Using multiple agents with intentional information barriers prevents LLMs from confirming their own errors during fact-checking, letting smaller models match larger ones on reliability.
MARCH is a framework that reduces hallucinations in LLMs by using three specialized agents that work together with deliberate information separation. A Solver generates responses, a Proposer breaks them into verifiable claims, and a Checker validates claims without seeing the original output—preventing the verifier from copying the generator's mistakes.