Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks. However, prior work has mainly focused on logical reasoning tasks (e.g. arithmetic, commonsense QA); it remains unclear whether improvements hold for more diverse types of reasoning, especially in socially situated contexts. Concretely, we perform a controlled evaluation of zero-shot CoT across two socially sensitive domains: harmful questions and stereotype benchmarks. We find that zero-shot CoT reasoning in sensitive domains significantly increases a model's likelihood to produce harmful or undesirable output, with trends holding across different prompt formats and model variants. Furthermore, we show that harmful CoTs increase with model size, but decrease with improved instruction following. Our work suggests that zero-shot CoT should be used with caution on socially important tasks, especially when marginalized groups or sensitive topics are involved.
Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in Its Latent Thoughts
Department of Computer Science and Engineering, The Ohio State University, USA（计算机科学与工程系，俄亥俄州立大学）；Department of Biomedical Informatics, The Ohio State University, USA（生物医学信息学系，俄亥俄州立大学）；Translational Data Analytics Institute, The Ohio State University, USA（转化数据分析研究所，俄亥俄州立大学）
THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models
School of Artificial intelligence, JiLin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, JiLin University（吉林大学知识驱动人机智能工程研究中心）；International Center of Future Science, JiLin University（吉林大学未来科学国际中心）
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Southern California（南加利福尼亚大学）；Yonsei University（延世大学）；University of Washington（华盛顿大学）
The Price of a Second Thought: On the Evaluation of Reasoning Efficiency in Large Language Models
University of Electronic Science and Technology of China（电子科技大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Nanyang Technological University（南洋理工大学）
Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）
H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking
Duke University（杜克大学）；Accenture（埃森哲）；Accenture Security（埃森哲安全）
School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Kuaishou Technology（快手科技）；Shanghai Jiao Tong University（上海交通大学）
SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly（联合NTU-UBC老龄化积极生活卓越研究中心）；Alibaba-NTU Global e-Sustainability CorpLab (ANGEL)（阿里巴巴-NTU全球可持续性企业实验室（ANGEL））；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）；KTH Royal Institute of Technology, Sweden（瑞典皇家理工学院）
Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study
School of Informatics, Xiamen University, China（厦门大学信息学院）；Baidu Inc., Beijing, China（百度公司）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
On the Thinking-Language Modeling Gap in Large Language Models
Hong Kong Baptist University（香港 Baptist 大学）；MBZUAI Carnegie Mellon University（MBZUAI 美国卡内基梅隆大学）；The University of Sydney（悉尼大学）；The Chinese University of Hong Kong（香港中文大学）
Think Consistently, Reason Efficiently: Energy-Based Calibration for Implicit Chain-of-Thought
University of Oxford（牛津大学）；Tsinghua University（清华大学）；Tencent（腾讯）；Southern University of Science and Technology（南方科技大学）；National University of Singapore（新加坡国立大学）
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
Department of Computer Science, Princeton University, Princeton, NJ, USA（计算机科学系，普林斯顿大学）；NYU Center for Data Science, New York University, New York, NY, USA（纽约大学数据科学中心）；Department of Psychology, Princeton University, Princeton, NJ, USA（心理学系，普林斯顿大学）
