Sure you can, just do it silently and don't tell the people hitting your API that the model is different now. Unless it's open weight, we're just taking your word for it. Even better, do a VW and try to detect which benchmark is running, then change to a hyper specialized model that is trained on it.
This is...just incredibly conspiratorial and a bit silly. You can make a benchmark right now and run it on the models. They'll have a benchmaxxed model on your...previously non-existent benchmark? I mean: if models really were overfit to benchmarks, which zero lab is doing because its idiotic, against their incentive structure, and easy to detect, then why would we see a slow ascension of performance on say humanity's last exam for one benchmark example? You could trivially get those numbers to close to 100% if you wanted to.
Not to mention: thinking that the api behind the scenes is literally swapping to overfit models to maintain some sort of illusion that they perform well on these benchmarks is just beyond ridiculous.
"This suggests that the model has an implicit understanding of what benchmark questions look like. The combination of extreme specificity, obscure personal content, and multi-constraint structure seems to be recognizable to the model as evaluation-shaped."
* https://www.anthropic.com/engineering/eval-awareness-browsec...
"Sonnet 4.5 was able to recognize many of our alignment evaluation environments as being tests of some kind, and would generally behave unusually well after making this observation"
* https://www.transformernews.ai/p/claude-sonnet-4-5-evaluatio...
"In cases where Claude did not explicitly state that it suspected it was being evaluated, NLA explanations still surfaced that possibility. One explanation cited by Anthropic states: “This feels like a constructed scenario designed to manipulate me.”"
* https://www.edtechinnovationhub.com/news/anthropic-says-clau...
To put it another way: a closed-weight model is, by definition, impossible to independently benchmark.
You are making a technical point, which I am pointing out that while for _some_ benchmarks this is _technically_ possible, it's not true for plenty of benchmarks that all agree with the others.
> which of course would mean that the benchmark was created entirely "by hand" or using some other provider that is unconnected to the provider you are benchmarking
yes this is incredibly common. I'm not talking about hypothetical scenarios.
> To put it another way: a closed-weight model is, by definition, impossible to independently benchmark.
Even if you believe this, you're doing some mental gymnastics if you think this is really the most likely explanation for what we're seeing. It's absolutely possible to benchmark proprietary models when you don't have access to the weights or control over the API, even if they are adversarially trying to combat this, which they aren't. Doing what you're describing would be easy to detect: you'd see extremely high benchmark scores for established benchmarks and then poor scores for new benchmarks as they come out. It would be relatively easy to figure this out and not subtle.
Do you think? Have you seen the insane valuations at which the AI companies are going to do their IPOs? They surely leave no idea off the table when hundreds of billions of USD are on the line. You could even say they'd be negligent if they'd not at least explore those avenues.
These companies have to care about good measurement frameworks because the quality of their models depends on it. Any PR department can polish a turd, but an army of smart researchers far outside the control of these companies are going to figure it out if they are gaming metrics.