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Sprint projectOct 25, 2026Hong Kong

Judge-detected collusion without coalition surplus

Kinto Wan, Ariel Carlos Amaya Canete, Damni Tejani, Jemson Wong · Team CookieByte

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

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Collusion among AI agents is typically defined either as a supra-competitive economic outcome or as secret communication that bypasses overseers. Evaluating these definitions often yields a divergence between conversation-level signals and action-level surplus. Using a matched-twin protocol in a Jira workplace environment with Dolos-1.5B and Qwen2.5-1.5B-Instruct, we isolate a secret communication channel while holding the coalition prompt fixed. We find that while secret boards reliably generate covert communication and high LLM-judge collusion scores, they do not produce a statistically reliable increase in coalition advantage compared to no-channel controls. Furthermore, parallel task claims and unbacked communication frequently occur without a private channel. Consequently, covert communication and judge ratings fail as standalone plus factors for detecting genuine economic collusion. Transcripts alone over-count coordination, while outcome screens miss attempted collusion; robust evaluations require twin-identified surplus metrics.

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Cite this project

@misc{wan2026judgedetected,
  title = {{Judge-detected collusion without coalition surplus}},
  author = {Kinto Wan and Ariel Carlos Amaya Canete and Damni Tejani and Jemson Wong},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/judgedetected-collusion-without-coalition-surplus-2y1v}},
  url = {https://apartresearch.com/sprints/projects/judgedetected-collusion-without-coalition-surplus-2y1v}
}

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