Meta-Moderator: Empowering Multi-Agent Debate with Meta-Cognition

Abstract:Multi-agent debate can improve large language model reasoning by eliciting diverse hypotheses and critiques, yet its performance is often constrained by weak moderation. Common pipelines rely on fixed budgets, agreement-based stopping, or untrained judges, leading to redundant deliberation and unreliable evidence aggregation. We cast moderation as a meta-cognitive process, monitoring debate utility, controlling deliberation, and adjudicating a final answer, and introduce Meta-Moderator, a learnable framework that dynamically regulates debate and decides when to finalize an answer. Meta-Moderator is trained independently of the debaters via outcome-driven policy optimization, making debate regulation an explicit capability rather than an incidental effect of prompting. Across five benchmarks, Meta-Moderator outperforms widely used decision layers and transfers across tasks and system configurations. Further analyses show that it allocates debate more selectively and reduces mis-aggregation after informative hypotheses appear.

Computation and Language (cs.CL)

arXiv:2608.23029 [cs.CL]

(or arXiv:2608.23029v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.23029 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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