LCoT-GV: Graph Attention Networks for Verifying Long Reasoning Chains in Large Language Models
On the map: LLM reasoning → Knowledge- and tool-augmented reasoning
Abstract (arXiv)
Large Reasoning Models produce Long Chains-of-Thought (LCoTs) which involve breaking down the problem into smaller reasoning steps before reaching the conclusion. However, these steps often contain contradictions, unsupported inferences, or irrelevant steps, even when the final answer is correct. We propose Long Chain-of-Thought Graph Verifier (LCoT-GV), a graph-based framework that represents LCoTs as reasoning graphs. Each node in the graph represents a reasoning step and the edges encode semantic and logical relations. A Graph Attention Network is then trained to predict chain-of-thought correctness from the reasoning graph. We construct a new graph-oriented verification dataset from multiple reasoning benchmarks for question answering in various domains. The results show that our method is competitive with the most similar approaches.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 6 of its 17 references that are in the map. First posted on arXiv 31 Aug 2026.
Builds on
6 of 17 references.
References · 6
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
- 2025Can Large Language Models Detect Errors in Long Chain-of-Thought Reasoning?cited by 43
- 2024General Purpose Verification for Chain of Thought Promptingcited by 16
- 2025ReasoningFlow: Semantic Structure of Complex Reasoning Tracescited by 12
- 2026Characterizing, Evaluating, and Optimizing Complex Reasoningcited by 5
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- 2025What Makes a Good Reasoning Chain? Uncovering Structural Patterns in Long Chain-of-Thought Reasoning
- 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
- 2024A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
- 2025Multi-chain Graph Refinement and Selection for Reliable Reasoning in Large Language Models
- 2026From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
- 2023GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach
- 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning
- 2024Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains