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Sprint projectOct 26, 2026Hyderabad

When Monitors Agree for the Wrong Reasons: Information Design for Collusion Auditing

Nirdesh Bhandari · Team NB

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

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Report: When Monitors Agree for the Wrong Reasons: Information Design for Collusion Auditing

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We tested whether deliberately creating an "informed minority" of LLM monitors — by giving different monitors different levels of access to multi-agent activity logs — enables a Surprisingly Popular (SP)-inspired voting rule to detect secret coordination without the auditor knowing which monitors have privileged access. In a pilot study using Colosseum's JiraTicket environment, we found three things: (1) only monitors with private channel access detected collusion — V1/V2 monitors missed it 100% of the time; (2) the SP rule failed by default because informed monitors suffered a "curse of knowledge," predicting 95% agreement when actual was 25%; (3) a one-sentence calibration prompt telling informed monitors what others can't see dropped predictions from 95% to ~15%, restoring the SP signal. Our results suggest that information-partition design combined with calibrated aggregation could offer a practical, view-blind method for auditing AI collusion.

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Cite this project

@misc{bhandari2026monitors,
  title = {{When Monitors Agree for the Wrong Reasons: Information Design for Collusion Auditing}},
  author = {Nirdesh Bhandari},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/when-monitors-agree-for-the-wrong-reasons-information-design-for-collusion-auditing-bp9g}},
  url = {https://apartresearch.com/sprints/projects/when-monitors-agree-for-the-wrong-reasons-information-design-for-collusion-auditing-bp9g}
}

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