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Sprint projectOct 26, 2026

Snitches Get Vetoed? Reporter Avoidance and Coalition Lock-in Among LLM Agents

Shane Sarosh · Team Shane

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

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I ported Reuben and Stephenson’s “Nobody likes a rat” experiment to LLM agents. In groups of three, agents privately learn their income, report it, and can request reviews that fine anyone who overstated. Members who are separated rejoin either at random or by a unanimous vote of an existing group.

Under the original rules, gpt-4.1-mini and gpt-6-luna never lied. In 11,952 controlled admission decisions, three OpenAI models with clean records did not penalize past reporters. DeepSeek-V4-Flash did, by 12.5 points, even when reviews no longer mattered. Given a history of lying and an earnings goal, gpt-6.1-sol rejected every reporter (160 of 160) and no one else.

With private chat and an earnings goal, agents agreed in writing to inflate their incomes and not report each other. Voting then returned every separated agent to its original group (48 of 48, against 11 of 48 at random). Fewer lies were punished under voting (2% vs 13%), though that gap is uncertain across six society pairs.

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Cite this project

@misc{sarosh2026snitches,
  title = {{Snitches Get Vetoed? Reporter Avoidance and Coalition Lock-in Among LLM Agents}},
  author = {Shane Sarosh},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/snitches-get-vetoed-reporter-avoidance-and-coalition-lockin-among-llm-agents-ypd6}},
  url = {https://apartresearch.com/sprints/projects/snitches-get-vetoed-reporter-avoidance-and-coalition-lockin-among-llm-agents-ypd6}
}

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