Jagged AGI: o3, Gemini 2.5, and everything after

In the last example (the riddle)--I generally assume the AI isn't misreading, rather that it assumes you couldn't give it the riddle correctly, but it has seen it already.

I would do the same thing, I think. It's too well-known.

The variation doesn't read like a riddle at all, so it's confusing even to me as a human. I can't find the riddle part. Maybe the AI is confused, too. I think it makes an okay assumption.

I guess it would be nice if the AI asked a follow up question like "are you sure you wrote down the riddle correctly?", and I think it could if instructed to, but right now they don't generally do that on their own.

> generally assume the AI isn't misreading, rather that it assumes you couldn't give it the riddle correctly, but it has seen it already.

LLMs doesn't assume, its a text completer. It sees something that looks almost like a well known problem and it will complete with that well known problem, its a problem specific to being a text completer that is hard to get around.

This take really misses a key part of implementation of these LLMs and I’ve been struggling to put my finger on it.

In every LLM thread someone chimes in with “it’s just a statistical token predictor”.

I feel this misses the point and I think it dismisses attention heads and transformers, and that’s what sits weird with me every time I see this kind of take.

There _is_ an assumption being made within the model at runtime. Assumption, confusion, uncertainty - one camp might argue that none of these exist in the LLM.

But doesn’t the implementation constantly make assumptions? And what even IS your definition of “assumption” that’s not being met here?

Edit: I guess my point, overall, is: what’s even the purpose of making this distinction anymore? It derails the discussion in a way that’s not insightful or productive.