You don't need skepticism, because even if you're acting in 100% good faith and building a new model, what's the first thing you're going to do? You're going to go look up as many benchmarks as you can find and see how it does on them. It gives you some easy feedback relative to your peers. The fact that your own model may end up being put up against these exact tests is just icing.

So I don't think there's even a question of whether or not newer models are going to be maximizing for benchmarks - they 100% are. The skepticism would be in how it's done. If something's not being run locally, then there's an endless array of ways to cheat - like dynamically loading certain LoRAs in response to certain queries, with some LoRAs trained precisely to maximize benchmark performance. Basically taking a page out of the car company playbook in response to emissions testing.

But I think maximizing the general model itself to perform well on benchmarks isn't really unethical or cheating at all. All you're really doing there is 'outsourcing' part of your quality control tests. But it simultaneously greatly devalues any benchmark, because that benchmark is now the goal.