HiveMind 社会：通过基础模型集群的多智能体优化释放集体智能潜力
The Society of HiveMind: Multi-Agent Optimization of Foundation Model Swarms to Unlock the Potential of Collective Intelligence
AI总结：
本文提出 Society of HiveMind（SOHM）框架，依据进化理论协调多个基础模型交互；实验显示其显著提升逻辑推理任务，但对现实知识任务收益有限，证明多样化模型可形成自我改进的人工集群智能。
摘要：
多智能体系统解决了人工智能（AI）基础模型在可访问性和可扩展性方面的问题，这些模型通常以大型语言模型为代表。我们开发了一个框架——“Society of HiveMind”（SOHM，HiveMind 社会）——用于协调多个 AI 基础模型之间的交互，该框架遵循现代进化理论，模仿自然界中观察到的动物集群行为。一方面，我们发现 SOHM 在主要需要现实世界知识的任务上带来的收益微乎其微。另一方面，我们注意到其在需要密集逻辑推理的任务上有显著提升，这表明与单个智能体相比，多智能体系统能够增强集体的推理能力。我们的研究结果证明，将大量多样化的 AI 基础模型结合起来，形成能够通过与给定环境交互实现自我改进的人工集群智能，具有巨大潜力。
英文摘要：
Multi-agent systems address issues of accessibility and scalability of artificial intelligence (AI) foundation models, which are often represented by large language models. We develop a framework - the "Society of HiveMind" (SOHM) - that orchestrates the interaction between multiple AI foundation models, imitating the observed behavior of animal swarms in nature by following modern evolutionary theories. On the one hand, we find that the SOHM provides a negligible benefit on tasks that mainly require real-world knowledge. On the other hand, we remark a significant improvement on tasks that require intensive logical reasoning, indicating that multi-agent systems are capable of increasing the reasoning capabilities of the collective compared to the individual agents. Our findings demonstrate the potential of combining a multitude of diverse AI foundation models to form an artificial swarm intelligence capable of self-improvement through interactions with a given environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
