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Papers

arxiv:2602.06176

Large Language Model Reasoning Failures

Published on Feb 5

· Submitted by

Peiyang Song on Feb 9

Stanford University

Upvote

Authors:

Peiyang Song ,

Pengrui Han ,

Abstract

Large language models exhibit significant reasoning failures that can be categorized into embodied and non-embodied types, with fundamental, application-specific, and robustness-related subtypes, requiring systematic analysis and mitigation strategies.

AI-generated summary

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.

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p-song1

Paper author Paper submitter 10 days ago

LLMs show strong reasoning abilities but still fail in surprisingly simple situations. In this work, we systematically examine those failures, introducing a framework that categorizes reasoning types (embodied, informal, and formal) and classifies failure modes (fundamental, application-specific, and robustness-related). It analyzes root causes, reviews existing research, and outlines mitigation strategies to guide the development of more reliable and robust reasoning systems.