Do LLM-derived graph priors improve multi-agent coordination?
Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly in multi-domain operations (MDO). A central challenge in cooperative MARL is determining how agents should coordinate: existing approaches must either hand-specify graph topology, rely on proximity-based heuristics, or learn structure entirely from environment interaction; all of which are brittle, semantically uninformed, or data-intensive. We investigate whether large language models (LLMs) can generate useful coordination graph priors for MARL by using minimal natural language descriptions of agent observations to infer latent coordinati...
| Authors | Nikunj Gupta, Rajgopal Kannan, Viktor Prasanna |
|---|---|
| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 19 April 2026 |
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