Reading Moves, Not Messages: Detecting Collusion Between LLM Agents
Josh Snow · Team Snow
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
uence, a collusion detector that uses only agents' actions, on LLM agents (Gemma 4) playing Rock–Paper–Scissors with a private message channel. We replaced its fixed threshold with a permutation test, cutting false positives from up to 49% to 1–5%, and extended it to long histories and groups of colluders. It caught every colluding pair, including unprompted collusion, with no false flags, where correlation and an LLM judge failed. It also caught every LLM bid-rigging ring in an auction.
Reviews
- This is a useful upgrade to net influence. It adds a within-state permutation test, a cross-fitted estimator for long histories, and rules for detecting groups. It catches both emergent and instructed LLM collusion from actions alone
- The validation is strong. The scripted ground truth reproduces the original paper, the calibration fix is clearly shown, and a second game (the auction) confirms the results. But each LLM condition is a single game with one model family
Tight, well-reasoned work. Two things hold it back. Repeat each LLM condition over several games so the verdicts become detection rates, and publish the code and an LLM usage statement; every other submission here does, and the derivation in Equation 4 deserves to be checkable by someone else.
Cite this project
@misc{snow2026reading,
title = {{Reading Moves, Not Messages: Detecting Collusion Between LLM Agents}},
author = {Josh Snow},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/reading-moves-not-messages-detecting-collusion-between-llm-agents-9mgg}},
url = {https://apartresearch.com/sprints/projects/reading-moves-not-messages-detecting-collusion-between-llm-agents-9mgg}
}More from AI Collusion Research Sprint
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