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）
Recent thinking models trained with reinforcement learning and backward-checking CoT often suffer from overthinking: they produce excessively long outputs even on simple problems, wasting computation. Existing evaluations, based on token efficiency, give an incomplete view as they neglect problem difficulty and intermediate computation costs. We formalize reasoning efficiency as a relative measure between thinking and instruct models, treating instruct models as the minimal-effort baseline. A systematic study across four thinking models and multiple benchmarks reveals two consistent patterns: (i) instruct models achieve higher efficiency overall, and (ii) problem difficulty affects efficiency, with thinking models wasting computation on easy problems but providing value on harder ones. Building on this insight, we propose COTHINK, a simple two-stage pipeline: an instruct model drafts a brief outline, and a thinking model expands it. On GSM8K, MATH500, and AIME24, COTHINK cuts token usage by 21.1% while keeping accuracy on four thinking models, and remains competitive with strong efficiency baselines.
Thought Graph: Generating Thought Process for Biological Reasoning
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Arizona State University（亚利桑那州立大学）；University of Pennsylvania（宾夕法尼亚大学）；University of California San Diego（加利福尼亚大学圣迭戈分校）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；California Institute of Technology（加利福尼亚理工学院）
Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments
School of Information Science and Technology, VISTEC（信息科学与技术学院，VISTEC）；KAIST（韩国科学技术院）；Cohere；SCB 10X；AI Singapore（AI新加坡）；Department of Computer Engineering, Chulalongkorn University（朱拉隆梭大学计算机工程系）
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer
Clemson University（克莱姆森大学）；Université de Montréal（蒙特利尔大学）；Center for Advanced AI, Accenture（Accenture高级人工智能中心）；The University of Alabama（阿拉巴马大学）；University of Kansas（堪萨斯大学）；Arizona State University（亚利桑那州立大学）
A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings
Beijing University of Posts and Telecommunications（北京邮电大学）；Dept. of Comp. Sci. & Tech., Tsinghua University（清华大学计算机科学与技术系）；Northeastern University（东北大学）；Institute for AI, Tsinghua University（清华大学人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学研究中心）
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；School of Information, Renmin University of China（中国人民大学信息学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京大数据管理与分析方法重点实验室）
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
Portland State University（波特兰州立大学）；Renmin University of China（中国人民大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Dalian Maritime University（大连海事大学）；University of Kansas（堪萨斯大学）
Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning
Department of Computing, The Hong Kong Polytechnic University（计算机系，香港理工大学）；Ningbo Digital Twin Institute, Eastern Institute of Technology（宁波数字孪生研究院，东部技术研究所）
Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning
Tencent BAC（腾讯BAC）；Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学学院）
ThoughtSource: A central hub for large language model reasoning data
Medical University of Vienna（维也纳医科大学）；Technical University of Denmark（丹麦技术大学）；Copenhagen University Hospital（哥本哈根大学医院）；The Royal Institute of Technology (KTH)（皇家理工学院（KTH））
Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learning
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能方向）；Didichuxing Co. Ltd（滴滴出行有限公司）；CSE, The Hong Kong University of Science and Technology（香港科学与技术大学计算机科学与工程系）
