MAD-Sherlock: Multi-Agent Debate for Visual Misinformation Detection

Published: 10 Jun 2025, Last Modified: 29 Jun 2025CFAgentic @ ICML'25 OralEveryoneRevisionsBibTeXCC BY 4.0
Keywords: multi-agent debate, agents, misinformation
TL;DR: multi-agent debate for misinformation detection
Abstract: One of the most challenging forms of misinformation involves pairing images with misleading text to create false narratives. Existing AI-driven detection systems often require domain-specific finetuning, limiting generalizability, and offer little insight into their decisions, hindering trust and adoption. We introduce MAD-Sherlock, a multi-agent debate system for out-of-context mis-information detection. MAD-Sherlock frames detection as a multi-agent debate, reflecting the diverse and conflicting discourse found online. Multimodal agents collaborate to assess contextual consistency and retrieve external information to support cross-context reasoning. Our framework is domain- and time-agnostic—requiring no finetuning–yet achieves state-of-the-art accuracy with in-depth explanations. Evaluated on NewsCLIPpings, VERITE, and MMFakeBench, it outperforms prior methods by 2%, 3%, and 5%, respectively. Ablation and user studies show that the debate and resultant explanations significantly improve detection performance and improves trust for both experts and non-experts, positioning MAD-Sherlock as a robust tool for autonomous citizen intelligence.
Submission Number: 30
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