Multi-Agent Systems for the Misinformation Lifecycle // Aditya Gautam

The rapid spread of digital misinformation requires solutions that address the entire lifecycle, moving beyond single-LLM limitations. This talk, based on the author’s ICWSM research paper, offers a practitioner's guide to a novel, five-agent system—Classifier, Indexer, Extractor, Corrector, and Verification—designed for maximum scalability, modularity, and explainability. This paper aims at automating the working of fact-checkers, which is traditionally done through a team of experts, saving millions and increasing efficiency with a human-in-the-loop system. We will get the details for each specialized agent, detailing crucial elements like model sizing and fine-tuning—for example, matching small, fine-tuned encoder models for the Classifier's high-confidence multi-class labeling against the need for a strong reasoning LLM in the Corrector Agent. Topics include building an efficient Indexer Agent and reranking with retrieval through hybrid keyword and vector embeddings, enabling the Corrector Agent to use external search APIs for cross-validation, and the function of the Verification Agent as the final quality check for high precision.. The talk concludes by covering agent coordination protocols, cost, holistic evaluation, offline evaluation and online A/B testing and post-deployment metrics.