Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915462020857856 |
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| author | Feng, Pu Wang, Size Cao, Yuhong Liang, Junkang Shi, Rongye Wu, Wenjun |
| author_facet | Feng, Pu Wang, Size Cao, Yuhong Liang, Junkang Shi, Rongye Wu, Wenjun |
| contents | The development and application of large language models (LLM) have demonstrated that foundational models can be utilized to solve a wide array of tasks. However, their performance in multi-agent path finding (MAPF) tasks has been less than satisfactory, with only a few studies exploring this area. MAPF is a complex problem requiring both planning and multi-agent coordination. To improve the performance of LLM in MAPF tasks, we propose a novel framework, LLM-NAR, which leverages neural algorithmic reasoners (NAR) to inform LLM for MAPF. LLM-NAR consists of three key components: an LLM for MAPF, a pre-trained graph neural network-based NAR, and a cross-attention mechanism. This is the first work to propose using a neural algorithmic reasoner to integrate GNNs with the map information for MAPF, thereby guiding LLM to achieve superior performance. LLM-NAR can be easily adapted to various LLM models. Both simulation and real-world experiments demonstrate that our method significantly outperforms existing LLM-based approaches in solving MAPF problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17971 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding Feng, Pu Wang, Size Cao, Yuhong Liang, Junkang Shi, Rongye Wu, Wenjun Artificial Intelligence Robotics The development and application of large language models (LLM) have demonstrated that foundational models can be utilized to solve a wide array of tasks. However, their performance in multi-agent path finding (MAPF) tasks has been less than satisfactory, with only a few studies exploring this area. MAPF is a complex problem requiring both planning and multi-agent coordination. To improve the performance of LLM in MAPF tasks, we propose a novel framework, LLM-NAR, which leverages neural algorithmic reasoners (NAR) to inform LLM for MAPF. LLM-NAR consists of three key components: an LLM for MAPF, a pre-trained graph neural network-based NAR, and a cross-attention mechanism. This is the first work to propose using a neural algorithmic reasoner to integrate GNNs with the map information for MAPF, thereby guiding LLM to achieve superior performance. LLM-NAR can be easily adapted to various LLM models. Both simulation and real-world experiments demonstrate that our method significantly outperforms existing LLM-based approaches in solving MAPF problems. |
| title | Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2508.17971 |