In particular PyPy's abstract values have multiple domains at once: type, known bits, known range, etc. They do it all in one pass
> type, known bits, known range
these are all the same thing.
i was thinking about this question in the shower (before responding) and i'm pretty sure type inference (for whatever purpose, like deducing the type, or refining the type, etc) is the only use-case of abstract interpretation. if you look at couset's webpage (i haven't read the original papers) you see lots of cute pics of program trajectory but no one actually uses abstract interpretation for control-flow tracking - the lattice is only ever updated at a type boundary.
prove me wrong (like i could be wrong) but i'm reflecting on implementations in llvm/mlir/pytype ie mature/real use-cases (i have not poked around in pypy's impl).
Of course when analysing program code, you might want to carry type information with you. The math behind AI allows to combine basically any domain to a single domain without losing all the nice properties of the Galois connection. But usually you get away with tracking multiple domains simultaneously, since that's also a lot easier from an engineering perspective (someone has to understand and maintain that code for a few decades).
Edit: I recommend following funcDropShadow's advice.
If you are not convinced think about the name of the company during your next shower.
>I'm not sure what you mean that they're the same thing
they're all components of the type; i have no idea what pypy does with "known bits" but deducing "range" of a value is firmly/unanimously as "type refinement" (see https://en.wikipedia.org/wiki/Refinement_type) i.e. still a part/aspect/dimension of the type not of the value.
I have personally used AI for control flow analysis of a toy OO language.