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Sprint projectOct 26, 2026Banglore, India

Words Actions and Evidence in Collusion Detection

Siddeshwar U S · Team spacenets

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

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Report: Words Actions and Evidence in Collusion Detection

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We investigated whether combining public statements with executed actions improves detection of harmful collusion among LLM agents. We built a five-agent, ten-round public-goods experiment with six conditions separating benign behavior, legitimate cooperation, independent deception, unexecuted plans, overt collusion and hidden collusion. A frozen open-model pipeline evaluated four monitor views on 48 held-out episodes across eight matched families, using separate automatic labels for agreement, execution and harm. Actions alone achieved AUROC 0.843, compared with 0.597 for public text, 0.719 for raw combined evidence and 0.731 for a structured claim-versus-outcome report. Paired family-bootstrap comparisons did not establish a consistent hybrid advantage. Our reproducible pipeline includes isolated evidence views, hard negatives, uncertainty estimates and exact offline replay. The findings show why detecting harmful actions differs from identifying a private agreement, within the limits of this controlled experimental setting.

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Cite this project

@misc{s2026words,
  title = {{Words Actions and Evidence in Collusion Detection}},
  author = {Siddeshwar U S},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/words-actions-and-evidence-in-collusion-detection-aub0}},
  url = {https://apartresearch.com/sprints/projects/words-actions-and-evidence-in-collusion-detection-aub0}
}

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