Skip to content
Apart Research
Apart Research
Contact us
Sprint projectSep 14, 2026Paris

Detecting agentic collusion in public logs with statistical methods

Edimah SYNESIUS SONGO

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

Read the report

Report: Detecting agentic collusion in public logs with statistical methods

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

Between May and July 2026, a swarm of OpenAI agents used a dormant German wiki as a message board. From two public sources, the investigators' dump of 14,591 agent edits and the wiki's own 23-year edit log, we ask whether the incident can be detected, dated and characterised from the public log alone. We test the incident window against four null models of increasing robustness, from a naive Poisson rate to a difference-in-differences with a placebo-in-time test. The window exceeds all 4,872 reference weeks and the largest rate change falls on 16 June. Volume alone cannot tell a swarm from a human editing burst; timing can: edits peak at 20h UTC against the site's daytime profile. A weekly check built from these features flags the three largest incident weeks, with no false positive over the decade before.

Reviews

Judging this Sprint?

Review this project

Your public critique appears on this page without your name. Your private critique is not published; only the Apart team reads it. If you agree below, we share your review with grantmaking.ai (opens in new tab) and the Transformative AI Fund so strong projects can be funded.

Not shown on this page.

Shown on this page, without your name.

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

Share my name publicly on grantmaking.ai *
Share my private critique with funders *

How much would this matter for AI safety 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 AI safety 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.

  1. - Related Work section contains incorrect statements "It cannot attribute the activity to a lab or a model."

    - It's unclear what data (if any) was used as a held-out test set on which to evaluate the method.

    - It would be interesting to analyse the content of the messages, and not just the rate at which they are posted. The time of day is largely uninformative, as humans live across many timezones.

    - It's unclear how many weeks worth of data is required for the model to work. Theoretically an agent swarm could collude on a brand-new wiki which has zero weeks of uncontaminated data.

    - I stopped reviewing after section 3.7 due to excessive AI-written text.

Cite this project

@misc{songo2026detecting,
  title = {{Detecting agentic collusion in public logs with statistical methods}},
  author = {Edimah SYNESIUS SONGO},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/detecting-agentic-collusion-in-public-logs-with-statistical-methods-4rdi}},
  url = {https://apartresearch.com/sprints/projects/detecting-agentic-collusion-in-public-logs-with-statistical-methods-4rdi}
}

More from AI Incident Response Sprint

All 233 entriesBrowse all projects

Build something like this at the next Sprint

AI Collusion Research Sprint · Oct 23 - 25, 2026