让AI团队动态选角色，比固定分配效果好74.8%。
Dynamic Role Assignment for Multi-Agent Debate
- 先搞一场元辩论，筛选最适合的角色人选
- 在不同任务中，性能比随机分配高29.7%
- 适合需要多模型协作的复杂问题求解
多智能体大语言模型（LLM）和视觉语言模型（VLM）辩论系统通过专业化角色解决复杂问题，但现有方法未根据模型能力分配角色。本文提出动态角色分配框架，通过元辩论预先选择合适代理：第一阶段为提案，候选者提供角色定制化论证；第二阶段为同行评审，基于数据与角色特定标准打分，选出最佳人选。在LLM问题求解基准上评估，该方法在现有辩论系统基础上，相比统一分配（所有角色用同一模型）提升高达74.8%，相比随机分配（不考虑适配性）提升最高达29.7%，具体效果依任务和分配策略而定。本工作确立了新型多智能体系统设计范式，从静态部署转向动态、能力感知的角色选择。
原文摘要 · Abstract (English)
Multi-agent large language model (LLM) and vision-language model (VLM) debate systems employ specialized roles for complex problem-solving, yet model specializations are not leveraged to decide which model should fill which role. We propose dynamic role assignment, a framework that runs a Meta-Debate to select suitable agents before the actual debate. The meta-debate has two stages: (1) proposal, where candidates provide role-tailored arguments, and (2) peer review, where proposals are scored with data and role-specific criteria to choose the best agent for each position. We evaluate our method on LLM problem solving benchmarks. Applied on top of existing debate systems, our approach consistently outperforms uniform assignments (filling all roles with the same model) by up to 74.8% and random assignments (assigning models to roles without considering their suitability) by up to 29.7%, depending on the task and the specific assignment. This work establishes a new paradigm for multi-agent system design, shifting from static agent deployment to dynamic and capability-aware selection.
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