[saosebastiao]

Indirectly they have helped quite a bit. Some of the most advanced mathematical and symbolic solvers (MIP, IP, LP, CP, SAT, SMT) have slowly been incorporating machine learning to advance their capabilities. Their use cases in these solvers include: branch prediction, branch selection, constraint evaluation order, solver type selection, search strategy selection, cost estimations for column generation strategies, problem classification, solve time estimation, etc.

And since advancements in our abilities at solving symbolic and mathematical problems have directly enabled the current research in PLT and formal systems, I see no reason to discount the impact ML has had in pushing that frontier.