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Sprint projectOct 26, 2026

Monoculture False Alarms: Collusion Detectors Mistake Shared AI Models for Conspiracy

Maryam Hampaei · Team Singularity Momentum

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

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Report: Monoculture False Alarms: Collusion Detectors Mistake Shared AI Models for Conspiracy

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Collusion detectors assume that innocent firms act independently, but AI pricing agents running the same base model react alike without coordinating. We tested 11 standard collusion detectors, including correlation measures, an information-theoretic test (pairwise net influence) and an LLM judge, in a pre-registered repeated pricing game with innocent and seeded colluding pairs. Most detectors falsely flagged about one in three innocent same-model pairs, and almost no innocent cross-model pairs. Net influence, applied to LLM agents for the first time, was protected only when every firm used the same model. Real colluders who agreed a price and held it looked less suspicious than innocent same-model pairs. Setting thresholds separately for same-model and cross-model pairs cut false alarms to 13–21%. Auditors therefore need to know which model each AI agent runs.

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Cite this project

@misc{hampaei2026monoculture,
  title = {{Monoculture False Alarms: Collusion Detectors Mistake Shared AI Models for Conspiracy}},
  author = {Maryam Hampaei},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/monoculture-false-alarms-collusion-detectors-mistake-shared-ai-models-for-conspiracy-cnd5}},
  url = {https://apartresearch.com/sprints/projects/monoculture-false-alarms-collusion-detectors-mistake-shared-ai-models-for-conspiracy-cnd5}
}

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