arXiv AI
Beyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate
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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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When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?
arXiv:2510. 20963v2 Announce Type: replace Abstract: Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervision to superhuman LLMs.
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Where Reasoning Diverges: Localized Multi-Agent Debate for Multi-Hop Question Answering
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Demystifying Multi-Agent Debate: The Role of Confidence and Diversity
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Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems
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