# Logical Reasoning in Large Language Models: A Survey

arXiv: https://arxiv.org/abs/2502.09100 · HTML: https://mlsanity.com/paper/2502.09100/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 18 of its 87 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 18 of 18)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions](https://mlsanity.com/paper/2411.14405/) (2024)
- [Multi-Step Reasoning with Large Language Models, a Survey](https://mlsanity.com/paper/2407.11511/) (2024)
- [o1-Coder: an o1 Replication for Coding](https://mlsanity.com/paper/2412.00154/) (2024)
- [A Survey of Reasoning with Foundation Models](https://mlsanity.com/paper/2312.11562/) (2023)
- [Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs](https://mlsanity.com/paper/2404.15676/) (2024)
- [Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thought](https://mlsanity.com/paper/2308.08614/) (2023)
- [Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents](https://mlsanity.com/paper/2402.00798/) (2024)
- [LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages](https://mlsanity.com/paper/2406.06196/) (2024)
- [Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning](https://mlsanity.com/paper/2410.08047/) (2024)
- [Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework](https://mlsanity.com/paper/2412.16953/) (2024)
- [LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning](https://mlsanity.com/paper/2409.12929/) (2024)
- [Aligning with Logic: Measuring, Evaluating and Improving Logical Preference Consistency in Large Language Models](https://mlsanity.com/paper/2410.02205/) (2024)
- [Inductive Learning of Logical Theories with LLMs: An Expressivity-Graded Analysis](https://mlsanity.com/paper/2408.16779/) (2024)
- [Assessing the Sensitivity and Alignment of FOL Closeness Metrics](https://mlsanity.com/paper/2501.08613/) (2025)
- [Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension](https://mlsanity.com/paper/2409.14495/) (2024)

## Built on by (most cited first, 30 of 55)

- [Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://mlsanity.com/paper/2503.09567/) (2025)
- [Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification](https://mlsanity.com/paper/2504.05419/) (2025)
- [A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems](https://mlsanity.com/paper/2504.09037/) (2025)
- [MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs](https://mlsanity.com/paper/2505.21327/) (2025)
- [Pruning the Unsurprising: Efficient LLM Reasoning via First-Token Surprisal](https://mlsanity.com/paper/2508.05988/) (2025)
- [Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks](https://mlsanity.com/paper/2504.18838/) (2025)
- [ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning](https://mlsanity.com/paper/2506.09513/) (2025)
- [The Geometry of Reasoning: Flowing Logics in Representation Space](https://mlsanity.com/paper/2510.09782/) (2025)
- [Large Language Model Reasoning Failures](https://mlsanity.com/paper/2602.06176/) (2026)
- [Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning Models](https://mlsanity.com/paper/2506.17114/) (2025)
- [ARGUS: Hallucination and Omission Evaluation in Video-LLMs](https://mlsanity.com/paper/2506.07371/) (2025)
- [A Comment On "The Illusion of Thinking": Reframing the Reasoning Cliff as an Agentic Gap](https://mlsanity.com/paper/2506.18957/) (2025)
- [Evaluating the Logical Reasoning Abilities of Large Reasoning Models](https://mlsanity.com/paper/2505.11854/) (2025)
- [DeepSeek-R1 Outperforms Gemini 2.0 Pro, OpenAI o1, and o3-mini in Bilingual Complex Ophthalmology Reasoning](https://mlsanity.com/paper/2502.17947/) (2025)
- [GEAR: A General Evaluation Framework for Abductive Reasoning](https://mlsanity.com/paper/2509.24096/) (2025)
- [LogicReward: Incentivizing LLM Reasoning via Step-Wise Logical Supervision](https://mlsanity.com/paper/2512.18196/) (2025)
- [DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution](https://mlsanity.com/paper/2601.13761/) (2026)
- [From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI](https://mlsanity.com/paper/2606.14502/) (2026)
- [From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models](https://mlsanity.com/paper/2505.21935/) (2025)
- [AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models](https://mlsanity.com/paper/2509.24269/) (2025)
- [Agentic Design Patterns: A System-Theoretic Framework](https://mlsanity.com/paper/2601.19752/) (2026)
- [Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation](https://mlsanity.com/paper/2507.19333/) (2025)
- [Large Language Model for OWL Proofs](https://mlsanity.com/paper/2601.12444/) (2026)
- [Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons](https://mlsanity.com/paper/2506.23128/) (2025)
- [Investigating Language Model Capabilities to Represent and Process Formal Knowledge: A Preliminary Study to Assist Ontology Engineering](https://mlsanity.com/paper/2509.10249/) (2025)
- [Large Language Models Imitate Logical Reasoning, but at what Cost?](https://mlsanity.com/paper/2509.12645/) (2025)
- [Socio-technical aspects of Agentic AI](https://mlsanity.com/paper/2601.06064/) (2025)
- [Improving Symbolic Translation of Language Models for Logical Reasoning](https://mlsanity.com/paper/2601.09446/) (2026)
- [AILS-NTUA at SemEval-2026 Task 12: Graph-Based Retrieval and Reflective Prompting for Abductive Event Reasoning](https://mlsanity.com/paper/2603.04319/) (2026)
- [Adaptive Robust Estimator for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2603.21574/) (2026)

## Abstract (arXiv)

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.