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Sprint projectOct 23, 2026Vancouver, Canada

Calibrated Net Influence for Collusion Detection: Confounders, Masking and LLM Agents

Francise Liang

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

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Net influence (Bonjour et al. 2022) flags a colluding pair from action logs with a fixed 0.05-bit threshold. We add two calibrated tests and evaluate them in repeated four-player rock-paper-scissors. With scripted agents, a permutation test finds a planted pair (collusion probability 0.2, 1,200 rounds) in 98.5% of runs, against 46.5% for the fixed threshold, but with no collusion accuses some pair in 55.5% of runs where two followers copy one leader. A bootstrap test of whether a pair stands out keeps observed false accusations at or below 5% under four of five confounders, but cannot find colluders when a third player copies one of them more strongly than the partner does. With Kimi agents, instructed pairs lose points and honest pairs are about as dependent; tests that sometimes single out the colluders also single out an honest pair, and the bootstrap test never flags them.

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Cite this project

@misc{liang2026calibrated,
  title = {{Calibrated Net Influence for Collusion Detection: Confounders, Masking and LLM Agents}},
  author = {Francise Liang},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/calibrated-net-influence-for-collusion-detection-confounders-masking-and-llm-agents-qj76}},
  url = {https://apartresearch.com/sprints/projects/calibrated-net-influence-for-collusion-detection-confounders-masking-and-llm-agents-qj76}
}

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