Hidden Coalitions in Multi-Agent AI: A Spectral Diagnostic from Internal Representations

This paper introduces a novel method to detect hidden coalition structures within multi-agent AI systems by analyzing their internal neural representations. It constructs a pairwise mutual-information graph from hidden states and applies spectral partitioning to identify coalition boundaries, validated in reinforcement learning environments.