Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks. Despite these advances, significant reasoning failures persist, occurring even in seemingly simple scenarios. To systematically understand and address these shortcomings, we present the first comprehensive survey dedicated to reasoning failures in LLMs. We introduce a novel categorization framework that distinguishes reasoning into embodied and non-embodied types, with the latter further subdivided into informal (intuitive) and formal (logical) reasoning. In parallel, we classify reasoning failures along a complementary axis into three types: fundamental failures intrinsic to LLM architectures that broadly affect downstream tasks; application-specific limitations that manifest in particular domains; and robustness issues characterized by inconsistent performance across minor variations. For each reasoning failure, we provide a clear definition, analyze existing studies, explore root causes, and present mitigation strategies. By unifying fragmented research efforts, our survey provides a structured perspective on systemic weaknesses in LLM reasoning, offering valuable insights and guiding future research towards building stronger, more reliable, and robust reasoning capabilities. We additionally release a comprehensive collection of research works on LLM reasoning failures, as a GitHub repository at https://github.com/Peiyang-Song/Awesome-LLM-Reasoning-Failures, to provide an easy entry point to this area.
Dissecting Failure Dynamics in Large Language Model Reasoning
School of Information Science and Engineering, Yunnan University, Kunming, China（云南大学信息科学与工程学院，昆明，中国）；Yunnan Key Laboratory of Intelligent Systems and Computing, Kunming, China（云南省智能系统与计算重点实验室，昆明，中国）
Systematic Failures in Collective Reasoning under Distributed Information in Multi-Agent LLMs
School of Computer Science, Carnegie Mellon University, Pittsburgh, USA（计算机科学学院，卡内基梅隆大学，匹兹堡，美国）；School of Environment and Society, Institute of Science Tokyo, Tokyo, Japan（环境与社会学院，东京科学研究所，东京，日本）
The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes
Singapore Institute of Technology（新加坡理工大学）；Nvidia AI Center (SNAIC)（英伟达人工智能中心（SNAIC））；MIDAS Lab, IIIT Delhi（IIIT德里MIDAS实验室）；MIDAS Lab, IIT Mandi（IIT曼迪MIDAS实验室）；Owl Autonomous Imaging, Inc.（Owl自主成像公司）；College of Computing & Data Science, NTU Singapore（新加坡南洋理工大学计算与数据科学学院）；NVIDIA AI Technology Centre, Singapore（英伟达新加坡人工智能技术中心）；Department of Computer Science and Engineering, IIT Kanpur（IIT坎普尔计算机科学与工程系）
REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model
Tsinghua University（清华大学）；City University of Hong Kong（香港城市大学）；Peking University（北京大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北航）；Baidu Inc（百度公司）
本文提出 REMA 框架，将正确推理表征建模为低维推理流形，通过 k 近邻距离和逐层偏差定位 LLM 推理失效的起点。
An Investigation of Robustness of LLMs in Mathematical Reasoning: Benchmarking with Mathematically-Equivalent Transformation of Advanced Mathematical Problems
Department of Computer Science University of Illinois Urbana–Champaign（计算机科学系伊利诺伊大学厄巴纳-香槟分校）；Department of Computer Science Stanford University（计算机科学系斯坦福大学）
Beyond Accuracy: Diagnosing Algebraic Reasoning Failures in LLMs Across Nine Complexity Dimensions
Department of Computer Science & Information Systems BITS Pilani（比拉理工学院皮拉尼校区计算机科学与信息系统系）；School of Computer Engineering, KIIT Bhubaneshwar（KIIT布巴内斯瓦尔分校计算机工程学院）
Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning
Department of Computer Science, University College London, UK（英国伦敦大学学院计算机科学系）；Centre for Artificial Intelligence, University College London, UK（英国伦敦大学学院人工智能中心）
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Center for Data Science, New York University（纽约大学数据科学中心）；New York University（纽约大学）；Department of Linguistics, New York University（纽约大学语言学系）；Google（谷歌公司）；NYU Shanghai（上海纽约大学）；Anthropic（Anthropic公司）
When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning
Institute of Science Tokyo（东京科学大学）；Osaka University（大阪大学）；NII LLMC（日本信息学研究所语言、语言学与媒体中心）；Nara Institute of Science and Technology（奈良科学技术研究所）；Center of Juris-Informatics, ROIS-DS（日本学术研究推进机构跨学科科学研究中心法学信息学中心）
Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement
Alibaba Group（阿里巴巴集团）；Shanghai Key Laboratory of Data Science, Fudan University（上海数据科学国家重点实验室，复旦大学）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；School of Data Science, Fudan University（复旦大学数据科学学院）
Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
Mayo Clinic College of Medicine and Science（梅奥诊所医学与科学学院）；School of Computing and AI, Arizona State University（亚利桑那州立大学计算与人工智能学院）；Mayo Clinic Comprehensive Cancer Center（梅奥诊所综合癌症中心）；Department of Radiation Oncology, Mayo Clinic（梅奥诊所放射肿瘤科）；Department of Oncology, Mayo Clinic（梅奥诊所肿瘤科）；Department of Artificial Intelligence and Informatics, Mayo Clinic（梅奥诊所人工智能与信息学系）；Dana-Farber Cancer Institute, Harvard Medical School（哈佛医学院丹娜-法伯癌症研究所）
