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Sprint projectOct 26, 2026

Exploring a Pre-Registered Tripartite Evidence Framework for Auditing Multi-Agent AI Collusion

Jesi Martin Maglana · Team Orca

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Exploring a Pre-Registered Tripartite Evidence Framework for Auditing Multi-Agent AI Collusion

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Auditors get fooled by innocent parallel conduct ("shared priors") or harmless scheming ("collusion on paper") of LLM agents; this project tackles this evidentiary crisis by introducing a pre-registered, three-pillar framework that jointly evaluates conversational intent, regret-based coalition advantage, and causal action dependence. Evaluated across a standardized 60-trajectory benchmark combining Jira sprint allocations (Colosseum) and competitive duopoly pricing (ColludeBench), our split-freeze-test audit reveals that single-pillar detectors suffer from up to 100% false positive rates. By contrast, a cryptographically frozen Tripartite Decision Rule achieves a robust 1.000 F1 score (0.0% false alarms across both confounder types) on held-out test data, establishing a reliable standard of proof for multi-agent AI safety audits. However, this result should be treated with caution (factors described in the paper).

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Cite this project

@misc{maglana2026exploring,
  title = {{Exploring a Pre-Registered Tripartite Evidence Framework for Auditing Multi-Agent AI Collusion}},
  author = {Jesi Martin Maglana},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/exploring-a-preregistered-tripartite-evidence-framework-for-auditing-multiagent-ai-collusion-zv8i}},
  url = {https://apartresearch.com/sprints/projects/exploring-a-preregistered-tripartite-evidence-framework-for-auditing-multiagent-ai-collusion-zv8i}
}

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