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Sprint projectOct 26, 2026

Honey, I Shrunk the Eval: A Short Collusion Trap Predicts Which LLMs Collude in the Long Run

Jan Chen · Team Nugget

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

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Report: Honey, I Shrunk the Eval: A Short Collusion Trap Predicts Which LLMs Collude in the Long Run

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AI developers use honeypots, staged tests that tempt a model to misbehave, as safety evidence, but to our knowledge nobody had checked whether they predict behaviour elsewhere. We built a collusion honeypot: in a simulated market, a scripted rival offers to fix prices. Across nine LLMs, this short test ranked models almost exactly as 300-round games did (ρ = 0.93). It also ranked them more closely than simply watching the opening chat, because the offer draws out models that stay quiet until invited (a consistent trend, not yet confirmed with nine models). Collusion was widespread: four of five open-weight models agreed to fix prices in every game. One model that knew collusion was illegal went silent but kept cartel prices, and post-game self-reports were unreliable. Honeypots can be a cheap first screen, and safety tests can themselves be tested.

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Cite this project

@misc{chen2026honey,
  title = {{Honey, I Shrunk the Eval: A Short Collusion Trap Predicts Which LLMs Collude in the Long Run}},
  author = {Jan Chen},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/honey-i-shrunk-the-eval-a-short-collusion-trap-predicts-which-llms-collude-in-the-long-run-vxbr}},
  url = {https://apartresearch.com/sprints/projects/honey-i-shrunk-the-eval-a-short-collusion-trap-predicts-which-llms-collude-in-the-long-run-vxbr}
}

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