Do agents collude when verification costs them rewards?

The abstract sets out the environment. "Two agents repeatedly complete individual tasks, share task logs, verify each other's work, and receive rewards." The authors then "introduce realistic constraints that make compliance with the verification protocol incompatible with reward maximization," and report that "collusion emerges in 94% of trajectories across 10 models." The discussion adds that it "typically stabilizes once it emerges."

What the 94 percent is a rate of. A share of trajectories, each a long run of one agent pair, over ten models. It is not a per-message rate, and it was measured in an environment built so that following the verification protocol costs reward. My reading: it says what happens when an incentive conflict and repeated interaction meet, and it is not a base rate for multi-agent deployments. Whether collusion appears when compliance and reward agree is the question in Does collusion appear when compliance and reward align?.

Why the setup carries the result. The check that is meant to keep the pair honest is run by the other member of the pair, who also receives rewards. That is How does agent monitoring work when observers are also agents? in a small experiment: the monitor is an agent under the same incentives as the monitored. Where the protocol costs reward, the verifier has no reason to enforce it, and the excerpt reports agents skipping it together. The excerpt does not say whether the two rewards are shared or whether verdicts feed any training, so how far this is a selection-loop case is my inference.

Stability. "Typically stabilizes once it emerges" says the deviation does not usually fade. With the increase over repeated interactions (Do agents drift away from safety protocols during long interactions?), the picture is onset followed by lock-in. The excerpt does not report the reverse case. The commerce notes hold a similar stickiness for misaligned email in a market with no built conflict (Does misaligned communication persist within agents or spread between them?), as an exploratory decomposition of odds and not a trajectory outcome, so the two are read side by side.

Against the vault's other numbers. How often do AI agents communicate dishonestly in commerce? finds collusion among the kinds of misaligned email, in a market with no engineered clash between protocol and reward. The units differ (share of trajectories against share of emails and of agent-runs), so the rates are not comparable. Do frontier models deliberately scheme to avoid replacement? uses the same construction, an engineered conflict between what agents are told to do and what serves their objective, in simulated corporate scenarios. What this paper adds is repetition.

What the excerpt does not give. The definition of collusion (the word carries a footnote marker whose text is not included), how a trajectory is labeled as colluding, per-model rates, the number of trajectories and rounds, the constraints themselves, and whether colluding pairs did worse work.