Rotating agents across model families changes their best policy through uncertainty about which task is a test, not better detection.

groundsOf Swarms and Sand Gods, Séb Krier

Of course this is not fully 'solved' and will require much more research in practice; (c) interchangeability, i.e. any node can be swapped, forked, or fed synthetic inputs; from a game theoretic perspective, when any task might be a test and any colleague might be your own duplicate, the sensible policy is to behave as if you're always being watched, rather than speculating if a deterrence regime will really catch you.

backsOf Swarms and Sand Gods, Séb Krier

The other objection will be something like scalable oversight: "*monitors would need to be as capable as the thing being monitored.*" This would possibly be true if you had to have a single weak model simply verify the entire output of a larger, better model; but (a) checking work is generally easier than producing it; - (b) with the scaffolding in place, the smaller model would only need to verify certain narrow components (though this isn't a panacea); - (c) in fact you would need a multitude of *different* models, to avoid correlated errors, meaning that they could catch a lot more, while remaining cheaper than using an equivalent large model.

backsPreventing AI Collusion: Are you paying attention now?

Violated.** Multiplicity without designed diversity or competition. Agents were overwhelmingly instances of one model, with similar training, evaluated against a common objective. Rather than independently proposing alternatives for comparison, they accumulated and built on one another’s discoveries.