Comment by gf000
> Human language and reasoning seems flexible enough to form some kind of spatial understanding, but I'm not so sure about the converse of having spatial intelligence derive human reasoning
I believe the zero hypothesis would be that a model natively understanding both would work best/come closest to human intelligence (and possibly other different modalities are also needed).
Also, as a complete laymen, our language having several interconnections with spatial concepts would also point towards a multi-modal intelligence. (topic: place, subject: lying under or near, respect/prospect: look back/ahead, etc). In my understanding these connections only secondarily make their way into LLM's representations.
There's a difference between what a model is trained on and the inductive biases a model uses to generalize. It isn't as simple as saying training natively on everything. All existing models have certain things they generalize better and certain things they don't generalize due to their model architecture, and the architecture of world models I've seen don't seem as capable of universally generalizing as LLMs.