Large Reasoning Models (LRMs) achieve remarkable performance by explicitly generating multi-step chains of thought, but this capability incurs substantial inference latency and computational cost. Collaborative inference offers a promising solution by selectively allocating work between lightweight and large models, yet a fundamental challenge remains: determining when a reasoning step requires the capacity of a large model or the efficiency of a small model. Existing routing strategies either rely on local token probabilities or post-hoc verification, introducing significant inference overhead. In this work, we propose a novel perspective on step-wise collaboration: the difficulty of a reasoning step can be inferred from its very first token. Inspired by the “Aha Moment” phenomenon in LRMs, we show that the entropy of the initial token serves as a strong predictor of step difficulty. Building on this insight, we introduce GlimpRouter, a training-free step-wise collaboration framework. GlimpRouter employs a lightweight model to generate only the first token of each reasoning step and routes the step to a larger model only when the initial token entropy exceeds a threshold. Experiments on multiple benchmarks demonstrate that our approach significantly reduces inference latency while preserving accuracy. For instance, GlimpRouter attains a substantial 10.7% improvement in accuracy while reducing inference latency by 25.9% compared to a standalone large model on AIME25. These results suggest a simple yet effective mechanism for reasoning: allocating computation based on a glimpse of thought rather than full-step evaluation.

Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabilities are mainly evaluated on pristine, well-structured document images. We consider document reconstruction from shredded fragments, a challenging VRDU setting that requires integrating visual pattern recognition with semantic reasoning under significant content discontinuities. To facilitate systematic evaluation of complex VRDU tasks, we introduce ShredBench, a benchmark supported by an automated generation pipeline that renders fragmented documents directly from Markdown. The proposed pipeline ensures evaluation validity by allowing the flexible integration of latest or unseen textual sources to prevent training data contamination. ShredBench assesses four scenarios (English, Chinese, Code, Table) with three fragmentation granularities (8, 12, 16 pieces). Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap: The method is effective on intact documents; however, once the document is shredded, restoration becomes a significant challenge, with NED dropping sharply as fragmentation increases. Our findings highlight that current MLLMs lack the fine-grained cross-modal reasoning required to bridge visual discontinuities, identifying a critical gap in robust VRDU research.

Multi-agent systems powered by large language models have achieved strong performance on complex tasks, yet naive collaboration topologies often cause high communication costs and redundant context. Existing methods usually use a fixed communication graph and manage collaboration structure and shared memory in separate modules. Our log analysis of several representative systems shows that this separation leads to multiple copies of the same key facts in dialogue, memory and model inputs. We address this issue with EvoHyper, a framework based on an evolving hypergraph topology for multi-agent collaboration. In EvoHyper, a single hypergraph represents agents and shared memory, and each hyperedge serves as a collaboration unit that binds a group of agents to that shared memory. During execution a controller edits the hypergraph through a small set of predefined evolution operations, so collaboration units can spawn, update and merge as tasks unfold. Experiments on four benchmarks covering mathematical reasoning and code generation show that EvoHyper is (I) high-performing, achieving 3.2% to 7.8% accuracy gains over state-of-the-art methods, (II) efficient, reducing token consumption by up to 23.5%, and (III) adaptive, adjusting topology complexity according to task requirements.

Understanding and reasoning about entire soft-ware repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and CodeQA have advanced the field, they predominantly focus on small, self-contained code snippets. These setups fail to capture the complexity of real-world repositories, where effective understanding and reasoning often require navigating multiple files, understanding software architecture, and grounding answers in long-range code dependencies. In this paper, we present SWE-QA, a repository-level code question answering (QA) benchmark designed to facilitate research on automated QA systems in realistic code environments. SWE-QA involves 720 high-quality question-answer pairs spanning diverse categories, including intention understanding, cross-file reasoning, and multi-hop dependency analysis. To construct SWE-QA, we first crawled 77,100 GitHub issues from 12 popular repositories. Based on an analysis of naturally occurring developer questions extracted from these issues, we developed a two-level taxonomy of repository-level questions and constructed a set of seed questions for each category. For each category, we manually curated and validated questions and collected their corresponding answers. We evaluate six advanced LLMs on SWE-QA under various context augmentation strategies. Experimental results highlight the promise of LLMs.

Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variations with the same semantics. While large language models (LLMs) have enhanced text augmentation by their “knowledge emergence” capability, controlling the style and structure of these outputs remains challenging and requires meticulous prompt engineering. In this paper, we propose LMTransplant, a novel text augmentation paradigm leveraging LLMs. The core idea of LMTransplant is transplant-then-regenerate: incorporating seed text into a context expanded by LLM, and asking the LLM to regenerate a variant based on the expanded context. This strategy allows the model to create more diverse and creative content-level variants by fully leveraging the knowledge embedded in LLMs, while preserving the core attributes of the original text. We evaluate LMTransplant across various text-related tasks, demonstrating its superior performance over existing text augmentation methods. Moreover, LMTransplant demonstrates exceptional scalability as the size of augmented data grows.

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.

While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information. Prior work has shown that decomposing the levels of granularity (e.g., word, phrase, or sentence) for input tokens has produced substantial improvements, suggesting the possibility of enhancing Transformers via more fine-grained modeling of granularity. In this work, we present continuous decomposition of granularity for neural paraphrase generation (C-DNPG): an advanced extension of multi-head self-attention with: 1) a granularity head that automatically infers the hierarchical structure of a sentence by neurally estimating the granularity level of each input token; and 2) two novel attention masks, namely, granularity resonance and granularity scope, to efficiently encode granularity into attention. Experiments on two benchmarks, including Quora question pairs and Twitter URLs have shown that C-DNPG outperforms baseline models by a significant margin. Qualitative analysis reveals that C-DNPG indeed captures fine-grained levels of granularity with effectiveness.

Attention based methods for image-text generation often focus on visual features individually, while ignoring relationship information among image features that provides important guidance for generating sentences. To alleviate this issue, in this work we propose the Joint Relationship Attention Network (JRAN) that novelly explores the relationships among the features. Specifically, different from the previous relationship based approaches that only explore the single relationship in the image, our JRAN can effectively learn two relationships, the visual relationships among region features and the visual-semantic relationships between region features and semantic features, and further make a dynamic trade-off between them during outputting the relationship representation. Moreover, we devise a new relationship based attention, which can adaptively focus on the output relationship representation when predicting different words. Extensive experiments on large-scale MSCOCO and small-scale Flickr30k datasets show that JRAN achieves state-of-the-art performance. More remarkably, JRAN achieves new 28.3% and 58.2% performance in terms of BLEU4 and CIDEr metric on Flickr30k dataset.