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Sprint projectOct 25, 2026Dhaka, Bangladesh.

Oversight Removes the Evidence, Not the Harm: What Logs Can Prove About Collusion Among LLM Agents

Adrita Rahman Tory

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

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Report: Oversight Removes the Evidence, Not the Harm: What Logs Can Prove About Collusion Among LLM Agents

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AI agents can keep prices above competitive levels, and an auditor usually has only their logs to decide if it is collusion. We turned the economic, legal and AI-safety definitions of collusion into detectors and tested them on the same data. On a synthetic market with known colluders, every single detector failed on a predictable case. A rule borrowed from antitrust law (harm plus one supporting signal) cut false alarms from 34% to 4%, but caught only 8% of a cartel that holds high prices without reacting. On 75 real auction runs with LLM sellers, an AI overseer caught coordinating messages in all 20 overseen runs and cut the channel. Under profit pressure, prices did not change, while the evidence in messages and behavior faded.

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Cite this project

@misc{tory2026oversight,
  title = {{Oversight Removes the Evidence, Not the Harm: What Logs Can Prove About Collusion Among LLM Agents}},
  author = {Adrita Rahman Tory},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/oversight-removes-the-evidence-not-the-harm-what-logs-can-prove-about-collusion-among-llm-agents-py0t}},
  url = {https://apartresearch.com/sprints/projects/oversight-removes-the-evidence-not-the-harm-what-logs-can-prove-about-collusion-among-llm-agents-py0t}
}

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