  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.
