# Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yet arXiv: https://arxiv.org/abs/2401.03630 ยท HTML: https://mlsanity.com/paper/2401.03630/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. - Builds on 27 of its 41 references that are in the map. - First posted on arXiv 8 Jan 2024. ## Placed in - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 27 of 27) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023) - [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023) - [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022) - [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023) - [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023) - [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023) - [Complexity-Based Prompting for Multi-Step Reasoning](https://mlsanity.com/paper/2210.00720/) (2022) - [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023) - [A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration](https://mlsanity.com/paper/2310.02170/) (2023) - [Building Cooperative Embodied Agents Modularly with Large Language Models](https://mlsanity.com/paper/2307.02485/) (2023) - [Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks](https://mlsanity.com/paper/1906.08291/) (2019) - [The Capacity for Moral Self-Correction in Large Language Models](https://mlsanity.com/paper/2302.07459/) (2023) - [A Language Agent for Autonomous Driving](https://mlsanity.com/paper/2311.10813/) (2023) - [Theory of Mind for Multi-Agent Collaboration via Large Language Models](https://mlsanity.com/paper/2310.10701/) (2023) - [Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data](https://mlsanity.com/paper/2302.12822/) (2023) - [Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?](https://mlsanity.com/paper/2309.15943/) (2023) - [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018) - [Language Conditioned Traffic Generation](https://mlsanity.com/paper/2307.07947/) (2023) - [LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models](https://mlsanity.com/paper/2310.03903/) (2023) - [PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelong](https://mlsanity.com/paper/2010.08184/) (2020) - [Prompt a Robot to Walk with Large Language Models](https://mlsanity.com/paper/2309.09969/) (2023) - [Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning](https://mlsanity.com/paper/2310.01207/) (2023) - [Dialogue Games for Benchmarking Language Understanding: Motivation, Taxonomy, Strategy](https://mlsanity.com/paper/2304.07007/) (2023) - [DDM: Fast Near-Optimal Multi-Robot Path Planning using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics](https://mlsanity.com/paper/1904.02598/) (2019) ## Built on by (most cited first, 22 of 22) - [Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces](https://mlsanity.com/paper/2410.09918/) (2024) - [Multi-agent Embodied AI: Advances and Future Directions](https://mlsanity.com/paper/2505.05108/) (2025) - [Large Language Models for Multi-Robot Systems: A Survey](https://mlsanity.com/paper/2502.03814/) (2025) - [RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning](https://mlsanity.com/paper/2510.02240/) (2025) - [Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction](https://mlsanity.com/paper/2402.15368/) (2024) - [RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents](https://mlsanity.com/paper/2406.11132/) (2024) - [YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2410.03997/) (2024) - [LSPO: Length-aware Dynamic Sampling for Policy Optimization in LLM Reasoning](https://mlsanity.com/paper/2510.01459/) (2025) - [SRMT: Shared Memory for Multi-agent Lifelong Pathfinding](https://mlsanity.com/paper/2501.13200/) (2025) - [Iterative Deepening Sampling as Efficient Test-Time Scaling](https://mlsanity.com/paper/2502.05449/) (2025) - [Challenges Faced by Large Language Models in Solving Multi-Agent Flocking](https://mlsanity.com/paper/2404.04752/) (2024) - [Look Further Ahead: Testing the Limits of GPT-4 in Path Planning](https://mlsanity.com/paper/2406.12000/) (2024) - [Can Large Language Models Solve Robot Routing?](https://mlsanity.com/paper/2403.10795/) (2024) - [Flaming-hot Initiation with Regular Execution Sampling for Large Language Models](https://mlsanity.com/paper/2410.21236/) (2024) - [Assessing and Enhancing the Robustness of LLM-based Multi-Agent Systems Through Chaos Engineering](https://mlsanity.com/paper/2505.03096/) (2025) - [GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models](https://mlsanity.com/paper/2503.23875/) (2025) - [Where Paths Collide: A Comprehensive Survey of Classic and Learning-Based Multi-Agent Pathfinding](https://mlsanity.com/paper/2505.19219/) (2025) - [LLMDR: LLM-Driven Deadlock Detection and Resolution in Multi-Agent Pathfinding](https://mlsanity.com/paper/2503.00717/) (2025) - [Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding](https://mlsanity.com/paper/2508.17971/) (2025) - [Multi-Agent Path Finding via Offline RL and LLM Collaboration](https://mlsanity.com/paper/2509.22130/) (2025) - [ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Models](https://mlsanity.com/paper/2603.19515/) (2026) - [HEART: Coordination of Heterogeneous Expert Agents for Physically Grounded Robotic Task Planning](https://mlsanity.com/paper/2606.25404/) (2026) ## Abstract (arXiv) 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 the performance of solving MAPF with LLMs. We first show the motivating success on an empty room map without obstacles, then the failure to plan on the harder room map and maze map of the standard MAPF benchmark. We present our position on why directly solving MAPF with LLMs has not been successful yet, and we use various experiments to support our hypothesis. Based on our results, we discussed how researchers with different backgrounds could help with this problem from different perspectives.