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. Researchers have further investigated Multi-Agent Debate (MAD) frameworks, which enhance the reasoning and collaboration capabilities of MAS through information exchange and debate among multiple agents. However, existing approaches often rely on unguided initialization, causing agents to adopt identical reasoning paths that lead to the same errors. As a result, effective debate among agents is hindered, and the final outcome frequently degenerates into simple majority voting. To solve the above problem, we introduce Dynamic Multi-Agent Debate (DynaDebate), which enhances the effectiveness of multi-agent debate through three key mechanisms: (1) Dynamic Path Generation and Allocation, which employs a dedicated Path Generation Agent to generate diverse and logical solution paths with adaptive redundancy; (2) Process-Centric Debate, which shifts the focus from surface-level outcome voting to rigorous step-by-step logic critique to ensure process correctness; (3) A Trigger-Based Verification Agent, which is activated upon disagreement and uses external tools to objectively resolve deadlocks. Experiments show that DynaDebate achieves superior or highly competitive performance across the majority of benchmarks\footnote{The code is at https://github.com/nwpuLee2021/brianstorm.}.

翻译：近年来，基于大语言模型的多智能体系统（MAS）发展迅速，在协同决策和复杂问题求解方面表现卓越。研究者进一步提出了多智能体辩论（MAD）框架，通过多智能体间的信息交互与辩论增强MAS的推理与协作能力。然而现有方法常采用无引导初始化，导致智能体遵循相同的推理路径产生相同错误，既阻碍了有效辩论，又使最终结果退化为简单多数投票。为此，我们提出动态多智能体辩论（DynaDebate），通过三种关键机制提升多智能体辩论效果：（1）动态路径生成与分配机制——采用专用路径生成智能体生成具有逻辑多样性且含自适应冗余的解题路径；（2）过程中心辩论机制——将焦点从表层结果投票转向严谨的逐步逻辑校验以确保过程正确性；（3）触发式验证智能体——在出现分歧时激活，借助外部工具客观化解僵局。实验表明，DynaDebate在多数基准测试中均达到优越或极具竞争力的性能表现\footnote{代码开源地址：https://github.com/nwpuLee2021/brianstorm}。