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

Collections of interacting AI agents can form coalitions, creating emergent group-level organization that is critical for AI safety and alignment. However, observing agent behavior alone is often insufficient to distinguish genuine informational coupling from spurious similarity, as consequential coalitions may form at the level of internal representations before any overt behavioral change is apparent. Here, we introduce a practical method for detecting coalition structure from the internal neu

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ethics.ai (4 May 2026), “Hidden Coalitions in Multi-Agent AI: A Spectral Diagnostic from Internal Representations,” evidence record 5004, https://ethics.ai/record/5004 (originally published by arXiv).

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