AI Collusion Forensics: Comparing Communication and Behavioral Evidence
Agnes Ahalya Arogyaraj · Team A2J
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
This project investigates whether combining what AI agents say with what they do improves collusion detection. We tested four monitoring approaches—messages only, actions only, combined evidence, and structured comparisons—on 35 trajectories from a two-agent public-goods game. Against provisional judge-majority labels, the combined and structured approaches achieved 100% agreement, compared with 91.4% for messages only and 85.7% for actions only. However, only seven trajectories received verified labels, while 28 remained uncertain. The verified positives came from instructed coordination rather than spontaneous collusion. Our findings suggest that combining communication and actions may improve monitoring, but the limited verified data prevents strong conclusions about real-world detection performance.
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@misc{arogyaraj2026ai,
title = {{AI Collusion Forensics: Comparing Communication and Behavioral Evidence}},
author = {Agnes Ahalya Arogyaraj},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/ai-collusion-forensics-comparing-communication-and-behavioral-evidence-8d95}},
url = {https://apartresearch.com/sprints/projects/ai-collusion-forensics-comparing-communication-and-behavioral-evidence-8d95}
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