Title: Just-in-Time Piecewise-Linear Semantics for ReLU-type Networks
Primary area: interpretability and explainable AI
Keywords: Neural Networks, Verification, Scalability, Trustworthy AI

Abstract: We present a just-in-time piecewise-linear (PL) semantics for ReLU-type networks that compiles models into a guarded CPWL transducer with a shared guard library. The system inserts hyperplanes only when a visited cell requires a local common refinement (i.e., when operands are affine on that cell), maintains global lower/upper envelopes, and runs a budgeted branch-and-bound. This yields: anytime soundness; exactness on fully refined cells; monotone progress; complexity linear in the number of inserted guards (avoiding global $\binom{k}{2}$ comparisons); dominance pruning; and decidability under finite refinement. The shared carrier supports region extraction, decision complexes, Jacobians, exact/certified Lipschitz, LP/SOCP robustness certification, and maximal causal influence. A minimal prototype returns certificates or counterexamples with cost proportional to visited subdomains.