ReflectFact: Self-Reflective Agents for Improving Comprehension and Reasoning in Multi-Hop Fact Verification

2026-08-13

AI Summary

Existing multi-hop fact verification methods often suffer from reasoning deviations or erroneous conclusions due to a lack of global objective awareness and conflicts between parametric knowledge and retrieved evidence. To address these issues, this work proposes ReflectFact, a self-reflective agent framework that introduces a three-stage mechanism—explicit reasoning path planning, evidence drift verification, and reflective reasoning validation—to automatically detect and correct positional and substitution biases within reasoning chains for the first time. By integrating multi-hop question decomposition, evidence-grounded re-answering, step-wise consistency checking, and verification chain aggregation, ReflectFact effectively mitigates the misalignment between subtasks and the overarching verification goal. The method achieves state-of-the-art performance, surpassing the strongest baseline by 3.32% on HOVER and 2.78% on EX-FEVER.

Abstract

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.

Problem

• multi-hop fact verification
• evidence-grounded reasoning
• reasoning deviation
• parametric knowledge conflict
• misinformation

Innovation

• self-reflective agents
• multi-hop fact verification
• evidence-grounded reasoning
• reasoning path planning
• bias correction