Year: 2026
Type: conference-paper
Abstract: Half-truths, claims that are factually correct yet misleading due to omitted context, remain a blind spot for fact verification systems focused on explicit falsehoods.Addressing such omission-based manipulation requires reasoning not only about what is said, but also about what is left unsaid.We propose RADAR, a role-anchored multi-agent debate framework for omission-aware fact verification under realistic, noisy retrieval.RADAR assigns complementary roles to a Politician and a Scientist, who reason adversarially over shared retrieved evidence, moderated by a neutral Judge.A dual-threshold early termination controller adaptively decides when sufficient reasoning has been reached to issue a verdict.Experiments show that RADAR consistently outperforms strong single-and multi-agent baselines across datasets and backbones, improving omission detection accuracy while reducing reasoning cost.These results demonstrate that role-anchored, retrieval-grounded debate with adaptive control is an effective and scalable framework for uncovering missing context in fact verification.
Institution: National University of Singapore
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
Award: G1795493338
OpenAlex ID: W7155207751