There is clearly a degree of abstraction-- consider you can make up some game which creates words the LLM has never seen before and assigns them meaning, then ask the LLM to reason about them within the logic of the game and it will do so at least somewhat successfully.

(much worse than it does stuff it's seen before, for sure, but that it does it at all shows there is some abstraction)

Now if that qualifies as "reasoning" is another question, but it may be a metaphysical one with little value in making the world a better place. :P

Whatever we call it there clearly is some amount of emergent abstraction in the models which is useful for at least some applications (if many fewer than the hype suggests). Can that abstraction be isolated from the factual data that went in to construct it? If so then perhaps we could have smaller models with better performance or construct ways to amplify that "operating over abstraction" until it did meet whatever bar you'd require to call it "reasoning", or at least become more useful along the way.