Skip to content
Apart Research
Apart Research
Contact us
Sprint projectOct 25, 2026

Collusion Detectors See Shared Secrets, Not Collusion

Sidhant Mahi · Team LUNA

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

Read the report

Report: Collusion Detectors See Shared Secrets, Not Collusion

Open in a new tab (opens in new tab)
Presentation

Presentation: Collusion Detectors See Shared Secrets, Not Collusion

Open in a new tab (opens in new tab)
Code (opens in new tab)
Share

We tested whether information-theoretic statistics, including Bonjour et al.’s pairwise net influence, can identify a colluding pair in repeated Rock–Paper–Scissors. We ran them on scripted agents, Qwen2.5-3B and Claude Haiku 5.5, and compared them against action correlation and an LLM judge. We found that these detectors measure shared hidden information, not collusion. They miss collusion through public signals, and net influence is biased and degenerate in short games. In our LLM pilot the seeded colluders never actually coordinated, so every statistic was at chance. The LLM judge was below chance, because it mistook honest herding for collusion.

Reviews

Judging this Sprint?

Review “Collusion Detectors See Shared Secrets, Not Collusion”

Your public critique appears on the project page without your name. Reviews of strong projects are shared with funders, as you choose below.

Not shown on the project page.

Shown on the project page, without your name.

Only the Apart team reads this, and funders if you agree below.

Decline to score

Only when the quality of the writing makes the work impossible to follow. Say why in your private critique; the project page shows only that a judge declined, not why.

How much would this matter for the field if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.
Sharing your name *

When we share a strong project with funders, your name, scores and public critique go with it, confidentially; this choice is only about whether your name is public. If you think this project deserves funding, please also endorse it: that carries the most weight.

Share my private critique with funders *

See our Privacy Policy.

    • The statistics are great - calibrated permutation nulls, multiplicity correction and an honest reproduction of net influence. But the LLM section is more of a compliance audit, not a detector test.
    • The scope is narrow - one toy game, 20-round runs and no communication channel

  2. This project asks what statistical collusion detectors actually measure. These detectors flag two agents whose moves are more related than chance. The main claim is that the right test is whether moves are related beyond what the public game history contains, and identifies three blind spots for such detectors: coordination through a public signal, a shared secret without an agreed way to use it, and codes that only show up across three agents. I like how the author frames the test as dependence beyond the public history, but • the LLM experiments contain no real collusion to detect. The pairs told to collude did worse than other players, and when detectors ranked these pairs low, it only shows that they played differently, and it says nothing about whether detectors "see collusion".

  3. The authors seem to have identified valid issues in their chosen collusion detector. However, the chosen information-theoretic detector seems unlikely to be a key monitoring tool for realistic LLM deployments, and this limits the significance of the finding

Cite this project

@misc{mahi2026collusion,
  title = {{Collusion Detectors See Shared Secrets, Not Collusion}},
  author = {Sidhant Mahi},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/collusion-detectors-see-shared-secrets-not-collusion-3apy}},
  url = {https://apartresearch.com/sprints/projects/collusion-detectors-see-shared-secrets-not-collusion-3apy}
}

More from AI Collusion Research Sprint

All 392 entriesBrowse all projects

A Sprint is running now

AI x Epistemics Research Sprint · Nov 13 - 15, 2026