DEDUCE: Robust Fact-Checking under Knowledge Poisoning via Claim Decomposition and Evidence Reweighting

Automated fact-checking systems traditionally operate under the “clean-knowledge” assumption: the premise that retrieved evidence is inherently reliable. However, in open-domain environments, knowledge bases may contain fabricated or misleading content, causing substantial degradation when misinformation is retrieved as evidence. We formalize the task of Fact-Checking under Contamination (FCC) and propose DEDUCE, a robust verification framework designed for contaminated knowledge environments. DEDUCE combines claim decomposition, claim-conditioned stance aggregation, and dynamic passage-level reliability estimation in a closed-loop retrieval process, where learned reliability signals are fed back into subsequent retrieval through reranking. To rigorously evaluate this setting, we introduce a controlled stress test with topically aligned, adversarially generated distractors and complement it with naturally occurring misinformation. Across seven benchmarks and four open-source LLM backbones, contamination substantially degrades standard fact-checking pipelines, while DEDUCE consistently recovers a significant portion of the lost performance. Additional comparisons on HoVer and EX-FEVER show that DEDUCE also outperforms purpose-built poisoning defenses, while further stress tests reveal that coordinated and reputation-based misinformation remain challenging when misleading evidence becomes highly consistent or inherits previously accumulated reliability. These results demonstrate the importance of explicitly modeling evidence reliability and retrieval feedback for robust fact checking under contaminated knowledge.