Prime Intellect's Florian Brand proposes treating AI evaluations as active environments rather than static performance metrics

I used to think of evals as "here is how you can see how good a model is" but i think of evals now more as "there is something you want to teach the model, and this is the test to figure out if the model actually learned it". Yes, similar, but I think the latent matters.