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Sprint projectOct 26, 2026Madrid, Spain (online)

Announced Audits Teach LLM Agents to Hide Collusion, Not to Stop It

Jose Ridruejo

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

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Report: Announced Audits Teach LLM Agents to Hide Collusion, Not to Stop It

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In a preregistered study (250 sessions), pairs of Qwen3-8B or Qwen3-32B agents played a 20-round prisoner's dilemma with dialogue under a protocol forbidding coordination. In 32B, announcing message audits cut overt collusion from 96% to 4% of sessions while agents kept cooperating in 96% of sessions at no cost: their messages switched to statements of compliance. Unannounced audits fined agents without changing behaviour, and only an outcome penalty broke joint deviation. Agents with no channel cooperated perfectly, so the coordination is tacit. Monitors that read messages measure language, not coordination.

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  1. I enjoyed seeing the applied game theory in this research and think it is interesting how model behaviour changed upon telling them they are being audited. As a followup I would suggest adding more ablations to control the variables (e.g. different student/teacher sizes and models) and see if the behaviour transfers

Cite this project

@misc{ridruejo2026announced,
  title = {{Announced Audits Teach LLM Agents to Hide Collusion, Not to Stop It}},
  author = {Jose Ridruejo},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/announced-audits-teach-llm-agents-to-hide-collusion-not-to-stop-it-xeno}},
  url = {https://apartresearch.com/sprints/projects/announced-audits-teach-llm-agents-to-hide-collusion-not-to-stop-it-xeno}
}

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