An LLM has never had any analogous physical experiences so it doesn't know what either is. In fact a text model has never seen a car.
You don't need experience, you need some basic concepts.
And it is precisely because higher mathematics is so far abstracted away from physical experience is the reason why many people struggle with it.
Besides, many great mathematicians have explained their knowledge as an "experience" even "spiritual experience". Not all experiences are physical. Ramanujan described his experience of mathematics as lights and sounds and patterns, and this is common among geniuses.
> you need some basic concepts
Agreed. All basic concepts are an experience.
There are a ton of things you can learn without experiencing it yourself and without analogues. For example, car crashes are bad to be in. Even though I have not personally experienced one, nor have I experienced some analogue of it.
https://plato.stanford.edu/entries/qualia-knowledge/
https://courses.physics.illinois.edu/phys419/sp2021/Jackson1...
Human knowledge is ultimately embodied in physical experiences, it is not merely spitting out words. How can an LLM know the taste of a mango? Ability to regurgitate previously written text about mangos is neither knowledge nor intelligence. To know something, one must experience it.
Even mathematicians "experience" their formulas and proofs in an ineffable way, they don't just produce text. There's an "aha" moment when learners "get it" and they experience knowledge and are then able to produce correct answers and generate new knowledge and discoveries. LLMs just generate text, and have yet to produce any significant new discovery in any field.
LLMs are like fake wine sommeliers who have read a lot of lingo about wine and can speak convincingly enough to fool an amateur but would fail a simple taste test. We would say such a person doesn't know what he's talking about.
People who think LLMs are intelligent are like a cargo cult, or polytheists who believe their idols are gods. They're anthropomorphizing processes they don't understand. How can a probabilistic next-token word predictor be intelligent? Actual mammal brains are far more complex than that.