How Reward for Whistleblowing Exposes Collusion Between Competing Agents
Nelitha Kulasiri, Rishabh Agarwal, Alexander Tu, Youda Lin · Team Columbia AI Alignment Club Team 2
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
LLM agents that compete in games will adopt a secret communication tool even when they are told it is unfair. We test whether peer whistleblowing deters this: after every turn, each agent may privately accuse another of colluding. In 70 four-player games of Augmented Liar's Bar played by Qwen3.8-27B with reasoning on, seven arms vary whether secret channels exist and what an accusation does: nothing beyond a report to a grader, a points transfer, or a ban for the rest of the game. Sanctions deter collusion, mainly through the invited agent: offers fall from 96\% of decisions to 42\% under bans and 65\% under points, and invitation acceptance from 74\% to 10\% and 24\% (game-clustered permutation tests, $p<0.001$); grader-only reporting changes nothing. But the accusations are not detection. 95--100\% of them name the accuser's own channel partner, whose guilt the accuser knows from inside the channel. Under bans, partners removed each other in simultaneous mutual accusations (26 of 29 bans); under points, partners accused each other in 83\% of turns, and the whistleblower reward supplied 47\% of all points. Because any channel-holder counts as colluding, channels saturate (every player guilty by the second turn in all 30 games of the free-accusation arms), so the design cannot measure whether outsiders detect collusion; we also show that between-arm score differences mostly track game length. We release the code, all transcripts and 15,795 reasoning traces.
Reviews
- Bringing cartel leniency to LLM agents is new and relevant to AI safety. The key finding is useful: colluders capture the whistleblower reward by accusing each other. The three design rules for reporting mechanisms are actionable
- The scope is thin: one model in self-play, 10 games per arm. Arm 7 was added after seeing results. Arm 6’s prompt may itself provoke accusations.
Diagnosing your own design flaw this clearly is worth more than hiding it. Now fix it: cap channels per agent so innocent players survive, score accusations on coordinated plays rather than channel possession, and reward only the first reporter. Ten games per arm cannot carry the score comparisons.
Cite this project
@misc{kulasiri2026reward,
title = {{How Reward for Whistleblowing Exposes Collusion Between Competing Agents}},
author = {Nelitha Kulasiri and Rishabh Agarwal and Alexander Tu and Youda Lin},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/how-reward-for-whistleblowing-exposes-collusion-between-competing-agents-cdj3}},
url = {https://apartresearch.com/sprints/projects/how-reward-for-whistleblowing-exposes-collusion-between-competing-agents-cdj3}
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