A graph-neural-network-based algorithmic reasoner feeds map and planning information to an LLM through cross-attention, improving multi-agent path finding over LLM-only baselines.
Surynek, ``Problem compilation for multi-agent path finding: a survey.'' in IJCAI, 2022, pp
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Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding
A graph-neural-network-based algorithmic reasoner feeds map and planning information to an LLM through cross-attention, improving multi-agent path finding over LLM-only baselines.