AI 中文总结
针对多跳事实验证中智能体推理偏离目标及参数知识与证据冲突的问题，提出自反思智能体框架ReflectFact，经HOVER、EX-FEVER实验，性能优于最强基线。
AI 中文摘要
多跳事实验证是指通过对多份证据进行推理来验证主张的任务，它对打击社交媒体上的虚假信息至关重要，但仍极具挑战性。现有方法主要依赖多智能体协作，将事实验证分解为专门的子任务，但面临两个关键局限：其一，智能体执行单个子任务时可能未充分意识到全局验证目标，导致其推理偏离预期方向；其二，参数化知识与所提供证据之间的冲突可能破坏基于证据的推理，进而导致错误结论。为应对这些挑战，我们提出了ReflectFact，这是一个用于多跳事实验证的新型自反思智能体框架。ReflectFact引入了三项关键任务：显式推理路径规划，通过解析隐式实体、将主张分解为子问题，并将验证后的事实整合为结论，构建基于证据的推理路径；证据漂移验证，当基于证据的答案仅与智能体的参数化先验一致时，让智能体通过引用支持证据重新作答，从而校准证据漂移，确保基于证据的理解；推理反思验证，从全局任务视角重新检查每个推理步骤，若检测到不一致则重新生成，纠正位置偏差、替换偏差等推理缺陷。随后，智能体聚合经过验证的推理链以得出可靠结论。在HOVER和EX-FEVER数据集上开展的大量实验表明，ReflectFact可有效弥补现有方法在理解与推理方面的缺陷，实现了最先进的性能，在两个数据集上分别以3.32%和2.78%的优势超越了最强基线。
英文摘要
Multi-hop fact verification, which verifies claims by reasoning over multiple pieces of evidence, is critical for combating misinformation on social media yet remains highly challenging. Recent methods primarily rely on multi-agent collaboration to decompose fact verification into specialized subtasks. However, these methods face two critical limitations: (1) agents may perform individual subtasks without sufficient awareness of the global verification objective, causing their reasoning to deviate from the intended direction; and (2) conflicts between parametric knowledge and the provided evidence may undermine evidence-grounded reasoning and lead to incorrect verdicts. To address these challenges, we propose ReflectFact, a novel self-reflective agent framework for multi-hop fact verification. ReflectFact introduces three key tasks. Explicit Reasoning Path Planning builds an evidence-grounded reasoning path by resolving implicit entities, decomposing the claim into sub-questions, and integrating the verified facts into a verdict. Evidence-Drift Verification makes the agent re-answer by quoting the supporting evidence when a grounded answer merely echoes its parametric prior, thereby calibrating evidence deviation to ensure grounded comprehension. Reasoning Reflection Verification re-examines each reasoning step and regenerates it once an inconsistency is detected, correcting reasoning flaws such as location bias and replacement bias through a global task perspective. Subsequently, the agent aggregates validated reasoning chains to yield reliable verdicts. Extensive experiments on HOVER and EX-FEVER demonstrate that ReflectFact effectively remedies the comprehension and reasoning defects of existing methods, achieving state-of-the-art performance and respectively outperforming the strongest baseline by 3.32\% and 2.78\% on the two datasets.
Comments9 pages, 4 figures, 3 tables
