Claim Verification with Adversarial Reasoning and Planning

The scale and speed of digital communication demands robust automated claim verification systems that handle complex, multi-hop reasoning. Existing approaches have critical limitations: single-agent systems exhibit confirmation bias, while conventional multi-agent frameworks rely on homogeneous agents that exhibit groupthink, limiting critical evaluation. We present CARP (Claim Verification with Adversarial Reasoning and Planning), a novel multi-agent claim verification framework that organizes heterogeneous agents powered by multiple different language models competing as support and refutation teams. This adversarial structure forces comprehensive evaluation from both perspectives while mitigating confirmation bias and groupthink. Our framework incorporates systematic claim decomposition, strategic verification planning, and multi-hop knowledge retrieval to handle complex reasoning tasks. We evaluate CARP on two claim verification datasets–HOVER and FEVEROUS–where it demonstrates significant improvements in verification accuracy compared to existing single-agent and homogeneous multi-agent approaches, particularly for complex claims requiring multi-hop reasoning and evidence synthesis. Ablation studies confirm that both adversarial evaluation model and multihop knowledge retrieval contribute substantially to performance, with benefits scaling with reasoning complexity.