Standard in-context learning (ICL) assumes identical output spaces between test and retrieval datasets (fully aligned). However, in practice, these datasets can be fully aligned, partially aligned, or fully disjoint in label space (Output space), forming an information continuum from rich to scarce. Naive ICL often becomes ineffective under such mismatches. In this work, we challenge this assumption by demonstrating that the retrieval dataset need not perfectly align with the test dataset, as long as it remains related to the target task. We propose Task-Related In-Context Learning (TRICL), a unified framework for ICL under output-space mismatch, designed to cover the full continuum of scenarios. TRICL first identifies demonstrations in the mismatched retrieval dataset that are relevant to the test label space via a lightweight Bayesian probabilistic criterion, and uses them to form a related dataset. TRICL then perform ICL on the related dataset to obtain preliminary predictions; finally, TRICL leverage these intermediate predictions to reduce and transform the output space of the original test task, thereby improving the performance of LLMs. Even in the most information-scarce fully disjoint scenario, as long as the retrieval dataset is task-related to the test task, TRICL achieves state-of-the-art (SOTA) results across three LLMs, three task types, and four datasets. Moreover, TRICL remains effective in the fully aligned and partially aligned scenarios, consistently yielding strong gains over competitive baselines. Moreover, TRICL also extends to generative task.

Existing In-context Learning (ICL) typically assumes the retrieval dataset contains demonstrations for all output label spaces. However, in real-world scenarios, delays in dataset updates or incomplete data annotation may result in the retrieval dataset containing labeled demonstrations for only a subset of the output space. We refer to this phenomenon as an incomplete retrieval dataset and define the in-context learning under this condition as Incomplete In-context Learning (IICL). To address IICL, we propose Iterative Judgments and Integrated Prediction (IJIP), a framework with train-free and train-based variants. For classification, the iterative judgments stage of IJIP reformulates an (m)-class problem into (m) binary tasks, converting IICL into standard ICL. The integrated prediction stage of IJIP then refines results using both the input and initial predictions. We further extend IJIP to text regression and generation, and introduce lightweight variants that reduce computation and token costs. Across six LLMs, seven tasks, and eight datasets, IJIP achieves state-of-the-art results under two incompleteness settings and even outperforms standard ICL with complete labels. IJIP also supports a semi-supervised variant and can serve as a plug-and-play enhancement for existing ICL and zero-shot methods.

Recently, long-thought reasoning LLMs, such as OpenAI’s O1, adopt extended reasoning processes similar to how humans ponder over complex problems. This reasoning paradigm significantly enhances the model’s problem-solving abilities and achieves promising results. However, long-thought reasoning process leads to a substantial increase in inference time. A pressing challenge is reducing the inference overhead of long-thought LLMs while ensuring accuracy. In this paper, we identify that long-thought reasoning models struggle to effectively allocate token budgets based on problem difficulty and reasoning redundancies. To address this, we propose Length-Harmonizing Fine-Tuning (O1-Pruner), aiming at minimizing reasoning overhead while maintaining accuracy. This effective fine-tuning method first estimates the LLM’s baseline performance through pre-sampling and then uses RL-style fine-tuning to encourage the model to generate shorter reasoning processes under accuracy constraints. This allows the model to achieve efficient reasoning with lower redundancy while maintaining accuracy. Experiments on various mathematical reasoning benchmarks show that O1-Pruner not only significantly reduces inference overhead but also achieves higher accuracy, providing a novel and promising solution to this challenge.

Current multi-task adversarial text attacks rely on abundant access to shared internal features and numerous queries, often limited to a single task type. As a result, these attacks are less effective against practical scenarios involving black-box feedback APIs, limited queries, or multiple task types. To bridge this gap, we propose Cluster and Ensemble Mutil-task Text Adversarial Attack (CEMA), an effective black-box attack that exploits the transferability of adversarial texts across different tasks. CEMA simplifies complex multi-task scenarios by using a deep-level substitute model trained in a plug-and-play manner for text classification, enabling attacks without mimicking the victim model. This approach requires only a few queries for training, converting multi-task attacks into classification attacks and allowing attacks across various tasks. CEMA generates multiple adversarial candidates using different text classification methods and selects the one that most effectively attacks substitute models. In experiments involving multi-task models with two, three, or six tasks—spanning classification, translation, summarization, and text-to-image generation—CEMA demonstrates significant attack success with as few as 100 queries. Furthermore, CEMA can target commercial APIs (e.g., Baidu and Google Translate), large language models (e.g., ChatGPT 4o), and image-generation models (e.g., Stable Diffusion V2), showcasing its versatility and effectiveness in real-world applications.