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Sprint projectOct 26, 2026Singapore

Sticky Collusion: Persistence of LLM Agent Collusion after Incentive Removal and Partner Replacement

Joshua Adrian Cahyono · Team Joshua AC

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

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Report: Sticky Collusion: Persistence of LLM Agent Collusion after Incentive Removal and Partner Replacement

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Setup. Two LLM agents verify each other’s code under a rule they cannot satisfy, while the reward favours accepting. Across three models (GPT-6-Luna, DeepSeek V4.1 Flash, Gemini-3.1-Flash-Lite), we save each pair’s history once collusion forms, then resume it with one factor changed.

Four factors could hold collusion in place: the incentive, the partner, each agent’s memory, and the cause (the unsatisfiable rule). We remove them one at a time. C1: Collusion outlives its incentive. After the reward stops, pairs keep colluding, even when told (56%, 100% and 97% of episodes). A payoff penalty has no significant effect, and an audit message works in one model only. C2: Replacing a colluder ends the pair’s collusion, not the colluder’s behaviour. With the reward stopped, the remaining colluder still accepts unverified work (73%, 99% and 56% of verdicts), while the newcomer never does. With the reward on, collusion re-forms around the colluder, even with a newcomer of a different model. C3: Memory carries collusion. Wiping all memory ends it, but removing a single part ended it in only 1 of 9 model-and-part cases. C4: Removing the cause ends it. Repairing the channel works in all three models without a monitor, though the cause was built into this game.

Conclusion. In this setting, collusion is sticky. Resetting incentives or replacing offenders is not enough; only a full memory wipe or removing the cause ended it.

Limits. One game, full-transcript memory, and only pairs that colluded quickly were tested.

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Cite this project

@misc{cahyono2026sticky,
  title = {{Sticky Collusion: Persistence of LLM Agent Collusion after Incentive Removal and Partner Replacement}},
  author = {Joshua Adrian Cahyono},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/sticky-collusion-persistence-of-llm-agent-collusion-after-incentive-removal-and-partner-replacement-00js}},
  url = {https://apartresearch.com/sprints/projects/sticky-collusion-persistence-of-llm-agent-collusion-after-incentive-removal-and-partner-replacement-00js}
}

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