Models have trouble with discrete spaces often, partly because of the internal continuous space representations, but also in this context because the transition

Models have trouble with discrete spaces often, partly because of the internal continuous space representations, but also in this context because the transition from the probabilities in natural language to mathematics may not be as stark as it should be. But to make matters worse, or more muddied, 1+1=1 can be a valid mathematical statement. It simply depends upon the set and group you have, or if you're doing modular arithmetic, etc. Sometimes you're given a unital magma. So, there's still a heavy dependency on context for the problem setup, but the underlying discrete and deterministic rules that are applied to the context is less malleable than other context switches in NLP LLMs do well in (such as language styling).