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Sprint projectOct 26, 2026

WHAT COLLUSION MONITORS MEASURE: MESSAGES, ACTIONS, AND OUTCOMES IN LLM DOUBLE AUCTIONS

Devesh Kaushal · Team Kaushal

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

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Report: WHAT COLLUSION MONITORS MEASURE: MESSAGES, ACTIONS, AND OUTCOMES IN LLM DOUBLE AUCTIONS

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We evaluate message and action indicators of collusion in LLM double auctions using 40 new Qwen3-8B runs, 50 published GPT-4.1 runs, and synthetic controls. A Qwen3-32B judge re-scores 1,920 message-rounds, with agreement against the archived overseer (κ = 0.72). In Qwen, monitored conditions have fewer flagged rounds (84% versus 3%), without statistically detected price or surplus differences. In GPT-4.1, oversight triggers early message restrictions, yet buyer surplus shows no statistically detected improvement over unrestricted communication under pressure ($104 versus $86). Early communication remains a possible contributor. In synthetic markets, pooled dependence flags a common market signal in 98% of datasets; net influence flags it in 2% and a pair-specific signal in 86%. These results motivate evaluating indicators alongside matched controls and welfare outcomes; non significant differences do not establish equivalence.

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Cite this project

@misc{kaushal2026collusion,
  title = {{WHAT COLLUSION MONITORS MEASURE: MESSAGES, ACTIONS, AND OUTCOMES IN LLM DOUBLE AUCTIONS}},
  author = {Devesh Kaushal},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/what-collusion-monitors-measure-messages-actions-and-outcomes-in-llm-double-auctions-37eq}},
  url = {https://apartresearch.com/sprints/projects/what-collusion-monitors-measure-messages-actions-and-outcomes-in-llm-double-auctions-37eq}
}

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