Proof-Carrying Verification for ReLU Networks via Rational Certificates
Chandrasekhar Gokavarapu (Department of Mathematics, Government College (Autonomous), Rajahmundry, A.P., India)

TL;DR
This paper introduces a proof-carrying verification method for ReLU neural networks that uses rational certificates to ensure the trustworthiness of safety claims, providing an exact and certified reasoning framework.
Contribution
It develops a formal proof core with rational certificates for verifying PWL neural constraints, bridging SMT/MILP encodings with exact proof checking.
Findings
Certificates validate linear consequences and infeasibility in rational arithmetic.
The proof kernel is sound and complete for entailment, ensuring reliable verification.
End-to-end examples demonstrate certified reasoning without solver trust.
Abstract
Rectified Linear Unit (ReLU) networks are piecewise-linear (PWL), so universal linear safety properties can be reduced to reasoning about linear constraints. Modern verifiers rely on SMT(LRA) procedures or MILP encodings, but a safety claim is only as trustworthy as the evidence it produces. We develop a proof-carrying verification core for PWL neural constraints on an input domain . We formalize the exact PWL semantics as a union of polyhedra indexed by activation patterns, relate this model to standard exact SMT/MILP encodings and to the canonical convex-hull (ideal) relaxation of a bounded ReLU, and introduce a small certificate calculus whose proof objects live over . Two certificate types suffice for the core reasoning steps: entailment certificates validate linear consequences (bound tightening and learned cuts), while Farkas certificates…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Formal Methods in Verification · Advanced Graph Neural Networks
