The limit of a LLM "distribution" effectively is actually only at the token level though once the model has consumed enough language. Which is why those out of distribution tokens are so problematic.

From that point on the model can infer linguistics even on purely encountered words, concepts. I would even propose in context inferred meaning based on context, just like you would do.

It builds conceptual abstractions of MANY levels and all interrelated.

So imagine giving it a task like "design a car for a penguin to drive". The LLM can infer what kinda of input does a car need, what anatomy does a penguin have and it can wire it up descriptively. It is an easy task for an LLM. When you think about the other capabilities like introspection, and external state through observation (any external input), there really are not many fundamental limits on what they can do.

(Ignore image generation, it is an important distinction on how an image is made, end to end sequence vs. pure diffusion vs. hybrid.)

I think you've confused some things. Pay careful note to what I'm calling a distribution. There are many distributions at play here but I'm referring to two specific ones that are clear from context.

I think you've also made a leap in logic. The jury's still out on whether LLMs have internalized some world model or not. It's quite difficult to distinguish memorization from generalization. It's impossible to do when the "test set" is spoiled.

You also need to remember that we train for certain attributes. Does the LLM actually have introspection or does it just appear that way because that's how it was optimized (which we definitely optimize it for that). Is there a difference? The duck test only lets us conclude something is probably a duck, not that it isn't a sophisticated animatronic that we just can't distinguish but someone or something else could.