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

PAIR Auditing Pairs for AI Agent Collusion

Chung Ho Man · Team PAIR

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

Read the report

Report: PAIR Auditing Pairs for AI Agent Collusion

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

Collusion can leave evidence across two agents while each individual record appears innocuous. A monitor that reports only per-agent coverage can therefore overstate its ability to collect actionable evidence. We study how audit correlation changes detection under an exact record budget and identical marginal coverage for every agent. PAIR applies a standard max-min linear program to the joint inclusion probabilities of a specified interaction graph. For eight agents and two audits per round, four feasible disjoint pairs obtain 25% joint coverage, compared with 3.57% under uniform fixed-size sampling. The same policy reduces coverage when either record alone suffices. We derive the exact reversal: under equal marginals and a mixture of joint-only and single-record evidence, pairing helps precisely when the joint-only fraction exceeds one half. In 40 shared- controller bandit runs per condition, the higher coverage deters attacks in a deliberately selected incentive regime. However, allowing migration to unmodeled pairs or revealing the realized audit draw defeats the specialized policy. A uniform hedge restores a measurable coverage floor but does not guarantee deterrence. These are synthetic allocation results, not evidence of emergent LLM collusion or a validated semantic detector. The contribution is a reproducible evidence-aware benchmark and explicit limits on when correlated auditing is beneficial.

Reviews

Judging this Sprint?

Review “PAIR Auditing Pairs for AI Agent 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.

  1. Thank you for this submission! The core idea is intriguing – please do continue with your suggested next steps.

Cite this project

@misc{man2026pair,
  title = {{PAIR Auditing Pairs for AI Agent Collusion}},
  author = {Chung Ho Man},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/pair-auditing-pairs-for-ai-agent-collusion-ks3h}},
  url = {https://apartresearch.com/sprints/projects/pair-auditing-pairs-for-ai-agent-collusion-ks3h}
}

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