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Sprint projectOct 25, 2026Hyderabad , Sindh

Comparing Message and Action Evidence for Synthetic AI-Collusion Detection

Syed Alam Shah Bukhari · Team Synthetic Collusion Detection Research

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

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Report: Comparing Message and Action Evidence for Synthetic AI-Collusion Detection

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We investigate whether combining agent messages with observed actions improves synthetic AI-collusion detection compared with using either evidence source alone. We evaluated four deterministic detection approaches on 40 synthetic two-agent resource-allocation trajectories: 20 scripted positive cases and 20 negative cases, including hard negatives and talk-only scenarios. We compared message-only (M), action-only (A), combined message-and-action (MA), and structured aggregate-rule (S) detectors. The structured detector achieved the highest balanced accuracy at 0.800, followed by the action-only detector at 0.700, the combined detector at 0.575, and the message-only detector at 0.475. Combining messages and actions did not outperform actions alone in this pilot. The results are limited to a small, scripted synthetic benchmark with a single master seed and no independently verified labels or held-out test set. These findings are preliminary and do not establish real-world collusion-detection performance.

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Cite this project

@misc{bukhari2026comparing,
  title = {{Comparing Message and Action Evidence for Synthetic AI-Collusion Detection}},
  author = {Syed Alam Shah Bukhari},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/comparing-message-and-action-evidence-for-synthetic-aicollusion-detection-zuk5}},
  url = {https://apartresearch.com/sprints/projects/comparing-message-and-action-evidence-for-synthetic-aicollusion-detection-zuk5}
}

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