One interesting insight is that the set of all possible problems are not as compressible as mathematics assume. That is. Math and problems go hand in hand. Then since problems changes so could math or network math.

One such example is that of singularity. I speculate that math functions gets adapted to the problem. Not assuming the singularity problem are like every other problem. If you start at a singularity problem its perhaps a little bit harder. But otherwise I assume you approach the singularity from an easier problem.

So if you have a variable with a limited range from 0 to 9. Then any function that carries that variable outside its range would impose a problem. Likewise I think the singularity is related to infinity as its an mathematical idea number. So the solution to the singularity is also an idea.

So here is what I propose. Under smart network mathematics you could replace functions with network model functions or network ideas.

So for a problem of 1/0 you replace / with a different network_division(a,b) function. This particular division model function knows your entire problem and adapts its result.

The result is depending on your problem so we got a new idea. There is always a (problem, math model) pair that exists. So a math model could be a machine learning model that solves division near the problem specific singularity.