arXiv:2601. 05746v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of Large Language Model-based Multi-Agent Systems (MAS), which excel at collaborative decision-making and complex problem-solving.
By Zhenghao Li, Zhi Zheng, Wei Chen, Jielun Zhao, Yong Chen, Tong Xu, Enhong Chen
arXiv:2604. 09679v2 Announce Type: replace-cross Abstract: Multi-Agent Debate (MAD) is a collaborative framework in which multiple agents iteratively refine solutions through the generation of reasoning and alternating critique cycles.
By Yiqing Liu, Hantao Yao, Wu Liu, Allen He, Yongdong Zhang
arXiv:2607. 26212v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems.
By Quim Motger, Marc Oriol, Jordi Marco, Xavier Franch
Meta-Moderator is a learnable framework that treats moderation as a meta‑cognitive process, monitoring debate utility, controlling deliberation, and adjudicating final answers. It is trained independently of the debaters through outcome‑driven policy optimization, allowing dynamic regulation of debate rather than relying on fixed budgets or untrained judges. Across five benchmarks, Meta‑Moderator outperforms common decision layers, transfers across tasks and system configurations, and selectively allocates debate to reduce mis‑aggregation after informative hypotheses appear.
By Wentao Hu, Zhuoyue Wan, Jinhao Shen, Chen Jason Zhang, Xiaoyong Wei, Qing Li
arXiv:2606. 13197v1 Announce Type: new Abstract: Multi-agent debate (MAD) can improve large language model reasoning, but fixed debate pipelines often waste computation and can amplify correlated errors among similar agents.
By Fuqiang Niu, Bowen Zhang
arXiv:2608. 01463v2 Announce Type: replace Abstract: Multi-agent debate commonly exchanges complete rationales even when disagreements concern only a few intermediate claims.
By Weijun Gao, Xiang Ding, Haoyang Liu, Tiancheng Xing