title: Logical Reasoning in Large Language Models: A Survey
summary: With the emergence of advanced reasoning models like OpenAI o3 and
  DeepSeek-R1, large language models (LLMs) have demonstrated remarkable
  reasoning capabilities. However, their ability to perform rigorous logical
  reasoning remains an open question. This survey synthesizes recent advancements
  in logical reasoning within LLMs, a critical area of AI research. It outlines
  the scope of logical reasoning in LLMs, its theoretical foundations, and the
  benchmarks used to evaluate reasoning proficiency. We analyze existing
  capabilities across different reasoning paradigms - deductive, inductive,
  abductive, and analogical - and assess strategies to enhance reasoning
  performance, including data-centric tuning, reinforcement learning, decoding
  strategies, and neuro-symbolic approaches. The review concludes with future
  directions, emphasizing the need for further exploration to strengthen logical
  reasoning in AI systems.
paper_authors._id: 67aeb0a3d58f4990b384d83e
paper_authors.name: Hanmeng Liu
paper_authors._id: 67aeb0a3d58f4990b384d83f
paper_authors.name: Zhizhang Fu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-14T11:10:54.325Z
paper_authors._id: 6467136a8334813a7ae1d1b0
paper_authors.avatarUrl: /avatars/d0fd37532c830e8bef14995148190f9f.svg
paper_authors.fullname: Zhizhang Fu
paper_authors.type: user
paper_authors.user: HarryFu
paper_authors._id: 67aeb0a3d58f4990b384d840
paper_authors.name: Mengru Ding
paper_authors._id: 67aeb0a3d58f4990b384d841
paper_authors.name: Ruoxi Ning
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-14T06:28:50.414Z
paper_authors._id: 62e47d1b6a82e063860c587e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62e47d1b6a82e063860c587e/jvFt1caSZNWDQTYKZQ9K-.jpeg
paper_authors.fullname: ruoxining
paper_authors.type: user
paper_authors.user: ruoxining
paper_authors._id: 67aeb0a3d58f4990b384d842
paper_authors.name: Chaoli Zhang
paper_authors._id: 67aeb0a3d58f4990b384d843
paper_authors.name: Xiaozhang Liu
paper_authors._id: 67aeb0a3d58f4990b384d844
paper_authors.name: Yue Zhang
paper_title: Logical Reasoning in Large Language Models: A Survey
paper_summary: With the emergence of advanced reasoning models like OpenAI o3 and
  DeepSeek-R1, large language models (LLMs) have demonstrated remarkable
  reasoning capabilities. However, their ability to perform rigorous logical
  reasoning remains an open question. This survey synthesizes recent advancements
  in logical reasoning within LLMs, a critical area of AI research. It outlines
  the scope of logical reasoning in LLMs, its theoretical foundations, and the
  benchmarks used to evaluate reasoning proficiency. We analyze existing
  capabilities across different reasoning paradigms - deductive, inductive,
  abductive, and analogical - and assess strategies to enhance reasoning
  performance, including data-centric tuning, reinforcement learning, decoding
  strategies, and neuro-symbolic approaches. The review concludes with future
  directions, emphasizing the need for further exploration to strengthen logical
  reasoning in AI systems.
paper_ai_summary: This survey examines advancements in logical reasoning within large language models, analyzing capabilities across various paradigms and strategies for improvement.
paper_ai_keywords: large language models logical reasoning deductive reasoning inductive reasoning abductive reasoning analogical reasoning data-centric tuning reinforcement learning decoding strategies neuro-symbolic approaches