> it’s more like “predict the full output in full for this blob of input text"

Hm. I would actually go so far as to say "predict" is just the wrong word to use in 2026, as it drags in a picture of what LLMs are that's from the pre RLVR era, and which is frequently used to suggest they have all sorts of limitations that they've now surpassed ("an LLM will never solve a major open mathematics problem, they're just predicting tokens"). These days I tend to use the more neutral phrase "generate the next token"; I'd only say "predict" in the technical context of (say) the pre-training part of the training run where the object is indeed to minimize the cross-entropy loss function by predicting next tokens in existing text. Generating a token with a modern LLM is better described as something like, "given the totality of its training, including not just next token prediction with pre-training but also solving vast numbers of math and coding problems with RLVR, the LLM generates tokens one by one."

Anyhow. I'm not sure the weight understanding is all that strange. (It's impressive, but so are many thing LLMs do these days.) LLMs are very very good at coding and math problems, of which this is one, and in particular, their RLVR training presumably includes many tasks involving neural networks. It's amazing and impressive, I agree, but I'd say less amazing than proving major open theorems in mathematics, which we already know they can do.

‘Predict’ still works if you think of it as ‘predict what a knowledgeable human would say in this context’.

Substitute ‘generate’ and the meaning is unchanged.

The generation is done by... prediction. It is making a guess based on weights (statistical likelihood) plus a pinch of wobbliness.

It's worth reflecting on the original domain jargon for selecting the next token: completion.

I know how LLMs work. I understand the output projection and logits and sampling just fine. I don't think we should call what is happening prediction because it is a misleading term with a lot of baggage associated with claims that LLMs are just statistically modeling language and "guessing" tokens without any internal representation of meaning. Since your reply is very short I can't tell whether you mean it in that sense or not.

And yes, I understand "prediction" is a standard term in the field. I just think it is a bad one and we should stop using it.

It's still prediction executed by a statistical model. Even RL models are just predicting what the training gauntlet taught them the trainers want to see, in terms of typical behavior.

This doesn't make it any less fascinating that by combining this basic "do as I say" training with massive amounts of all of humanity's data, you can solve major problems—that is still incredible. But that doesn't fundamentally change what is going on, mechanistically.

I don't understand these pseudo-religious desires to read into models something "more" than what we know. We built them. We don't go around making wild claims about how cars are actually more than just a mechanical system, how they aren't just taking us places—we do, but usually such points are cultural not about the mechanica capabilities of cars. People in the LLM space seem to be constantly conflating these two views. It's entirely accurate to say, on the one hand, next token predictors combined with RL, and massive troves of existing human work, can solve culturally interesting problems, and to hold, on the other hand, that mechanistically what is happening is statistical next token predictions over global linguistic space and more or less "monkey see monkey do".