Multi-agent Systems for Misinformation Lifecycle : Detection, Correction And Source Identification

TL;DR

This paper addresses the end-to-end misinformation lifecycle by proposing a modular multi-agent framework that partitions detection, correction, and source verification into five specialized agents. The architecture enables dynamic knowledge indexing, evidence-based retrieval, and provenance tracking to improve transparency, scalability, and robustness against evolving misinformation. The authors argue that separating concerns across agents with cross-agent audit trails yields more interpretable and adaptable systems than monolithic detectors. The framework is envisioned for deployment by government bodies, fact-checkers, and media platforms to monitor and correct misinformation with verifiable sources.

Abstract

The rapid proliferation of misinformation in digital media demands solutions that go beyond isolated Large Language Model(LLM) or AI Agent based detection methods. This paper introduces a novel multi-agent framework that covers the complete misinformation lifecycle: classification, detection, correction, and source verification to deliver more transparent and reliable outcomes. In contrast to single-agent or monolithic architectures, our approach employs five specialized agents: an Indexer agent for dynamically maintaining trusted repositories, a Classifier agent for labeling misinformation types, an Extractor agent for evidence based retrieval and ranking, a Corrector agent for generating fact-based correction and a Verification agent for validating outputs and tracking source credibility. Each agent can be individually evaluated and optimized, ensuring scalability and adaptability as new types of misinformation and data sources emerge. By decomposing the misinformation lifecycle into specialized agents - our framework enhances scalability, modularity, and explainability. This paper proposes a high-level system overview, agent design with emphasis on transparency, evidence-based outputs, and source provenance to support robust misinformation detection and correction at scale.