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.

Yes, here's the link: https://arxiv.org/abs/2503.21934v1

Anecdotally, I've been playing around with o3-mini on undergraduate math questions: it is much better at "plug-and-chug" proofs than GPT-4, but those problems aren't independently interesting, they are explicitly pedagogical. For anything requiring insight, it's either:

1) A very good answer that reveals the LLM has seen the problem before (e.g. naming the theorem, presenting a "standard" proof, using a much more powerful result)

2) A bad answer that looks correct and takes an enormous amount of effort to falsify. (This is the secret sauce of LLM hype.)

I dread undergraduate STEM majors using this thing - I asked it a problem about rotations and spherical geometry, but got back a pile of advanced geometric algebra, when I was looking for "draw a spherical triangle." If I didn't know the answer, I would have been badly confused. See also this real-world example of an LLM leading a recreational mathematician astray: https://xcancel.com/colin_fraser/status/1900655006996390172#...

I will add that in 10 years the field will be intensely criticized for its reliance on multiple-choice benchmarks; it is not surprising or interesting that next-token prediction can game multiple-choice questions!

Anecdotally: schoolkids are at the leading edge of LLM innovation, and nowadays all homework assignments are explicitly made to be LLM-proof. (Well, at least in my son's school. Yours might be different.)

This effectively makes LLMs useless for education. (Also sours the next generation on LLMs in general, these things are extremely lame to the proverbial "kids these days".)

How do you make homework assignments LLM-proof? There may be a huge business opportunity if that actually works, because LLMs are destroying education at a rapid pace.

By giving pen and paper exams and telling your students that the only viable preparation strategy is doing the hw assignments themselves :)

You wish. I used to think that too. But it turns out, nowadays, every single exam in person is done with a phone hidden somewhere, with various efficiency, and you can't really strip students before they enter the room.

Some teachers try to collect the phones beforehand, but then students simply give out older phones and keep their active ones with them.

You could try to verify that the phones they're giving out are working by calling them, but that would take an enormous amount of time and it's impractical for simple exams.

We really have no idea how much AI is ruining education right now.

Unlike the hard problem of "making an exam difficult to take when you have access to an LLM", "making sure students don't have devices on them when they take one" is very tractable, even if teachers are going to need some time to catch up with the curve.

Any of the following could work, though the specific tradeoffs & implementation details do vary:

- have <n> teachers walking around the room to watch for cheaters

- mount a few cameras to various points in the room and give the teacher a dashboard so that they can watch from all angles

- record from above and use AI to flag potential cheaters for manual review

- disable Wi-Fi + activate cell jammers during exam time (with a land-line in the room in case of emergencies?)

- build dedicated examination rooms lined with metal mesh to disrupt cell reception

So unlike "beating LLMs" (where it's an open question as to whether it's even possible, and a moving target to boot), barring serious advances in wearable technology this just seems like a question of funding and therefore political will.