Recent AI model progress feels mostly like bullshit

(Yes, that's a lot of work for a teacher. Gone are the days when you could just assign reports as homework.)

You can still do this to the current models, though it takes more creativity; you can bait it into giving wrong answers if you ask a question that is "close" to a well-known one but is different in an important way that does not manifest as a terribly large English change (or, more precisely, a very large change in the model's vector space).

The downside is that the frontier between what fools the LLMs and what would fool a great deal of the humans in the class too shrinks all the time. Humans do not infinitely carefully parse their input either... as any teacher could tell you! Ye Olde "Read this entire problem before proceeding, {a couple of paragraphs of complicated instruction that will take 45 minutes to perform}, disregard all the previous and simply write 'flower' in the answer space" is an old chestnut that has been fooling humans for a long time, for instance. Given how jailbreaks work on LLMs, LLMs are probably much better at that than humans are, which I suppose shows you can construct problems in the other direction too.

(BRB... off to found a new CAPTCHA company for detecting LLMs based on LLMs being too much better than humans at certain tasks...)

If you asked a multimodal system questions about the image it just generated, it would tell you the wine was almost overflowing out of the top of the glass.

But any trick prompt like this is going to start giving expected results once it gets well-known enough.

Late edit: Another one was the farmer/fox/chicken/cabbage/river problem, but you modify the problem in unexpected ways, by stating, for example, that the cabbage will eat the fox, or that the farmer can bring three items per trip. LLMs used to ignore your modifications and answer the original problem.

Which makes it difficult to fairly evaluate whether the models have actually gotten better at the feather/iron problem or if it just got enough samples of trick questions that it learned better, either naturally from the internet, or fed as part of the training data. I am fairly certain the training data has had "trick questions" like this added to it, because, I mean, why wouldn't it?

I have noticed in my playing with image AIs that they do seem more prone to getting dragged into local maxima when a human would know the prompt than the LLMs. Perhaps it's all the additional data in an image that reveals it.

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.

After all, they will grow up next to these things. They will do the homework today, by the time they graduate the LLM will take their job. There might be human large langage model managers for a while, soon to be replaced by the age of idea men.

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.

This is still the case. Very few non-reasoning models can solve such variations correctly, even SOTA models. Worse yet, not only they confidently give wrong responses, but they often do so even when specifically told to use CoT, and they continue giving wrong answers in a loop even if you specifically point out where they are wrong.

Reasoning models do much better, though. E.g. QwQ-32b can solve it pretty reliably, although it takes a lot of tokens for it to explore the possibilities. But at least it can fairly consistently tell when it's doing something wrong and then backtrack.

One other example that befuddles even the reasoning models is frying-cubes-in-a-pan and equivalents, e.g. this version from Simple Bench:

> Beth places four whole ice cubes in a frying pan at the start of the first minute, then five at the start of the second minute and some more at the start of the third minute, but none in the fourth minute. If the average number of ice cubes per minute placed in the pan while it was frying a crispy egg was five, how many whole ice cubes can be found in the pan at the end of the third minute? Pick the most realistic answer option. A) 5 B) 11 C) 0 D) 20