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Sprint projectOct 25, 2026Seoul, South Korea

Auditing a Collusion Detector: Reproducing Net Influence, Calibrating Its Threshold, and Testing It on LLM Agents

Junyoung Lee

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

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Bonjour et al. (2022) proposed net influence, a behaviour-only test for collusion between agents, but released no code. We re-implemented it and reproduced 18 of 20 cells of their Table 2 within 10 points. The paper's fixed threshold flags collusion in 45% of 100-game runs that contain none, while a threshold calibrated on simulated null games keeps false alarms near 5% and still catches the weak collusion the paper called undetectable (98% of 5,000-game runs). Agents that share a model are correlated without colluding: in 32 games of four local LLM agents (Llama-3.1-8B, Qwen2.5-7B), net influence flagged an innocent same-model pair in all 8 mixed-model games. A replay-swap test that re-queries each agent on its public context did so in 1 of 8, while all detectors still found the planted covert partnerships in at least 7 of 8 games.

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Cite this project

@misc{lee2026auditing,
  title = {{Auditing a Collusion Detector: Reproducing Net Influence, Calibrating Its Threshold, and Testing It on LLM Agents}},
  author = {Junyoung Lee},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/auditing-a-collusion-detector-reproducing-net-influence-calibrating-its-threshold-and-testing-it-on-llm-agents-hfvc}},
  url = {https://apartresearch.com/sprints/projects/auditing-a-collusion-detector-reproducing-net-influence-calibrating-its-threshold-and-testing-it-on-llm-agents-hfvc}
}

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