Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the robustness of this approach to corruptions in intermediate reasoning steps remains poorly understood. This paper presents a comprehensive empirical evaluation of LLM robustness to a structured taxonomy of 5 CoT perturbation types: \textit{MathError, UnitConversion, Sycophancy, SkippedSteps,} and \textit{ExtraSteps}. We evaluate 13 models spanning three orders of magnitude in parameter count, testing their ability to complete mathematical reasoning tasks despite perturbations injected in the reasoning chain. Our key findings reveal heterogeneous vulnerability patterns: MathError perturbations produce the most severe degradation in small models (50-60\% accuracy loss) but show strong scaling benefits; UnitConversion remains challenging across all scales (>5\% loss even for midsized models); ExtraSteps incur minimal accuracy degradation (0-6\%) even for the smallest of models; Sycophancy and SkippedSteps produce modest effects ($\sim$10\% loss for small models) and slightly improve with scale. Scaling relationships show that model size serve as a protective factor against many perturbations but not always. These findings have direct implications for deploying LLMs in multi-stage reasoning pipelines and underscore the necessity of task-specific robustness assessments and mitigation strategies. The code and results are available at https://github.com/Mystic-Slice/CoTPerturbation
Draw with Thought: Unleashing Multimodal Reasoning for Scientific Diagram Generation
Nanjing University of Information Science \& Technology Nanjing China；East China Normal University Shanghai China；The Hong Kong University of Science；Brown University Providence America；Southwest Jiaotong University Chengdu China；Nanjing University of Information Science \& Technology；East China Normal University；Brown University；Southwest Jiaotong University
提出无需训练的框架Draw with Thought，通过认知思维链推理将科学图表重建为可编辑的mxGraph XML代码，并发布Plot2XML基准，实验证明该方法能生成高保真且结构有效的重建结果。
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do
The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所复杂系统认知与决策智能实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Learning to Edit Knowledge via Instruction-based Chain-of-Thought Prompting
Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；Huawei Technologies Ltd.（华为技术有限公司）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
CoT-VLM4Tar: Chain-of-Thought Guided Vision-Language Models for Traffic Anomaly Resolution
City University of Hong Kong（香港城市大学）；Soochow University（苏州大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Honhai Research Institute（鸿海研究院）
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
LARG；Research Center for Social Computing and Interactive Robotics（社会计算与交互机器人研究中心）；Harbin Institute of Technology（哈尔滨工业大学）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；The University of Hong Kong（香港大学）；Fudan University（复旦大学）
Hey GPT-OSS, Looks Like You Got It -- Now Walk Me Through It! An Assessment of the Reasoning Language Models Chain of Thought Mechanism for Digital Forensics
Chair for Cybersecurity, University of Augsburg, Augsburg, Germany（网络安全系，乌尔姆大学，乌尔姆，德国）
While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it …
Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；School of Chinese as a Second Language, Peking University（北京大学第二语言学院）
Which Heads Matter for Reasoning? RL-Guided KV Cache Compression
Westlake University（西华大学）；McGill University（麦吉尔大学）；Mila - Quebec AI Institute（魁北克AI研究院）；Zhejiang University（浙江大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德智能大学）
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Nanyang Technological University（南洋理工大学）；University of Edinburgh（爱丁堡大学）；City University of Hong Kong（香港城市大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
TUR-DPO: Topology- and Uncertainty-Aware Direct Preference Optimization
Artificial Intelligence and Innovation Centre, University of Kurdistan, Erbil, Iraq（人工智能与创新中心，乌尔米耶大学，伊拉克）；Department of Computer Engineering, University of Kurdistan, Iran（计算机工程系，乌尔米耶大学，伊朗）；Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia（人工智能研究与优化中心，塔伦斯大学澳大利亚，布里斯班，澳大利亚）；Research and Innovation Center, Obuda University, Budapest 1034, Hungary（研究与创新中心，奥布达大学，布达佩斯1034，匈牙利）；Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland（机器视觉与信号分析中心（CMVS），奥卢大学，芬兰）
Gala: Global LLM Agents for Text-to-Model Translation
Department of Computer Science, University of Southern California（南加州大学计算机科学系）；AI Center of Excellence, Fidelity Investments（富达投资人工智能卓越中心）；Department of Computer Science, Brown University（布朗大学计算机科学系）
