Recent AI model progress feels mostly like bullshit

The biggest story in AI was released a few weeks ago but was given little attention: on the recent USAMO, SOTA models scored on average 5% (IIRC, it was some abysmal number). This is despite them supposedly having gotten 50%, 60% etc performance on IMO questions. This massively suggests AI models simply remember the past results, instead of actually solving these questions. I'm incredibly surprised no one mentions this, but it's ridiculous that these companies never tell us what (if any) efforts have been made to remove test data (IMO, ICPC, etc) from train data.

And then within a week, Gemini 2.5 was tested and got 25%. Point is AI is getting stronger.

And this only suggested LLMs aren't trained well to write formal math proofs, which is true.

> within a week

How do we know that Gemini 2.5 wasn't specifically trained or fine-tuned with the new questions? I don't buy that a new model could suddenly score 5 times better than the previous state-of-the-art models.

They retrained their model less than a week before its release, just to juice one particular nonstandard eval? Seems implausible. Models get 5x better at things all the time. Challenges like the Winograd schema have gone from impossible to laughably easy practically overnight. Ditto for "Rs in strawberry," ferrying animals across a river, overflowing wine glass, ...

I'm not generally inclined toward the "they are cheating cheaters" mindset, but I'll point out that fine tuning is not the same as retraining. It can be done cheaply and quickly.

Models getting 5X better at things all the time is at least as easy to interpret as evidence of task-specific tuning than as breakthroughs in general ability, especially when the 'things being improved on' are published evals with history.