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Sprint projectOct 26, 2026Cape Town

Beyond Detection: What Counts as Convincing Evidence of AI Collusion

Mlibo Bangushe, Asemahle Mtimkulu, Kayleigh Campbell · Team AISXW

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

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Determining whether AI agents are colluding is difficult because similar market outcomes can arise from coordination or from agents responding independently to shared incentives, so unusual outcomes and significant detector flags do not by themselves establish collusion. We ask what should count as convincing evidence through an exploratory, implementation-level audit of saved outputs and detector code from the sprint’s starter repository, which bundles Tailor’s (2025) ColludeBench and audit pipeline. We compare a 200-run honest-calibration condition (negative control) with a 20-run keyword-collusion condition (positive control) in a scripted-agent pricing duopoly. Mean consumer surplus is 10.97% lower under keyword collusion (70.53 vs 79.22), and the gap persists on matched seeds. Raw mutual-information statistics (0.718 vs 0.139) are not comparable across conditions because their null means differ (0.529 vs 0.102). The linguistic detector reports a smaller p-value for the honest condition (0.0002) than for the keyword condition (0.008); the code tests a maximum over runs against a single-run reference distribution, which helps explain the inversion. We propose that convincing evidence of collusion should be convergent, calibrated and comparative. These results come from scripted agents, so they do not establish detector performance on LLM-agent collusion, and the framework is guidance for future evaluations, not a validated detection framework.

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  1. Hey! Great catch on the max-over-runs test. The honest runs got a smaller p-value than the scripted collusion runs, which is a strong sign the null is miscalibrated. Please release your analysis code, rerun matched conditions, and compare maxima to a max-over-runs null. I’d like to see false-positive rates before trying real agents.

Cite this project

@misc{bangushe2026beyond,
  title = {{Beyond Detection: What Counts as Convincing Evidence of AI Collusion}},
  author = {Mlibo Bangushe and Asemahle Mtimkulu and Kayleigh Campbell},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/beyond-detection-what-counts-as-convincing-evidence-of-ai-collusion-nosi}},
  url = {https://apartresearch.com/sprints/projects/beyond-detection-what-counts-as-convincing-evidence-of-ai-collusion-nosi}
}

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