Large Language Model Reasoning Failures

Source: Hacker News

Abstract

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:

1. Fundamental failures intrinsic to LLM architectures that broadly affect downstream tasks.
2. Application‑specific limitations that manifest in particular domains.
3. 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 offers a structured perspective on systemic weaknesses in LLM reasoning, guiding future research toward 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.