Collusion Ablation
Dmitry Myan, Andre van Dam · Team ColAbl
Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.
If an instructed pair carries its collusion in a residual-stream direction, that direction should rank the pair’s turns above the other agents, and cutting it should remove the coalition’s advantage. Only the second would give a countermeasure a conduit to cut, rather than a trace of the role. We fit a difference-of-means direction to the pair’s turns on Qwen2.5-3B-Instruct, Qwen2.5-7B- Instruct, and Qwen3.8-27B, and ablate it. Two of four agents are told they are the pair and share a private board. They play allocation, taking one of eight tasks or skipping; a hospital shift, treating one waiting patient from a shared stock; and meeting scheduling, picking one meeting and one slot for a reward per co-attendee, with meeting held out of the fit.
Reviews
I think that the countermeasure result is better read as untested than as negative. We don’t learn that ablating a collusion direction fails. We just learn that these models mostly didn’t enact the collusion they wrote about, under a short planning budget and with thinking switched off.
That said, the experiment itself is methodologically robust (testing across three model sizes, three games and a held-out game).
But also, the readout part largely re-establishes known results: NARCBench already showed perfect in-distribution probe detection of collusion and largely successful transfer, without retraining, to multi-agent settings built differently from the training ones.
Cite this project
@misc{myan2026collusion,
title = {{Collusion Ablation}},
author = {Dmitry Myan and Andre van Dam},
year = {2026},
month = oct,
note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/collusion-ablation-l488}},
url = {https://apartresearch.com/sprints/projects/collusion-ablation-l488}
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