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： 在大型语言模型驱动的智能体方面的开创性进展突显了多智能体协作的设计模式，表明集体智能可以超越每个个体的能力。 受神经缩放定律的启发，该定律认为增加神经元会导致涌现能力，本研究探讨了类似的原理是否适用于增加多智能体协作中的智能体数量。 技术上，我们提出了多智能体协作网络（MacNet），它利用有向无环图来组织智能体，并通过拓扑排序简化它们的交互推理，解决方案来自它们的对话。 大量实验表明，MacNet始终优于基线模型，在各种网络拓扑中实现了有效的智能体协作，并支持超过一千个智能体之间的合作。 值得注意的是，我们观察到一种小世界协作现象，其中拓扑结构类似于小世界属性的表现出更优的性能。 此外，我们发现了一种协作缩放定律，表明随着智能体数量的增加，归一化解决方案质量遵循逻辑增长模式，协作涌现发生得比之前观察到的神经涌现要早得多。 代码和数据将在 https://github.com/OpenBMB/ChatDev 提供。 显示英文摘要

摘要： Pioneering advancements in large language model-powered agents have underscored the design pattern of multi-agent collaboration, demonstrating that collective intelligence can surpass the capabilities of each individual. Inspired by the neural scaling law, which posits that increasing neurons leads to emergent abilities, this study investigates whether a similar principle applies to increasing agents in multi-agent collaboration. Technically, we propose multi-agent collaboration networks (MacNet), which utilize directed acyclic graphs to organize agents and streamline their interactive reasoning via topological ordering, with solutions derived from their dialogues. Extensive experiments show that MacNet consistently outperforms baseline models, enabling effective agent collaboration across various network topologies and supporting cooperation among more than a thousand agents. Notably, we observed a small-world collaboration phenomenon, where topologies resembling small-world properties achieved superior performance. Additionally, we identified a collaborative scaling law, indicating that normalized solution quality follows a logistic growth pattern as scaling agents, with collaborative emergence occurring much earlier than previously observed instances of neural emergence. The code and data will be available at https://github.com/OpenBMB/ChatDev.