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    <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.</abstract>
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%0 Conference Proceedings
%T Meta-Moderator: Empowering Multi-Agent Debate with Meta-Cognition
%A Hu, Wentao
%A Wan, Zhuoyue
%A Shen, Jinhao
%A Zhang, Chen Jason
%A Wei, Xiao-Yong
%A Qing, Li
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hu-etal-2026-meta
%X 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.
%U https://aclanthology.org/2026.findings-emnlp.1250/
%P 26435-26451

Markdown (Informal)

[Meta-Moderator: Empowering Multi-Agent Debate with Meta-Cognition](https://aclanthology.org/2026.findings-emnlp.1250/) (Hu et al., Findings 2026)

• Meta-Moderator: Empowering Multi-Agent Debate with Meta-Cognition (Hu et al., Findings 2026)

ACL

• Wentao Hu, Zhuoyue Wan, Jinhao Shen, Chen Jason Zhang, Xiao-Yong Wei, and Li Qing. 2026. Meta-Moderator: Empowering Multi-Agent Debate with Meta-Cognition. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 26435–26451, Budapest, Hungary. Association for Computational Linguistics.