Evidence-Gated Adoption of LLM-Generated Graph Priors for Multi-Agent Reinforcement Learning

LLM-guided multi-agent reinforcement learning (MARL) increasingly uses coordination graphs as structural priors, yet lacks an evidence boundary between graph proposal and policy optimization. Across 36 conditions, 15 LLM graphs underperform a graph-free reference, while proposer confidence is weakly informative. We treat each proposed graph as a hypothesis whose adoption requires stage-specific evidence. FSGAC makes a pre-policy graph-admission decision from relative probe improvement, typed complexity, and propagation sensitivity; PCEVC audits retained edges using paired deletion and clipped DR-style estimation; ITSC regularizes cross-domain variance in audited type effects. FSGAC correctly classifies 33/36 conditions under grouped leave-one-scenario-out evaluation. PCEVC reports 3/176 harmful decisive accepted edges in a post-hoc MPE/SMACv2 subtotal. The full pipeline has 8.2% maximum reported benchmark-shift degradation. Across MPE, SMACv2, and MDO-Sim, the pipeline ranks first against 14 baselines, and the graph gate transfers to a matched Llama proposer. These results support an evidence-based adoption protocol for graph-prior MARL, with separate graph, edge, and transfer checks.