The authors introduce a method to detect hidden coalitions among AI agents by analyzing their internal neural representations, not just their behavior. They compute pairwise mutual information between agents' hidden states, build a graph, and use spectral partitioning (an eigenvalue-based clustering method) to find the strongest coalition boundary. Validation in multi-agent RL and LLM prompt-based teams shows the method recovers programmed coalition structures, rejects false positives from mere behavioral coordination, and reveals representational hierarchies (e.g., explicit team labels dominate conflicting interaction cues).
Main takeaways:
- Coalitions can form at the level of internal representations before behavior changes, so observing actions alone misses early warning signs.
- The method builds a mutual-information graph from hidden states and applies spectral partitioning to find coalition boundaries.
- Successfully recovers hierarchical and dynamic coalition structures in multi-agent RL; correctly rejects behavioral coordination without shared information.
- In LLM prompt-based experiments, identifies coalitions from descriptive prompts and tracks dynamic reassignments.
- Scalar cross-agent mutual information can't distinguish subgroup structure; spectral partitioning reveals the organization.