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Sprint projectOct 26, 2026Atlanta

When Cooperation Looks Like Collusion: The Limits of Counterfactual Auditing in Multi-Agent AI Systems

Aarav Patel

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

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Report: When Cooperation Looks Like Collusion: The Limits of Counterfactual Auditing in Multi-Agent AI Systems

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Counterfactual Collusion Certificates (CCC) examines whether the effects of communication between AI agents can reliably distinguish collusion from legitimate cooperation. The project uses controlled simulations and message-removal experiments to test cases where permitted and prohibited behavior produce identical outcomes.

The results show that relying on communication effects alone can lead to false accusations. Additional evidence, such as changes in task costs or authorization records, can distinguish certain cases, although the broader approach requires further testing. .

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Cite this project

@misc{patel2026cooperation,
  title = {{When Cooperation Looks Like Collusion: The Limits of Counterfactual Auditing in Multi-Agent AI Systems}},
  author = {Aarav Patel},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/when-cooperation-looks-like-collusion-the-limits-of-counterfactual-auditing-in-multiagent-ai-systems-govi}},
  url = {https://apartresearch.com/sprints/projects/when-cooperation-looks-like-collusion-the-limits-of-counterfactual-auditing-in-multiagent-ai-systems-govi}
}

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