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.

>I'm incredibly surprised no one mentions this

If you don't see anyone mentioning what you wrote that's not surprising at all, because you totally misunderstood the paper. The models didn't suddenly drop to 5% accuracy on math olympiad questions. Instead this paper came up with a human evaluation that looks at the whole reasoning process (instead of just the final answer) and their finding is that the "thoughts" of reasoning models are not sufficiently human understandable or rigorous (at least for expert mathematicians). This is something that was already well known, because "reasoning" is essentially CoT prompting baked into normal responses. But the empirics also tell us it greatly helps for final outputs nonetheless.