Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet

With the explosive influence caused by the success of large language models (LLM) like ChatGPT and GPT-4, there has been an extensive amount of recent work showing that foundation models can be used to solve a large variety of tasks. However, there is very limited work that shares insights on multi-agent planning. Multi-agent planning is different from other domains by combining the difficulty of multi-agent coordination and planning, and making it hard to leverage external tools to facilitate the reasoning needed. In this paper, we focus on the problem of multi-agent path finding (MAPF), which is also known as multi-robot route planning, and study how to solve MAPF with LLMs. We first show the motivating success on an empty room map without obstacles, then the failure to plan on a slightly harder room map. We present our hypothesis of why directly solving MAPF with LLMs has not been successful yet, and we use various experiments to support our hypothesis.