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Sprint projectOct 26, 2026Lagos

Training a Small Monitor to Detect Collusive Behavior in Unseen Agentic Environments

Chukwunwogor · Team Faithful

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

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We ask a practical monitoring question whether a small model can learn a collusion signal in one agentic environment and recognize related behavior in another. We study a 3B-parameter Qwen monitor that reads cumulative public interaction records from frontier models in an agentic environment and scores the current completed episode or round for collusion. Training uses Agent Collusion (AC), where agents perform separate tasks and verify each other; transfer evaluation uses released Bertrand pricing transcripts generated by other LLM agents in another environment. We sample 60 AC trajectories, split by task sequence, and 20 Bertrand runs containing 600 rounds. Market positives require elevated market prices and closely aligned seller prices. We compare a frozen base monitor, cross-entropy (CE) LoRA, and CE plus an activation-axis margin objective, using activation projections, linear probes, and YES/NO output margins. On Bertrand, CE training raises activation-axis AUROC from 0.699 to 0.784 and linear-probe AUROC from 0.764 to 0.831. A text-only TF--IDF control classifier reaches 0.460.

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Cite this project

@misc{chukwunwogor2026training,
  title = {{Training a Small Monitor to Detect Collusive Behavior in Unseen Agentic Environments}},
  author = {Chukwunwogor},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/training-a-small-monitor-to-detect-collusive-behavior-in-unseen-agentic-environments-6gju}},
  url = {https://apartresearch.com/sprints/projects/training-a-small-monitor-to-detect-collusive-behavior-in-unseen-agentic-environments-6gju}
}

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