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Logical Reasoning in Large Language Models: A Survey

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arxiv 2502.09100 v1 pith:EU7IKZJM submitted 2025-02-13 cs.AI cs.CL

Hanmeng Liu , Zhizhang Fu , Mengru Ding , Ruoxi Ning , Chaoli Zhang , Xiaozhang Liu , Yue Zhang This is my paper
classification cs.AIcs.CL
keywords reasoninglogicalllmsmodelscapabilitieslanguagelargestrategies
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ARGUS: Hallucination and Omission Evaluation in Video-LLMs

    cs.CV 2025-06 conditional novelty 7.0 of 10

    ARGUS measures hallucination and omission in free-form video captions using LLM-based entailment and temporal alignment, finding that even the best video-LLM still produces roughly 40% hallucinated content.

  2. Deductive Logic in Language Models: Horizontal vs Vertical Reasoning

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    A 2-layer, single-head attention-only transformer learns to perform multi-step logical deduction through induction-head circuits for rule completion, chaining, and final decision.

  3. MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs

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    A new 1,188-question multimodal benchmark covering deductive, inductive, and abductive reasoning shows that leading MLLMs score around 60% and are especially weak at abductive reasoning.

  4. DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching

    cs.SD 2025-08 unverdicted novelty 4.0 of 10

    DAFMSVC swaps source SSL features for similar target features and adds dual cross-attention plus flow matching to improve one-shot singing voice conversion.

  5. Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons

    cs.AI 2025-06 conditional novelty 3.0 of 10

    DeepSeek-R1 outperforms GPT-4o and DeepSeek-V3 on family tree and graph reasoning benchmarks at sizes 10 and 20, but all models collapse at size 40.

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