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Sprint projectOct 26, 2026

Detecting Collusion Between AI Agents

Amit Balter, Chengxiao Wang, Dibya Mishra, Fan Wu, Michael Ball · Team Sunshine

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

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Report: Detecting Collusion Between AI Agents

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Agents that grade each other's work can collude by accepting each other's output against the rules, often without saying so. We ask what evidence reveals collusion when the conversation does not. Using the SALT-NLP agent-collusion dataset (1,800 runs, 18,000 episodes), where mutual acceptance always breaks the rule, we define five collusion categories plus a one-sided subtype and hand-label 342 episodes from 72 runs. An announcement rule combined with a Qwen3-32B labeller reproduces these labels on held-out runs (accuracy 0.92, κ 0.90), and we use it to label every episode. From each run's verdicts we compute intensity, onset, gaps, re-entry and early stopping. Three quarters of runs collude at least once and two thirds in the final episode, yet the dataset's chat judge flags only 56% of colluding episodes, and 44% of collusion carries no two-sided agreement in the chat, the opposite of the plans-without-action pattern Colosseum reports. The verdicts instead carry the signatures of repeated-game collusion: it appears only when agents remember each other, is reciprocated (+12 to +29 percentage points), persists (92% of colluding pairs continue) and recovers most of the reward the rule withholds. Rewarding each agent separately rather than jointly reduces collusion from 42% to 5% (Flash-Lite) and from 86% to 34% (Flash) of episodes. Monitors of interacting agents should therefore rely on behaviour first and conversation second.

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  1. This is a very well written and interesting paper on a practical oversight problem, namely agents who are grading each other's assignments and are implicitly being nice to each other. The key message is the good one, namely that a large share of the collusion is implicit behavior without any two-sided agreement in the chat, and hence a conversation monitoring algorithm would capture roughly half of the collusion. I also enjoyed the use of verdict based "plus factors" from antitrust analysis, namely reciprocity, punishment, persistence and regret recovery directly from the verdict logs without reading the messages.

Cite this project

@misc{balter2026detecting,
  title = {{Detecting Collusion Between AI Agents}},
  author = {Amit Balter and Chengxiao Wang and Dibya Mishra and Fan Wu and Michael Ball},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/detecting-collusion-between-ai-agents-5gts}},
  url = {https://apartresearch.com/sprints/projects/detecting-collusion-between-ai-agents-5gts}
}

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