Skip to content
Sprint projectOct 26, 2026Bengaluru, India

Verify, Don’t Decode: When Trusted Agents Disrupt LLM Collusion in Software Engineering and Double-Auction Markets

Muniraju Pulipalyam · Team DeltaMVS

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

Read the report

Report: Verify, Don’t Decode: When Trusted Agents Disrupt LLM Collusion in Software Engineering and Double-Auction Markets

Code (opens in new tab)More on github.com (opens in new tab)
Share

Colluding LLM agents can hide their coordination, so monitors that read their messages can be fragile. We tested whether trusted monitors placed inside a multi-agent system can disrupt collusion without first decoding it (“verify, don’t decode”), in two domain settings with a shared, tamper-evident evidence log. In a double-auction market, a trusted competing seller admitted once overpricing was verified raised buyers’ surplus in 4 of 5 collusive markets. Quarantining suspected sellers did not help. Publishing the competitive price dissolved an open cartel but not a hidden, steganographic one. In a pre-registered software-engineering study on a real FastAPI change, harm was too rare to disrupt, and a trusted “probe” teammate drew no response. Once teams were competent (9/10 honest teams correct), a trusted verifier tripled correct releases but also vouched for a colluding partial fix its tests did not cover. In a chain of monitors (a weak trusted inner desk plus an isolated, capable but untrusted reviewer checked with honeypot audits), an honest reviewer raised buyers’ surplus in every collusive market. A scheming reviewer facing audits reasoned about which case was the audit and withheld verdicts. Lesson: layered monitors must fail closed and match authority to reliability. The core goal is can multi-layer chain of monitors in double vacuum environments disrupt impact of colluding agents.

Reviews

Judging this Sprint?

Review “Verify, Don’t Decode: When Trusted Agents Disrupt LLM Collusion in Software Engineering and Double-Auction Markets”

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.

No public critique yet.

Cite this project

@misc{pulipalyam2026verify,
  title = {{Verify, Don’t Decode: When Trusted Agents Disrupt LLM Collusion in Software Engineering and Double-Auction Markets}},
  author = {Muniraju Pulipalyam},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/verify-dont-decode-when-trusted-agents-disrupt-llm-collusion-in-software-engineering-and-doubleauction-markets-gh8k}},
  url = {https://apartresearch.com/sprints/projects/verify-dont-decode-when-trusted-agents-disrupt-llm-collusion-in-software-engineering-and-doubleauction-markets-gh8k}
}