Remember and Reweight: Enhancing Multi-Agent Debate with Experience Memory and Confidence Estimation

The paper introduces R$^2$-MAD, a framework that enhances multi-agent debate by giving agents an experience memory from past debates. It uses a debate-state-aware retrieval policy to adjust concept priors based on current consensus, and derives confidence weights from retrieved experiences to modulate peer influence. Experiments demonstrate consistent improvements over existing single-agent and MAD baselines.

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• llms
• agents
• benchmarks