arXiv:2604.19005v2 Announce Type: replace 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 evid
Advances in multi-agent AI and the growing prevalence of information manipulation necessitate more sophisticated fact-verification methods that go beyond explicit falsehoods.
Detecting 'half-truths' or misleading omissions is a critical challenge for AI systems, impacting trust, information integrity, and the efficacy of automated decision-making.
Fact verification systems can move beyond simple truth checks to a more nuanced understanding of context and intent, potentially reducing the vulnerability of AI to sophisticated deceptions.
- · AI safety researchers
- · Digital journalism
- · Social media platforms
- · AI-powered content moderation
- · Producers of disinformation
- · Bots generating misleading content
AI's ability to discern misleading information improves, making it a more reliable tool for processing complex data.
This improved reliability could lead to wider adoption of AI in sensitive fields like intelligence analysis and policy recommendations.
The arms race between AI for detection and AI for generation of nuanced disinformation will intensify, pushing both capabilities to new levels.
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Read at arXiv cs.CL