LLMs are not/will never be thinking though, no matter how good they get? You could potentially argue that there is some level of cognition during the training phases (as long as that isn't being outsourced to humans anyways), but generation of output is stachostic selection of most common (/highly ranked if tuned) following patterns? They cannot learn things outside of training, nor do they actually "know" things. To use the parrot example from above, a parrot doesnt "know" what the words its been taught to mimic are, nor does an LLM "know" what the concept of love is, its just be trained to regurgitate the words that are used by humans to describe such a thing. This isn't a criticism of LLMs, that's what they're supposed to do, but its certainly not cognition.

They factorize the distribution in which they are trained on which is essentially generalization

https://arxiv.org/abs/2602.02385

You’re assuming that thinking requires learning, which I don’t necessarily agree with. Humans can have brain damage which inhibits the formation of long term memories, but such people can still function in the world. Would you say the thing such a person’s brain is doing is something other than thinking?

At any rate, just because the architecture of current LLMs doesn’t support learning at inference time does not constitute a fundamental limit that can never be changed, just a local maximum that has worked well to productize the approach.

And I’m quite certain that once systems that include post-training learning exist people like you will find a way to distinguish that from human learning, moving the goalposts again. You’re not arguing in good faith, you have an essentially religious opinion and you will stick to it as long as you are able.

  > but generation of output is stachostic selection of most common (/highly ranked if tuned) following patterns