让大模型模拟多人协作解题，验证了群体智慧的可行性。
Synergistic Simulations: Multi-Agent Problem Solving with Large Language Models
- 用大模型构建多智能体框架，模拟真实互动环境中的协作。
- 在公寓和编程任务中，智能体协同完成目标，表现优于单个智能体。
- 为未来多智能体系统设计提供可扩展的参考方案，适合研究协作算法者。
大型语言模型（LLMs）在促进多智能体系统发展方面展现出日益增强的能力，能够解析每个个体生成的思想与行为。在与现有世界交互方面，基于大模型的仿真环境互动也取得了显著进展。本文旨在将上述两个方向整合至单一仿真环境中，使多个智能体共同协作解决问题，模拟人类群体通常比个体更高效解决问题的现象。通过检验大模型是否展现人类协作的协同效应，有望推动大模型应用的发展。我们实现了两个仿真场景：一个包含两名室友的物理工作室公寓，另一个是智能体协作完成编程任务。提供了多智能体框架，分析了各场景下智能体的表现，并探讨了未来可能的扩展方向。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have increasingly demonstrated the ability to facilitate the development of multi-agent systems that allow the interpretation of thoughts and actions generated by each individual. Promising advancements have also been made in LLM-based interaction with existing worlds, particularly in interacting with simulated environments. This paper aims to integrate both aforementioned topics (agents & world interaction) into a single simulation where multiple agents can work together to solve a problem, modeling how groups of humans can often solve problems better than individuals. By showing whether LLMs demonstrate the synergy of human collaboration, it could lead to advancements in the applications of LLMs. We implemented two simulations: a physical studio apartment with two roommates, and another where agents collaborate to complete a programming task. We provide a multi-agent framework, discuss the performance of the agents in each simulation, and discuss potential future additions.
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