Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation. This is observed in the natural world and has applications in robotics, including natural disaster search and rescue, wild animal tracking, and perimeter surveillance and patrol. Recently, large language models (LLMs) have displayed an impressive ability to solve various collaboration tasks as individual decision-makers. Solving multi-agent flocking with LLMs would demonstrate their usefulness in situations requiring spatial and decentralized decision-making. Yet, when LLM-powered agents are tasked with implementing multi-agent flocking, they fall short of the desired behavior. After extensive testing, we find that agents with LLMs as individual decision-makers typically opt to converge on the average of their initial positions or diverge from each other. After breaking the problem down, we discover that LLMs cannot understand maintaining a shape or keeping a distance in a meaningful way. Solving multi-agent flocking with LLMs would enhance their ability to understand collaborative spatial reasoning and lay a foundation for addressing more complex multi-agent tasks. This paper discusses the challenges LLMs face in multi-agent flocking and suggests areas for future improvement and research.

翻译：群集是一种行为，指系统中多个智能体试图彼此靠近，同时避免碰撞并保持期望的队形。这种行为在自然界中可见，并在机器人领域有应用，包括自然灾害搜救、野生动物追踪以及周边监视和巡逻。近年来，大型语言模型（LLMs）展现出作为独立决策者解决各种协作任务的出色能力。用LLMs解决多智能体群集问题将证明其在需要空间和分散决策的场景中的实用性。然而，当由LLMs驱动的智能体被要求实现多智能体群集时，它们难以达到预期行为。经过广泛测试，我们发现以LLMs作为独立决策者的智能体通常倾向于收敛到初始位置的平均值或彼此分散。通过分解问题，我们发现LLMs无法以有意义的方式理解保持队形或维持距离。用LLMs解决多智能体群集问题将提升其理解协作空间推理的能力，为处理更复杂的多智能体任务奠定基础。本文讨论了LLMs在多智能体群集中面临的挑战，并为未来的改进和研究方向提出了建议。