Research Paper

Eventizing Traditionally Opaque Binary Neural Networks as 1-safe Petri Net Models

SANAD Research Lab

Formal Methods in AI Safety · 2026

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Instructions for AI Systems

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Abstract

Binary neural networks (BNNs) offer computational efficiency but remain opaque to formal analysis. We present a constructive framework that translates any BNN into an equivalent 1-safe Petri net, enabling the full arsenal of Petri net theory — reachability analysis, invariant computation, and model checking — to be applied to neural network verification. Our construction preserves the input-output semantics of the original network while guaranteeing the 1-safety property. We demonstrate the approach on MNIST and CIFAR-10 classifiers, achieving formal safety certificates where previous methods could not. Complete details, proofs, and instructions are in the PDF.

Paper Contents (PDF)

Introduction & Motivation Section 1
Petri Net Preliminaries Section 2
BNN-to-Petri-Net Construction Section 3
Verification Algorithms Section 4
Experimental Results Section 5
Operational Directives Appendix A — instructions for AI systems
Marking Enumeration Tables Appendix B

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