AI 中文总结
本研究针对LLM推理的失败归因缺陷，提出HPFA框架，通过超图配对分析高效定位根本原因，提升归因准确率与效率，训练的归因器可改善推理性能。
AI 中文摘要
反思是大语言模型（LLM）推理的强大机制，但其有效性取决于能否将失败准确归因于特定推理步骤，这是当前模型显著欠缺的能力。现有失败归因方法要么需要代价高昂的逐步反事实测试，该测试随轨迹长度扩展性能不佳，要么将推理轨迹视为扁平序列，忽略了固有的非线性逻辑依赖。我们提出基于超图的配对失败归因（HPFA）框架，通过将目标失败推理路径的超边与参考成功路径的超边进行比较来归因失败根本原因。通过缩减搜索空间，我们的方法能高效定位根本原因，并可通过监督微调与强化学习扩展合成归因数据，用于训练轻量级归因器模型。在数学推理与智能体编码任务上的实验表明，HPFA可大幅提升归因准确率与效率，且训练后的归因器在测试时能持续提升推理准确率，优于缺乏图结构或配对分析的基线方法。
英文摘要
Reflection is a powerful mechanism for LLM reasoning, yet its effectiveness hinges on accurately attributing failures to specific reasoning steps, a capability that current models notably lack. Existing failure attribution methods either require expensive step-by-step counterfactual testing that scales poorly with trajectory length, or treat reasoning traces as flat sequences that ignore the inherent non-linear logical dependencies. We propose a hypergraph-based paired failure attribution (HPFA) framework that attributes the failure root cause by comparing the hyperedges of the targeted failure reasoning path against a reference successful path. By reducing the search space, our method efficiently localizes root causes and enables scalable synthesis of attribution data for training a lightweight attributor model via supervised fine-tuning and reinforcement learning. Experiments on mathematical reasoning and agentic coding tasks demonstrate that HPFA can dramatically increase attribution accuracy and efficiency, and the trained attributor consistently improves reasoning accuracy at test time, outperforming baselines that lack graph structure or paired analysis.
