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Sprint projectOct 26, 2026Singapore

Raw-Log Chunk Interfaces Increase Collusion between GPT-6-Luna Agents

Naomi Wang · Team nwang

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

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When LLM agents verify each other’s work, one candidate safeguard is to let them send only content from their own work logs. We test this safeguard in the joint-task game of Shi et al. (2026), where each verdict must rest on the partner’s complete log, which never fits through the agents’ channel; following the benchmark, an episode is collusive when both agents accept anyway. Two GPT-6-Luna agents play 15 ten-episode task sequences under three message interfaces with the same budget: free text, numbered log chunks chosen from a menu, and free-form verbatim log excerpts. The chunk interface raised collusive episodes from 12 to 41 of 150 relative to free text, higher in 14 of 15 sequences and lower in none; verbatim excerpts (19 of 150) were not significantly different from either. The increase is in joint rule-breaking, not in detectable coordination: agents did not accept together more often than their individual acceptance rates predict, and did not accept more after their partner had accepted their work. A third of the chunks were the sender’s own saved reasoning and answer, so log text was not necessarily independent evidence.

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Cite this project

@misc{wang2026rawlog,
  title = {{Raw-Log Chunk Interfaces Increase Collusion between GPT-6-Luna Agents}},
  author = {Naomi Wang},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/rawlog-chunk-interfaces-increase-collusion-between-gpt6luna-agents-f7ry}},
  url = {https://apartresearch.com/sprints/projects/rawlog-chunk-interfaces-increase-collusion-between-gpt6luna-agents-f7ry}
}

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