LCoT-GV: Graph Attention Networks for Verifying Long Reasoning Chains in Large Language Models

• URL: http://arxiv.org/abs/2608.30679v1
• Date: Mon, 31 Aug 2026 12:21:22 GMT
• Title: LCoT-GV: Graph Attention Networks for Verifying Long Reasoning Chains in Large Language Models
• Abstract summary: We propose a graph-based framework that represents Long Chain-of-Thought Graph Verifier (LCoT-GV) as reasoning graphs.<n>A Graph Attention Network is then trained to predict chain-of-thought correctness from the reasoning graph.
• Score: 1.546945230112218
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

Related papers

• From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs [52.0811904328273]
  Real-world reasoning requires integrating multiple premises and solving subproblems in parallel.<n>Recent approaches rely on externally provided graphs and do not explore how LLMs can construct and use their own graph-structured reasoning.<n>We propose Self-Graph Reasoning (SGR), a framework that enables LLMs to explicitly represent their reasoning process as a structured graph before producing the final answer.
  arXiv  Detail & Related papers  (2026-01-07T05:27:41Z)
• SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering [24.405588261303834]
  SentGraph is a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering.<n>During online retrieval, SentGraph performs graph-guided evidence selection and path expansion to retrieve fine-grained sentence-level evidence.
  arXiv  Detail & Related papers  (2026-01-06T13:39:51Z)
• Rethinking and Benchmarking Large Language Models for Graph Reasoning [36.30471027175558]
  Large Language Models (LLMs) for Graph Reasoning have been extensively studied over the past two years.<n>Recent studies underscore the potential of LLMs in handling graph reasoning tasks, but their performance is underwhelming.
  arXiv  Detail & Related papers  (2025-09-29T04:10:12Z)
• Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning [7.1931434571877375]
  Graph Neural Networks (GNNs) are limited by fixed label spaces, while Large Language Models (LLMs) lack structural inductive biases.<n>Recent advances in Large Reasoning Models (LRMs) provide a zero-shot alternative via explicit, long chain-of-thought reasoning.<n>We propose a GNN-free approach that reformulates graph tasks--node classification, link prediction, and graph classification--as textual reasoning problems solved by LRMs.
  arXiv  Detail & Related papers  (2025-08-24T14:49:02Z)
• Do Larger Language Models Generalize Better? A Scaling Law for Implicit Reasoning at Pretraining Time [73.22651918134808]
  This work shows counterintuitive effects of model size scaling and provides new insights into the relationship between scaling and reasoning in language models (LMs)<n>We pretrain LMs from scratch on a synthetic implicit multihop reasoning environment designed to replicate the structure and distribution of real-world large-scale knowledge graphs.<n>We then assess the LMs' ability to complete the missing edges in the graph, which requires multi-hop reasoning that can be viewed as a simplification of implicit reasoning during real-world pretraining.
  arXiv  Detail & Related papers  (2025-04-04T17:57:22Z)
• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
  We develop a flexible question-answering framework targeting real-world textual graphs. We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
  arXiv  Detail & Related papers  (2024-02-12T13:13:04Z)
• Graph Elicitation for Guiding Multi-Step Reasoning in Large Language Models [16.432208223793666]
  Chain-of-Thought prompting along with sub-question generation and answering has enhanced multi-step reasoning capabilities. We propose a GE-Reasoning method, which directs Large Language Models to generate proper sub-questions and corresponding answers. Our approach outperforms previous CoT prompting methods and their variants on multi-hop question answering benchmark datasets.
  arXiv  Detail & Related papers  (2023-11-16T10:36:08Z)
• Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models [74.40196814292426]
  We propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. GoT captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. We evaluate GoT's performance on a text-only reasoning task and a multimodal reasoning task.
  arXiv  Detail & Related papers  (2023-05-26T02:15:09Z)
• Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching [68.35685422301613]
  We propose a novel non-parametric subgraph matching framework, dubbed MatchExplainer, to explore explanatory subgraphs. It couples the target graph with other counterpart instances and identifies the most crucial joint substructure by minimizing the node corresponding-based distance. Experiments on synthetic and real-world datasets show the effectiveness of our MatchExplainer by outperforming all state-of-the-art parametric baselines with significant margins.
  arXiv  Detail & Related papers  (2023-01-07T05:14:45Z)
• Reinforced Causal Explainer for Graph Neural Networks [112.57265240212001]
  Explainability is crucial for probing graph neural networks (GNNs) We propose a reinforcement learning agent, Reinforced Causal Explainer (RC-Explainer) RC-Explainer generates faithful and concise explanations, and has a better power to unseen graphs.
  arXiv  Detail & Related papers  (2022-04-23T09:13:25Z)
• ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning [65.15423587105472]
  We present a new generative and structured commonsense-reasoning task (and an associated dataset) of explanation graph generation for stance prediction. Specifically, given a belief and an argument, a model has to predict whether the argument supports or counters the belief and also generate a commonsense-augmented graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. A significant 83% of our graphs contain external commonsense nodes with diverse structures and reasoning depths.
  arXiv  Detail & Related papers  (2021-04-15T17:51:36Z)
• SRLGRN: Semantic Role Labeling Graph Reasoning Network [22.06211725256875]
  This work deals with the challenge of learning and reasoning over multi-hop question answering (QA) We propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths. Our proposed approach shows competitive performance on the HotpotQA distractor setting benchmark compared to the recent state-of-the-art models.
  arXiv  Detail & Related papers  (2020-10-07T18:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.