework for these generated natural language explanations, which provides valid uncertainty guarantees in a post-hoc and model-agnostic manner. Additionally, we also design a novel robust uncertainty estimation method that maintains valid uncertainty guarantees even under noise. Extensive experiments on QA tasks demonstrate the desired performance of our methods.

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Real-World Summarization: When Evaluation Reaches Its Limits
Patrícia Schmidtová | Ondrej Dusek | Saad Mahamood

We examine evaluation of faithfulness to input data in the context of hotel highlights—brief LLM-generated summaries that capture unique features of accommodations. Through human evaluation campaigns involving categorical error assessment and span-level annotation, we compare traditional metrics, trainable methods, and LLM-as-a-judge approaches. Our findings reveal that simpler metrics like word overlap correlate surprisingly well with human judgments (r=0.63), often outperforming more complex methods when applied to out-of-domain data. We further demonstrate that while LLMs can generate high-quality highlights, they prove unreliable for evaluation as they tend to severely under- or over-annotate. Our analysis of real-world business impacts shows incorrect and non-checkable information pose the greatest risks. We also highlight challenges in crowdsourced evaluations.

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Open-DeBias: Toward Mitigating Open-Set Bias in Language Models
Arti Rani | Shweta Singh | Nihar Ranjan Sahoo | Gaurav Kumar Nayak

Large Language Models (LLMs) have achieved remarkable success on question answering (QA) tasks, yet they often encode harmful biases that compromise fairness and trustworthiness. Most existing bias mitigation approaches are restricted to predefined categories, limiting their ability to address novel or context-specific emergent biases. To bridge this gap, we tackle the novel problem of open-set bias detection and mitigation in text-based QA. We introduce _OpenBiasBench_, a comprehensive benchmark designed to evaluate biases across a wide range of categories and subgroups, encompassing both known and previously unseen biases. Additionally, we propose _Open-DeBias_, a novel, data-efficient, and parameter-efficient debiasing method that leverages adapter modules to mitigate existing social and stereotypical biases while generalizing to unseen ones. Compared to the state-of-the-art BMBI method, Open-DeBias improves QA accuracy on BBQ dataset by nearly **48%** on ambiguous subsets and **6%** on disambiguated ones, using adapters fine-tuned on just a small fraction of the training data. Remarkably, the same adapters, in a zero-shot transfer to Korean BBQ, achieve **84% accuracy**, demonstrating robust language-agnostic generalization. Through extensive evaluation, we also validate the effectiveness of Open-DeBias across a broad range of NLP tasks, including StereoSet and CrowS-Pairs, highlighting its robustness, multilingual strength, and suitability for general-purpose, open-domain bias mitigation. The project page is available at: [https://sites.google.com/view/open-debias25](https://sites.google.com/view/open-debias25)

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SACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Dhruv Gupta | Gayathri Ganesh Lakshmy | Yiqing Xie

In this work, we conduct an in-depth analysis of code retrieval by systematically masking specific features while preserving code functionality. Our discoveries include: (1) although trained on code, current retrievers heavily rely on surface-level textual features (e.g., docstrings, identifier names), and (2) they exhibit a strong bias towards well-documented code, even if the documentation is irrelevant. Based on our discoveries, we propose SACL, a framework that enriches textual information and reduces bias by augmenting code or structural knowledge with semantic information. Extensive experiments show that SACL substantially improves code retrieval (e.g., by 12.8% / 9.4% / 7.0% Recall@1 on HumanEval / MBPP / SWE-Bench-Lite), which also leads to better code generation performance (e.g., by 4.88% Pass@1 on HumanEval).

Large language models (LLMs) are rapidly deployed in critical applications, raising urgent needs for robust safety benchmarking. We propose Jailbreak Distillation (JBDistill), a novel benchmark construction framework that “distills” jailbreak attacks into high-quality and easily-updatable safety benchmarks. JBDistill utilizes a small set of development models and existing jailbreak attack algorithms to create a candidate prompt pool, then employs prompt selection algorithms to identify an effective subset of prompts as safety benchmarks. JBDistill addresses challenges in existing safety evaluation: the use of consistent evaluation prompts across models ensures fair comparisons and reproducibility. It requires minimal human effort to rerun the JBDistill pipeline and produce updated benchmarks, alleviating concerns on saturation and contamination. Extensive experiments demonstrate our benchmarks generalize robustly to 13 diverse evaluation models held out from benchmark construction, including proprietary, specialized, and newer-generation LLMs, significantly outperforming existing safety benchmarks in effectiveness while maintaining high separability and diversity. Our framework thus provides an effective, sustainable, and adaptable solution for streamlining safety evaluation.

Large Language Models (LLMs) have demonstrated exceptional capabilities, yet selecting the most reliable response from multiple LLMs remains a challenge, particularly in resource-constrained settings. Existing approaches often depend on costly external verifiers, human evaluators, or self-consistency techniques that require multiple samples from a single model. While multi-LLM systems produce more diverse responses than single models and thus have greater potential, they often underperform compared to single LLM self-consistency. In this work, we propose a calibrated log-likelihood-based selection framework to improve multi-LLM performance. Our approach leverages uncertainty estimation to identify the most confident response while minimizing inference costs. We show that our method outperforms majority voting and exceeds self-consistency performance when using a large number of model calls. Through extensive experiments, we demonstrate improvements of approx. 4%, 3%, and 5% on GSM8K, MMLU, and ARC, respectively, when applying uncertainty-aware selection to multi-LLM systems.

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GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations
Odysseas S. Chlapanis | Dimitris Galanis | Nikolaos Aletras | Ion Androutsopoulos

We introduce GreekBarBench, a benchmark that evaluates LLMs on legal questions across five different legal areas from the Greek Bar exams, requiring citations to statutory articles and case facts. To tackle the challenges of free-text evaluation, we propose a three-dimensional scoring system combined with an LLM-as-a-judge approach. We also develop a meta-evaluation benchmark to assess the correlation between LLM-judges and human expert evaluations, revealing that simple, span-based rubrics improve their alignment. Our extensive evaluation of 13 proprietary and open-weight LLMs shows that even though the top models exhibit impressive performance, they remain susceptible to critical errors, most notably a failure to identify the correct statutory articles.

Text-to-SQL transforms the user queries from natural language to executable SQL programs, enabling non-experts to interact with complex databases. Existing prompt-based methods craft meticulous text guidelines and examples to facilitate SQL generation, but their accuracy is hindered by the large semantic gap between the texts and the low-resource SQL programs. In this work, we propose Pi-SQL, which incorporates the high-resource Python program as a pivot to bridge between the natural language query and SQL program. In particular, Pi-SQL first generates Python programs that provide fine-grained step-by-step guidelines in their code blocks or comments, and then produces an SQL program following the guidance of each Python program. The final SQL program matches the reference Python program’s query results and, through selection from candidates generated by different strategies, achieves superior execution speed, with a reward-based valid efficiency score up to 4.55 higher than the best-performing baseline. Extensive experiments demonstrate the effectiveness of Pi-SQL, which improves the execution accuracy of the best-performing baseline by up to 3.20.

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RAC: Efficient LLM Factuality Correction with Retrieval Augmentation
Changmao Li | Jeffrey Flanigan

Large Language Models (LLMs) exhibit impressive results across a wide range of natural language processing (NLP) tasks, yet they can often produce factually incorrect outputs. This paper introduces a simple but effective low-latency post-correction method, Retrieval Augmented Correction (RAC), aimed at enhancing the factual performance of LLMs without requiring additional fine-tuning. Our method is general and can be used with any instruction-tuned LLM, and has greatly reduced latency compared to prior approaches. RAC decomposes the LLM’s output into atomic facts and applies a fine-grained verification and correction process with retrieved content to verify and correct the LLM-generated output. Our extensive experiments show that RAC yields up to 30% improvements over the LLM baselines across three popular factuality evaluation datasets, validating its efficacy and robustness with and without the integration of Retrieval-Augmented Generation (RAG) across different LLMs. Notably, our method has reduced latency up to 40x and reduced token consumption up to 7x compared to previous state-of-the-art post-correction approaches with similar or better performance.

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Does It Run and Is That Enough? Revisiting Text-to-Chart Generation with a Multi-Agent Approach
James Ford | Anthony Rios

Large language models can translate natural-language chart descriptions into runnable code, yet approximately 15% of the generated scripts still fail to execute, even after supervised fine-tuning and reinforcement learning. We investigate whether this persistent error rate stems from model limitations or from reliance on a single-prompt design. To explore this, we propose a lightweight multi-agent pipeline that separates drafting, execution, repair, and judgment, using only an off-the-shelf GPT-4o-mini model. On the Text2Chart31 benchmark, our system reduces execution errors to 4.5% within three repair iterations, outperforming the strongest fine-tuned baseline by nearly 5 percentage points while requiring significantly less compute. Similar performance is observed on the ChartX benchmark, with an error rate of 4.6%, demonstrating strong generalization. Under current benchmarks, execution success appears largely solved. However, manual review reveals that 6 out of 100 sampled charts contain hallucinations, and an LLM-based accessibility audit shows that only 33.3% (Text2Chart31) and 7.2% (ChartX) of generated charts satisfy basic colorblindness guidelines. These findings suggest that future work should shift focus from execution reliability toward improving chart aesthetics, semantic fidelity, and accessibility.

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GeLoRA: Geometric Adaptive Ranks For Efficient LoRA Fine-tuning
Abdessalam Ed-dib | Zhanibek Datbayev | Amine M. Aboussalah

Fine-tuning large language models (LLMs) is computationally expensive because it requires updating all model parameters. Low-Rank Adaptation (LoRA) reduces this cost by modifying a subset of weights, but selecting the appropriate rank introduces a trade-off: lower ranks improve efficiency at the expense of expressivity, while higher ranks enhance performance but increase computational burden. Existing adaptive LoRA methods lack a theoretical foundation to guide this trade-off optimally. We propose Geometric Low-Rank Adaptation (GeLoRA), a principled approach that estimates the intrinsic dimensionality of hidden data representations to adaptively select LoRA ranks. We show theoretically and empirically that the intrinsic dimension serves as a lower bound for the optimal rank of LoRA matrices, enabling a balance between efficiency and expressivity. Extensive experiments on GLUE, SQuAD (with DeBERTa), and MT-Bench (with LLaMA) demonstrate that GeLoRA consistently outperforms recent adaptive LoRA methods by up to +1.0%, while simultaneously reducing computational time by 13.5% to 64.2%, depending on the baseline, under the same parameter budget.

Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can also efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness.

Large Language Models (LLMs) inevitably acquire harmful information during training on massive datasets. LLM unlearning aims to eliminate the influence of such harmful information while maintaining the model’s overall performance. Existing unlearning methods, represented by gradient ascent-based approaches, primarily focus on forgetting target data while overlooking the crucial impact of logically related knowledge on the effectiveness of unlearning. In this paper, through both theoretical and experimental analyses, we first demonstrate that a key reason for the suboptimal unlearning performance is that models can reconstruct the target content through reasoning with logically related knowledge. To address this issue, we propose Unlearning Improvement via Parameter Extrapolation (UIPE), a method that removes knowledge highly correlated with the forgetting targets. Experimental results show that UIPE significantly enhances the performance of GA-based method and its variants on the TOFU and WMDP benchmarks.

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FicSim: A Dataset for Multi-Faceted Semantic Similarity in Long-Form Fiction
Natasha Johnson | Amanda Bertsch | Maria-Emil Deal | Emma Strubell

As language models become capable of processing increasingly long and complex texts, there has been growing interest in their application within computational literary studies. However, evaluating the usefulness of these models for such tasks remains challenging due to the cost of fine-grained annotation for long-form texts and the data contamination concerns inherent in using public-domain literature. Current embedding similarity datasets are not suitable for evaluating literary-domain tasks because of a focus on coarse-grained similarity and primarily on very short text. We assemble and release a dataset, FicSim, of long-form, recently written fiction, including scores along 12 axes of similarity informed by author-produced metadata and validated by digital humanities scholars. We evaluate a suite of embedding models on this task, demonstrating a tendency across models to focus on surface-level features over semantic categories that would be useful for computational literary studies tasks. Throughout our data-collection process, we prioritize author agency and rely on continual, informed author consent.

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Masked Diffusion Captioning for Visual Feature Learning
Chao Feng | Zihao Wei | Andrew Owens

We learn visual features by captioning images with an image-conditioned masked diffusion language model, a formulation we call masked diffusion captioning (MDC). During training, text tokens in each image–caption pair are masked at a randomly chosen ratio, and a decoder conditioned on visual features is trained to reconstruct the original text. After training, the learned visual features can be applied to downstream vision tasks. Unlike autoregressive captioning, the strength of the visual learning signal in MDC does not depend on each token’s position in the sequence, reducing the need for auxiliary objectives. Linear probing experiments across a variety of academic-scale models and datasets show that the learned visual features are competitive with those produced by autoregressive and contrastive approaches.

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Diverse Multi-tool Aggregation with Large Language Models for Enhanced Math Reasoning
Bohan Yao | Vikas Yadav

Tool usage is a proven technique for developing high-performance reasoning in large language models (LLMs). Our work is focused on emphasizing the utility of leveraging multiple diverse tools for complex reasoning tasks. We present Multi-TAG, a Multi-Tool AGgregation-based LLM framework that utilizes multiple diverse tools to solve complex math problems over multiple reasoning steps. At each reasoning step, Multi-TAG invokes multiple tools and accepts the solution of the respective step by tools that have majority agreement on the final answer estimate. Multi-TAG strongly outperforms several standard baselines that use individual tools with the same number of runs, highlighting the importance of multi-tool invocation for solving complex reasoning tasks. We also show that naive aggregation of multiple tools at each reasoning step also leads to substantial improvements of up to 35% accuracy. Multi-TAG then further improves these gains by 7.4% on average on MATH500, AIME, AMC, and OlympiadBench.

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Enhancing Goal-oriented Proactive Dialogue Systems via Dynamic Multi-dimensional Consistency Optimization
Didi Zhang | Yaxin Fan | Peifeng Li | Qiaoming Zhu

Previous work on goal-oriented proactive dialogue systems frequently failed to address the multi-dimensional consistency issue between generated responses and key contextual elements (e.g., user profile, dialogue history, domain knowledge, and subgoal). To address this issue, we propose a novel Dynamic Multi-dimensional Consistency Reinforcement Learning (DMCRL) framework, which adaptively measures the impact of each consistency dimension on overall dialogue quality and provides targeted feedback to improve response quality. Experimental results on two datasets demonstrate that our DMCRL significantly improves the consistency of generated responses.

Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation. However, their general-purpose nature often limits their effectiveness in domain-specific applications that require specialized knowledge, such as healthcare, chemistry, or legal analysis. To address this, researchers have explored diverse methods to enhance LLMs by integrating domain-specific knowledge. In this survey, we provide a comprehensive overview of these methods, which we categorize into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. Each approach offers unique mechanisms to equip LLMs with domain expertise, balancing trade-offs between flexibility, scalability, and efficiency. We discuss how these methods enable LLMs to tackle specialized tasks, compare their advantages and disadvantages, evaluate domain-specific LLMs against general LLMs, and highlight the challenges and opportunities in this emerging field. For those interested in delving deeper into this area, we also summarize the commonly used datasets and benchmarks. To keep researchers updated on the latest studies, we maintain an open-source at: blueofficial-repo.com, dedicated to documenting research in the field of specialized LLM.

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Who’s the Author? How Explanations Impact User Reliance in AI-Assisted Authorship Attribution
Calvin Bao | Connor Baumler | Hal Daumé Iii | Marine Carpuat

Despite growing interest in explainable NLP, it remains unclear how explanation strategies shape user behavior in tasks like authorship identification, where relevant textual features may be difficult for lay users to pinpoint. To support their analysis of text style, we consider two explanation types: example-based style rewrites and feature-based rationales, generated using a LLM-based pipeline. We measured how explanations impact user behavior in a controlled study (n=95) where participants completed authorship identification tasks with our types of assistance. While no explanation type improved overall task accuracy, fine-grained reliance patterns (CITATION) revealed that rewrites supported appropriate reliance, whereas presenting both explanation types increased AI overreliance, minimizing participant self-reliance. We find that participants exhibiting better reliance behaviors had focused explanation needs, contrasting with the diffused preferences of those who overrelied on AI, or incorrectly self-relied. These findings highlight the need for adaptive explanation systems that tailor support based on specific user reliance behaviors.

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UniSpeaker: A Unified Approach for Multimodality-driven Speaker Generation
Zhengyan Sheng | Zhihao Du | Heng Lu | ShiLiang Zhang | Zhen-Hua Ling

While recent advances in reference-based speaker cloning have significantly improved the authenticity of synthetic speech, speaker generation driven by multimodal cues such as visual appearance, textual descriptions, and other biometric signals remains in its early stages. To pioneer truly multimodal-controllable speaker generation, we propose UniSpeaker, the first framework supporting unified voice synthesis from arbitrary modality combinations. Specifically, self-distillation is firstly applied to a large-scale speech generation model for speaker disentanglement. To overcome data sparsity and one-to-many mapping challenges, a novel KV-Former based unified voice aggregator is introduced, where multiple modalities are projected into a shared latent space through soft contrastive learning to ensure accurate alignment with user-specified vocal characteristics. Additionally, to advance the field, the first Multimodal Voice Control (MVC) benchmark is established to evaluate voice suitability, diversity, and quality. When tested across five MVC tasks, UniSpeaker is shown to surpass existing modality-specific models. Speech samples and the MVC benchmark are available at https://UniSpeaker.github.io.

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On the Fine-Grained Planning Abilities of VLM Web Agents
Surgan Jandial | Yinong Oliver Wang | Andrea Bajcsy | Fernando De la Torre

Vision-Language Models (VLMs) have shown promise as web agents, yet their planning—the ability to devise strategies or action sequences to complete tasks—remains understudied. While prior works focus on VLM’s perception and overall success rates (i.e., goal completion), fine-grained investigation of their planning has been overlooked. To address this gap, we examine VLMs’ capability to (1) understand temporal relationships within web contexts, and (2) assess plans of actions across diverse scenarios. We design four simple yet effective tests to delve into these nuanced aspects around planning. Our results across nineteen VLMs reveal that these models exhibit limited performance in the aforementioned skills and are not reliable to function as web agents. To facilitate future work, we release our planning evaluations and data, providing a foundation for advancing the future research in this area.

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InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models with Human Feedback
Henry Hengyuan Zhao | Wenqi Pei | Yifei Tao | Haiyang Mei | Mike Zheng Shou

Existing benchmarks do not test Large Multimodal Models (LMMs) on their interactive intelligence with human users which is vital for developing general-purpose AI assistants. We design InterFeedback, an interactive framework, which can be applied to any LMM and dataset to assess this ability autonomously. On top of this, we introduce InterFeedback-Bench that evaluates interactive intelligence using two representative datasets, MMMU-Pro and MathVerse, to test 10 different open-source LMMs. Additionally, we present InterFeedback-Human, a newly collected dataset of 120 cases designed for manually testing interactive performance in leading models such as OpenAI-o1 and Claude-3.5-Sonnet. Our evaluation results show that state-of-the-art LMM (e.g., OpenAI-o1) can correct their results through human feedback less than 50%. Our findings point to the need for methods that can enhance LMMs’ capabilities to interpret and benefit from feedback.

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ReFLAIR: Enhancing Multimodal Reasoning via Structured Reflection and Reward-Guided Learning
Jiazhou Ji | Xinru Lu

Large models can achieve higher performance on complex problems through iterative self-reflection. Yet when reflection is uncontrolled, it often leads to longer outputs, higher inference cost, and an increased risk of hallucination. Existing training methods rarely address this trade off. We introduce ReFLAIR, a unified framework that teaches multimodal large models to perform structured reflection via an explicit $think re-think answer $ format and hybrid reward learning. ReFLAIR begins with supervised cold start training on the ReFLAIR-cold dataset of curated multimodal reasoning trajectories, and then trains a Reflection Quality Scorer (RQS) to quantify the utility of rethinking steps. A modified Group Relative Policy Optimization algorithm optimizes a hybrid reward that combines answer correctness, structural fidelity, reflection utility, and sample difficulty. Evaluated on challenging mathematical benchmarks including MathVista, MathVerse, MM-Math and GSM8K, ReFLAIR yields improvements up to +12.2% absolute accuracy, produces higher quality reflective traces, and reduces harmful or redundant revisions. An adaptive test time reflection scheduler further reduces inference cost by nearly 23% while maintaining or improving accuracy. These results demonstrate that structured, reward guided reflection offers a scalable pathway to more reliable and interpretable reasoning in multimodal models.

This work demonstrates that diffusion models can achieve font-controllable multilingual text rendering using just raw images without font label annotations. Visual text rendering remains a significant challenge. While recent methods condition diffusion on glyphs, it is impossible to retrieve exact font annotations from large-scale, real-world datasets, which prevents user-specified font control. To address this, we propose a data-driven solution that integrates the conditional diffusion model with a text segmentation model, utilizing segmentation masks to capture and represent fonts in pixel space in a self-supervised manner, thereby eliminating the need for any ground-truth labels and enabling users to customize text rendering with any multilingual font of their choice. The experiment provides a proof of concept of our algorithm in zero-shot text and font editing across diverse fonts and languages, providing valuable insights for the community and industry toward achieving generalized visual text rendering.

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STA-CoT: Structured Target-Centric Agentic Chain-of-Thought for Consistent Multi-Image Geological Reasoning
Beibei Yu | Tao Shen | Ling Chen

Reliable multi-image geological reasoning is essential for automating expert tasks in remote-sensing mineral exploration, yet remains challenging for multimodal large language models (MLLMs) due to the need for locating target areas, accurate cross-image referencing, and consistency over long reasoning chains. We propose STA-CoT, a Structured Target-centric Agentic Chain-of-Thought framework that orchestrates planning, execution, and verification agents to decompose, ground, and iteratively refine reasoning steps over geological and hyperspectral image sets. By aligning each reasoning step to specific image target areas and enforcing consistency through agentic verification and majority voting, STA-CoT robustly mitigates tool errors, long-chain inconsistencies, and error propagation. We rigorously evaluate STA-CoT on MineBench, a dedicated benchmark for multi-image mineral exploration, demonstrating substantial improvements over existing multimodal chain-of-thought and agentic baselines. Our results establish STA-CoT as a reliable and robust solution for consistent multi-image geological reasoning, advancing automated scientific discovery in mineral exploration.

Despite of significant achievements in improving instruction-following capabilities of large language models (LLMs), the ability to process multiple potentially entangled or conflict instructions remains a considerable challenge. Real-world scenarios often require the consistency across multiple instructions over time, such as secret privacy, presonal preferences, and prioritization, so we demand sophisticated abilities to integrate multiple turns and carefully balance competing objectives when instructions intersect or conflict. This work presents a systematic investigation of LLMs’ capabilities in handling multiple turns of instructions, covering three levels of difficulty: (1) retrieving information from instructions, (2) tracking and reasoning across turns, and (3) resolving conflicts among instructions. We construct MultiTurnInstruct with 1.1K high-quality multi-turn conversations through the human-in-the-loop approach and result in a total of nine capability categories, including statics and dynamics, reasoning and multitasking. Our finding reveals an intriguing trade-off between different capabilities. While GPT models demonstrate superior memorization, they show reduced effectiveness in privacy-protection tasks requiring selective information withholding. Larger models exhibit stronger reasoning capabilities but still struggle with resolving conflicting instructions. Importantly, these performance gaps cannot be attributed solely to information loss, as models demonstrate strong BLEU scores on memorization tasks but their attention mechanisms fail to effectively integrate multiple related instructions. These findings highlight critical areas for improvement in the complex real-world tasks involving multi-turn instructions.

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How to Generalize the Detection of AI-Generated Text: Confounding Neurons
Claudio Borile | Carlo Abrate

Detectors of LLM-generated text suffer from poor domain shifts generalization ability. Yet, reliable text detection methods in the wild are of paramount importance for plagiarism detection, integrity of the public discourse, and AI safety. Linguistic and domain confounders introduce spurious correlations, leading to poor out-of-distribution (OOD) performance. In this work we introduce the concept of confounding neurons, individual neurons within transformers-based detectors that encode dataset-specific biases rather than task-specific signals. Leveraging confounding neurons, we propose a novel post-hoc, neuron-level intervention framework to disentangle AI-generated text detection factors from data-specific biases. Through extensive experiments we prove its ability to effectively reduce topic-specific biases, enhancing the model’s ability to generalize across domains.

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SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks
Fenia Christopoulou | Ronald Cardenas | Gerasimos Lampouras | Haitham Bou Ammar | Jun Wang

Direct alignment algorithms have proven an effective step for aligning language models to human-desired behaviors. Current variants of the Direct Preference Optimization objective have focused on a strict setting where all tokens are contributing signals of KL divergence and rewards to the loss function.However, human preference is not affected equally by each word in a sequence but is often dependent on specific words or phrases, e.g. existence of toxic terms leads to non-preferred responses. Based on this observation, we argue that not all tokens should be weighted equally during PO and propose a flexible objective termed SparsePO, that aims to automatically learn to weight the KL divergence and reward corresponding to each token during PO training. We propose two different variants of weight-masks that can either be derived from the reference model itself or learned on the fly. Notably, our method induces sparsity in the learned masks, allowing the model to learn how to best balance reward and KL divergence contributions at the token level, learning an optimal level of mask sparsity.Extensive experiments illustrate the effectiveness of our approach at aligning to preference proxies, including sentiment control, helpfulness and harmless, and summary quality.Our method obtains +10% and +3% win-rate points in summarization and dialogue scenarios, respectively,without compromising the reasoning capabilities of the model, or the relevancy and faithfulness of the summary response.

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We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism
Priyanshu Priya | Saurav Dudhate | Desai Vishesh Yasheshbhai | Asif Ekbal

Integrating argumentation mechanisms into negotiation dialogue systems improves conflict resolution through exchanges of arguments and critiques. Moreover, incorporating personality attributes enhances adaptability by aligning interactions with individuals’ preferences and styles. To advance these capabilities in negotiation dialogue systems, we propose a novel Personality-driven Argumentation-based Negotiation Dialogue Generation (PAN-DG) task. To support this task, we introduce PACT, a dataset of Personality-driven Argumentation-based negotiation Conversations for Tourism sector. This dataset, generated using Large Language Models (LLMs), features three distinct personality profiles, viz. Argumentation Profile, Preference Profile, and Buying Style Profile to simulate a variety of negotiation scenarios involving diverse personalities. Thorough automatic and manual assessments indicate high-quality dialogues in the dataset. Further, we conduct comparative experiments between pre-trained and fine-tuned LLMs for the PAN-DG task. Multi-dimensional evaluation demonstrates that the fine-tuned LLMs effectively generate personality-driven rational responses during negotiations. This underscores effectiveness of PACT in enhancing personalization and reasoning capabilities in negotiation dialogue systems, thereby establishing a foundation for future research in this domain.

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Towards the Roots of the Negation Problem: A Multilingual NLI Dataset and Model Scaling Analysis
Tereza Vrabcová | Marek Kadlčík | Petr Sojka | Michal Štefánik | Michal Spiegel

Negations are key to determining sentence meaning, making them essential for logical reasoning. Despite their importance, negations pose a substantial challenge for large language models (LLMs) and remain underexplored.We constructed and published two new textual entailment datasets NoFEVER-ML and NoSNLI-ML in four languages (English, Czech, German, and Ukrainian) with paired examples differing in negation. It allows investigation of the root causes of the negation problem and its exemplification: how popular LLM model properties and language impact their inability to handle negation correctly.Contrary to previous work, we show that increasing the model size may improve the models’ ability to handle negations. Furthermore, we find that both the models’ reasoning accuracy and robustness to negation are language-dependent and that the length and explicitness of the premise have an impact on robustness. We observe higher accuracy in languages with relatively fixed word order like English, compared to those with greater flexibility like Czech and German.Our entailment datasets pave the way to further research for explanation and exemplification of the negation problem, minimization of LLM hallucinations, and improvement of LLM reasoning in multilingual settings.

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Improving the Language Understanding Capabilities of Large Language Models Using Reinforcement Learning
Sai Ashish Somayajula | Bokai Hu | Qi Cao | Xin Pan | Pengtao Xie

Instruction-fine-tuned large language models (LLMs) under 14B parameters continue to underperform on natural language understanding (NLU) tasks, often trailing smaller models like BERT-base on benchmarks such as GLUE and SuperGLUE. Motivated by the success of reinforcement learning in reasoning tasks (e.g., DeepSeek), we explore Proximal Policy Optimization (PPO) as a framework to improve the NLU capabilities of LLMs. We frame NLU as a reinforcement learning environment, treating token generation as a sequence of actions and optimizing for reward signals based on alignment with ground-truth labels. PPO consistently outperforms supervised fine-tuning, yielding an average improvement of 6.3 points on GLUE, and surpasses zero-shot and few-shot prompting by 38.7 and 26.1 points, respectively. Notably, PPO-tuned models outperform GPT-4o by over 4% on average across sentiment and natural language inference tasks, including gains of 7.3% on the Mental Health dataset and 10.9% on SIGA-nli. This work highlights a promising direction for adapting LLMs to new tasks by reframing them as reinforcement learning problems, enabling learning through simple end-task rewards rather than extensive data curation. Our code is available at https://github.com/coder-qicao/RL4GLUE.

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HATECAT-TR: A Hate Speech Span Detection and Categorization Dataset for Turkish
Hasan Kerem Şeker | Gökçe Uludoğan | Pelin Önal | Arzucan Özgür

Hate speech on social media in Turkey remains a critical issue, frequently targeting minority groups. Effective moderation requires not only detecting hateful posts but also identifying the specific hateful expressions within them. To address this, we introduce HATECAT-TR, a span-annotated dataset of Turkish tweets, containing 4465 hateful spans across 2981 posts, each directed at one of eight minority groups. Annotations were created using a semi-automated approach, combining GPT-4o-generated spans with human expert review to ensure accuracy. Each hateful span is categorized into one of five discourse types, enabling a fine-grained analysis of the nature and intent behind hateful content. We frame span detection as binary and multi-class token classification tasks and utilize the state-of-the-art language models to establish a baseline performance for the new dataset. Our findings highlight the challenges of detecting and categorizing implicit hate speech, particularly when spans are subtle and highly contextual. The source code is available at github.com/boun-tabi/hatecat-tr and HATECAT-TR can be shared by complying with the terms of X upon contacting the authors.

Recent advancements in speech-language models have yielded significant improvements in speech tokenization and synthesis. However, effectively mapping the complex, multidimensional attributes of speech into discrete tokens remains challenging. This process demands acoustic, semantic, and contextual information for precise speech representations. Existing speech representations generally fall into two categories: acoustic tokens from audio codecs and semantic tokens from speech self-supervised learning models. Although recent efforts have unified acoustic and semantic tokens for improved performance, they overlook the crucial role of contextual representation in comprehensive speech modeling. Our empirical investigations reveal that the absence of contextual representations results in elevated Word Error Rate (WER) and Word Information Lost (WIL) scores in speech transcriptions. To address these limitations, we propose two novel distillation approaches: (1) a language model (LM)-guided distillation method that incorporates contextual information, and (2) a combined LM and self-supervised speech model (SM)-guided distillation technique that effectively distills multimodal representations (acoustic, semantic, and contextual) into a comprehensive speech tokenizer, termed DM-Codec. The DM-Codec architecture adopts a streamlined encoder-decoder framework with a Residual Vector Quantizer (RVQ) and incorporates the LM and SM during the training process. Experiments show DM-Codec significantly outperforms state-of-the-art speech tokenization models, reducing WER by up to 13.46%, WIL by 9.82%, and improving speech quality by 5.84% and intelligibility by 1.85% on the LibriSpeech benchmark dataset.

Multi-intent utterances processing remains a persistent challenge due to intricate intent-slot dependencies and semantic ambiguities. Traditional methods struggle to model these complex interactions, particularly when handling overlapping slot structures across multiple intents. This paper introduces a label-aware contrastive attention network (LCAN), a joint modeling approach for multi-intent recognition and slot filling in task-oriented dialogue systems. LCAN addresses this issue by integrating label-aware attention and contrastive learning strategies, improving semantic understanding and generalization in multi-intent scenarios. Extensive experiments on the MixATIS and MixSNIPS datasets demonstrate LCAN’s superiority over existing models, achieving improved intent recognition and slot filling performance, particularly in handling overlapping or complex semantic structures in multi-intent settings.

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Low-Resource Languages LLM Disinformation is Within Reach: The Case of Walliserdeutsch
Andrei Kucharavy | Sherine Seppey | Cyril Vallez | Dimitri Percia David | Ljiljana Dolamic

LLM-augmented online disinformation is of particular concern for low-resource languages, given their prior limited exposure to it. While current LLMs lack fluidity in such languages, their multilingual and emerging capabilities can potentially still be leveraged.In this paper, we investigate whether a moderately sophisticated attacker can leverage such capabilities and perform an impersonation attack in the Walliserdeutsch dialect, a low-resource (100k speakers) Swiss German Highest Allemanic dialect that is generally non-intelligible to both Standard German and other Swiss German dialects speakers and presents considerable within-dialect variability.We show that while a standard few-shot learning prompting of SotA LLMs, even by native Walliserdeutsch speakers, yields easily human-detectable texts, an expert attacker performing a PEFT on a small SotA LLM is partially able to perform such an impersonation with minimal resources, even if the fine-tuned LLM does not advertise any capabilities in Germanic languages. With Walliserdeutsch presenting many features of low-resource languages and dialects, our results suggest that LLM-augmented disinformation is within reach for low-resource languages, highlighting the urgency of LLM detectability research in low-resource languages.

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Exploring and Controlling Diversity in LLM-Agent Conversation
KuanChao Chu | Yi-Pei Chen | Hideki Nakayama

Controlling diversity in LLM-agent simulations is essential for balancing stability in structured tasks with variability in open-ended interactions. However, we observe that dialogue diversity tends to degrade over long-term simulations. To explore the role of prompt design in this phenomenon, we modularized the utterance generation prompt and found that reducing contextual information leads to more diverse outputs. Based on this insight, we propose Adaptive Prompt Pruning (APP), a novel method that allows users to control diversity via a single parameter, λ. APP dynamically prunes prompt segments based on attention scores and is compatible with existing diversity control methods. We demonstrate that APP effectively modulates diversity through extensive experiments and propose a method to balance the control trade-offs. Our analysis reveals that all prompt components impose constraints on diversity, with the Memory being the most influential. Additionally, high-attention contents consistently suppress output diversity.

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Agentic-ToM: Cognition-Inspired Agentic Processing For Enhancing Theory of Mind Reasoning
Sneheel Sarangi | Chetan Talele | Hanan Salam

The capacity to attribute mental states like beliefs, desires, and intentions to oneself and others, known as Theory of Mind (ToM), is fundamental to human social intelligence. As Large Language Models (LLMs) are increasingly integrated into complex interactive systems, developing their ToM capabilities is crucial. Such capabilities enable LLMs to understand and predict human behavior, leading to more intuitive and productive interactions. However, current models often struggle with sophisticated reasoning about others’ perspectives. In this work, we propose “Agentic-ToM”, showing that guiding LLMs by embedding psychologically-grounded functions for capabilities such as ‘perspective taking’ and mental state tracking markedly improves their proficiency in ToM tasks. We evaluate the approach on three diverse ToM datasets and show that this method significantly outperforms baselines across all tasks without requiring task-specific modifications.

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Can We Edit LLMs for Long-Tail Biomedical Knowledge?
Xinhao Yi | Jake Lever | Kevin Bryson | Zaiqiao Meng

Knowledge editing has emerged as an effective approach for updating large language models (LLMs) by modifying their internal knowledge. However, their application to the biomedical domain faces unique challenges due to the long-tailed distribution of biomedical knowledge, where rare and infrequent information is prevalent. In this paper, we conduct the first comprehensive study to investigate the effectiveness of knowledge editing methods for editing long-tail biomedical knowledge. Our results indicate that, while existing editing methods can enhance LLMs’ performance on long-tail biomedical knowledge, their performance on long-tail knowledge remains inferior to that on high-frequency popular knowledge, even after editing. Our further analysis reveals that long-tail biomedical knowledge contains a significant amount of one-to-many knowledge, where one subject and relation link to multiple objects. This high prevalence of one-to-many knowledge limits the effectiveness of knowledge editing in improving LLMs’ understanding of long-tail biomedical knowledge, highlighting the need for tailored strategies to bridge this performance gap.

Recent advancements in reinforcement learning (RL) have enhanced the reasoning abilities of large language models (LLMs), yet the impact on multimodal LLMs (MLLMs) is limited. Particularly in vision-intensive tasks like geometric reasoning, MLLMs hallucinate frequently, leading to inaccurate reasoning. We attribute this to the perceptual bottleneck in MLLMs, which caps the benefits of reasoning training. To quantify this, we design a Geo-Perception Question-Answering (GeoPQA) benchmark, targeting basic geometric concepts and spatial relationships. Experiments on GeoPQA reveal significant shortcomings of MLLMs in visual perception, constraining RL reward signals for training. To address this bottleneck, we propose a two-stage RL training framework by first enhancing the visual perception of geometric structures, then fostering reasoning capabilities. Applied to Qwen2.5-VL-3B-Instruct, our two-stage training improves geometric reasoning by 9.7% and problem-solving by 9.1%, compared to the direct reasoning training approach. Our method also generalizes to other vision-intensive domains like figure understanding, highlighting the importance of perceptual grounding in effective MLLM reasoning.

Current large language models (LLMs) generally show a significant performance gap in alignment between English and other languages.To bridge this gap, existing research typically leverages the model’s responses in English as a reference to select the best/worst responses in other languages, which are then used for Direct Preference Optimization (DPO) training.However, we argue that there are two limitations in the current methods that result in noisy multilingual preference data and further limited alignment performance: 1) Not all English responses are of high quality, and using a response with low quality may mislead the alignment for other languages. 2) Current methods usually use biased or heuristic approaches to construct multilingual preference pairs.To address these limitations, we design a consistency-based data selection method to construct high-quality multilingual preference data for improving multilingual alignment (CM-Align).Specifically, our method includes two parts: consistency-guided English reference selection and cross-lingual consistency-based multilingual preference data construction.Experimental results on three LLMs and three common tasks demonstrate the effectiveness and superiority of our method, which further indicates the necessity of constructing high-quality preference data.

Inference constitutes the majority of costs throughout the lifecycle of a large language model (LLM). While numerous LLM inference engines focusing primarily on low-level optimizations have been developed, there is a scarcity of non-intrusive client-side frameworks that perform high-level optimizations. In this paper, we introduce Cache Saver, a modular, plug-and-play, and asynchronous framework that facilitates high-level inference optimizations, thereby integrating cleanly into existing systems without requiring changes to the end-user application logic or the underlying LLM. The key novelty is a *namespace-aware list-valued cache* that ensures *statistical integrity* of LLM responses by generating *i.i.d.* responses within a namespace as well as ensuring *reproducibility*. Moreover, as a direct consequence of operating at a high level, Cache Saver supports both local and online models. We conduct extensive experiments with five representative state-of-the-art reasoning strategies, five diverse benchmark tasks, and three different LLMs. On average across all methods, tasks, and LLMs, Cache Saver reduces cost by ≃ 25% and CO₂ by ≃ 35%. Notably, Cache Saver excels in practical machine learning scenarios such as benchmarking across multiple methods or conducting ablation analysis of a specific method, obtaining substantial cost and carbon footprint reduction of ≃ 60%. Cache Saver is publicly available at [https://github.com/au-clan/cachesaver](https://github.com/au-clan/cachesaver).

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Evaluating Cultural Knowledge and Reasoning in LLMs Through Persian Allusions
Melika Nobakhtian | Yadollah Yaghoobzadeh | Mohammad Taher Pilehvar

Allusion recognition—a task demanding contextual activation of cultural knowledge—serves as a critical test of LLMs’ ability to deploy stored information in open-ended, figurative settings. We introduce a framework for evaluating Persian literary allusions through (1) classical poetry annotations and (2) LLM-generated texts incorporating allusions in novel contexts. By combining knowledge assessments, multiple-choice tasks, and open-ended recognition, we analyze whether failures stem from knowledge gaps or activation challenges. Evaluations across eleven LLMs highlight a notable observation: models exhibit strong foundational knowledge and high multiple-choice accuracy, yet performance drops substantially in open-ended tasks, especially for indirect references. Reasoning-optimized models generalize better to novel contexts, whereas distilled models show marked degradation in cultural reasoning. The gap underscores that LLMs’ limitations arise not from missing knowledge but from difficulties in spontaneously activating cultural references without explicit cues. We propose allusion recognition as a benchmark for contextual knowledge deployment, highlighting the need for training paradigms that bridge factual recall and culturally grounded reasoning. Our code, datasets and results are available at https://github.com/MelikaNobakhtian/Allusion

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Evolving Stances on Reproducibility: A Longitudinal Study of NLP and ML Researchers’ Views and Experience of Reproducibility
Craig Thomson | Ehud Reiter | João Sedoc | Anya Belz

Over the past 10 years in NLP/ML, as in other fields of science, there has been growing interest in, and work on, reproducibility and methods for improving it. Identical experiments producing different results can be due to variation between samples of evaluation items or evaluators, but it can also be due to poor experimental practice. Both can be mitigated by bringing multiple comparable studies together in systematic reviews that can draw conclusions beyond the level of the individual studies, but such systematic reviews barely exist in NLP/ML. The alternative is to focus on improving experimental practice and study-level reproducibility, and the first step in this direction is awareness of the importance of reproducibility and knowledge of how to improve it. Here we aim to assess (i) what NLP/ML practitioners’ current views and experience of reproducibility are, and (ii) to what extent they have changed over the past two years, a period of rapidly growing interest in reproducibility. We report for the first time, results from two identical surveys, the first carried out in 2022 and the second in 2024, each time surveying 149 NLP and ML researchers. The results from the 2024 survey assess i above. We then compare the results of the two surveys in order to address ii above. We find that views and experience overall are moving towards better practice and appreciation of reproducibility.

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KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration
Yajing Yang | Tony Deng | Min-Yen Kan

We propose KAHAN, a knowledge-augmented hierarchical framework that systematically extracts insights from raw tabular data at entity, pairwise, group, and system levels. KAHAN uniquely leverages LLMs as domain experts to drive the analysis. On DataTales financial reporting benchmark, KAHAN outperforms existing approaches by over 20% on narrative quality (GPT-4o), maintains 98.2% factuality, and demonstrates practical utility in human evaluation. Our results reveal that knowledge quality drives model performance through distillation, hierarchical analysis benefits vary with market complexity, and the framework transfers effectively to healthcare domains. The data and code are available at https://github.com/yajingyang/kahan.