LLM存在根本性缺陷，极易被攻击，一个简单提示就能绕开安全限制。
It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue …
用思维链蒸馏提升表格重排序效果，TabRank创新方法亮相。
arXiv:2607.25182v1 Announce Type: cross Abstract: The ability to retrieve relevant tables for answering questions is a key task for structured informa…
提出协同演化链式思考提示，让LLM在图推理任务中自我优化，刷新性能天花板
arXiv:2607.14114v1 Announce Type: new Abstract: Graph learning under distribution shift presents a persistent challenge, where models adapt to new gra…
多模态大模型推理新范式CoLT，用潜在思维链替代文本推理，大幅提升视觉推理速度。
arXiv:2606.31986v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has enabled multi-modal large language models (MLLMs) to tackle compl…
提出UniT统一框架，实现多模态链式思考在测试时的高效扩展，为多模态推理带来新范式。
arXiv:2602.12279v2 Announce Type: replace-cross Abstract: Unified models can handle both multimodal understanding and generation within a single archi…
新方法通过智能体引导链式思维，让大模型推理更高效、更可控。
arXiv:2606.03965v1 Announce Type: cross Abstract: Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but…
混合中长度策略优化实现思维链高效压缩，兼顾性能与成本。
arXiv:2606.01934v1 Announce Type: new Abstract: Large language models achieve remarkable performance via extended chain-of-thought (CoT) reasoning, ye…
提出基于反事实共形解码的COFT方法，保障大模型链式思维推理的公平性，ICML 2026前沿工作。
arXiv:2605.30641v1 Announce Type: cross Abstract: Large language models (LLMs) can reveal and amplify societal biases during chain-of-thought (CoT) ge…
BC Protocol通过结构化双专家对话生成高质量CoT训练数据，实验证明效果优于单专家独立编写，为大模型后训练提供新思路。
arXiv:2605.25549v1 Announce Type: cross Abstract: High-quality expert chain-of-thought (CoT) data is one of the core bottlenecks in large language mod…
ICML 2026 接收论文，将 k-means 聚类重新诠释为链式思维推理，为图学习提供全新数学视角。
arXiv:2605.24867v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large la…
首个带思维链推理的逐步数据集，让LLM更精准地优化张量程序，加速AI编译。
arXiv:2605.25954v1 Announce Type: cross Abstract: Despite the strong reasoning capabilities of large language models (LLMs), optimizing the execution …
链式思维加持3D点云推理，PointLLM-R让大模型空间理解更精准
arXiv:2605.22013v1 Announce Type: new Abstract: Understanding 3D point clouds through language remains a fundamental challenge in computer graphics an…
评估多模态大模型操作中心链式思维推理能力的新基准，强调接地与可验证性。
arXiv:2605.19559v1 Announce Type: new Abstract: The rapid development of Multimodal Large Language Models (MLLMs) has led to growing interest in egoce…
论文提出早停链式思维方法，减少大模型推理成本，无需白盒干预。
arXiv:2509.14004v2 Announce Type: replace Abstract: Reasoning large language models (LLMs) have demonstrated superior capacities in solving complicate…
提出ACIL方法，自动化链式思维推理与上下文学习结合，提升LLM多步推理能力。
arXiv:2605.17088v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have shown that Chain-of-Thought (CoT) reasoning can s…