Multi-Agent Fact-Checking: How Debating LLMs Catch Hallucinations in Real Time

This addresses a critical problem in LLM applications: when multiple models answer the same question, they often reinforce each other's hallucinations rather than correct them. By implementing a dedicated fact-checker agent within a structured debate framework, the system prevents confident false claims from propagating through downstream pipelines. This architectural approach, inspired by Mixture-of-Experts models, offers a practical solution for building more reliable AI systems that can self-correct mid-reasoning.