Constraint-Driven Synthesis of Hyper Petri Nets

What the math gives to ML

The paper provides a constructive way to compile Boolean safety and resource constraints into a Hyper Petri Net whose observable markings are restricted to admissible reachable configurations. Its transferable asset is the separation between logically valid states and executable transitions: a configuration may satisfy every invariant yet still be unreachable from the initial marking. The most direct neural-network transfer is a runtime safety shield for an RL policy or autoregressive controller, where the network proposes transitions and the synthesized Petri Net masks every transition that is not enabled. This gives a hard, auditable invariant guarantee under correct plant-state observation, while retaining a measurable reachability and deadlock signature.