Structured Evidence Filtering and Consensus Reasoning for Multi-Agent Fact-Checking

Large-scale retrieval-augmented fact-checking often suffers from noisy evidence and unreliable multi-agent reasoning aggregation. To address these challenges, we propose a Structured Evidence Filtering-based Multi-Agent Consensus Framework ($\text{SEF-MAC}$), consisting of two core modules. The first is a Structured Evidence Filtering (SEF) module, which leverages domain knowledge to remove irrelevant evidence and maintain semantic consistency with the target claim. The second is a Challenge-and-Validation Reasoning Approach, which treats minority dissent as a signal to activate iterative cross-examination, allowing agents to correct logical defects through evidence-grounded deliberation. Experiments on three benchmarks—CheckThat-T3, CHEF, and SciFact—show that SEF-MAC consistently outperforms competitive baselines, with Micro-F1 gains of up to 3.72 percentage points and Macro-F1 gains of up to 3.00 percentage points. Further ablation and analysis suggest that evidence filtering and consensus reasoning make complementary contributions to improve fact-checking accuracy across different evidence settings.