标题： 基于大型语言模型的多智能体协作扩展 显示英文标题

标题： Scaling Large Language Model-based Multi-Agent Collaboration

摘要： 最近在大型语言模型驱动的自主代理方面的突破表明，通过集体推理，多代理协作通常超越每个个体。 受神经尺度定律的启发——增加神经元可以提高性能，本研究探讨了持续添加协作代理是否能产生类似的好处。 技术上，我们利用有向无环图将代理组织成一个多代理协作网络（MacNet），在其上进行拓扑协调的交互推理以实现自主任务解决。 广泛的评估显示，它有效地支持超过一千个代理之间的协作，不规则的拓扑结构优于规则的拓扑结构。 我们还发现了一个协作尺度定律——随着代理规模的扩大，整体性能遵循逻辑增长模式，协作出现早于传统的神经出现。 我们推测这可能是因为扩展代理在交互反思和精炼过程中促进了多维考虑，从而产生了更全面的成果。 代码可在 https://github.com/OpenBMB/ChatDev/tree/macnet 获取。 显示英文摘要

摘要： Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning. Inspired by the neural scaling law--increasing neurons enhances performance, this study explores whether the continuous addition of collaborative agents can yield similar benefits. Technically, we utilize directed acyclic graphs to organize agents into a multi-agent collaboration network (MacNet), upon which their interactive reasoning is topologically orchestrated for autonomous task solving. Extensive evaluations reveal that it effectively supports collaboration among over a thousand agents, with irregular topologies outperforming regular ones. We also identify a collaborative scaling law--the overall performance follows a logistic growth pattern as agents scale, with collaborative emergence occurring earlier than traditional neural emergence. We speculate this may be because scaling agents catalyzes their multidimensional considerations during interactive reflection and refinement, thereby producing more comprehensive artifacts. The code is available at https://github.com/OpenBMB/ChatDev/tree/macnet.