arXiv: cs.AI preprintsInternational6 November 2026
Pareto DNN Verification: Fast for Most Queries
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arXiv:2512.20755v2 Announce Type: replace-cross Abstract: Formal verification of deep neural networks (DNNs) faces a fundamental scalability challenge: verifying robustness is NP-hard and state-of-the-art tools often time out on modern architectures. We propose PAreto VErification, or PAVE: by augmenting DNNs with early exits (EEs) and verifying exit-by-exit, we solve a large fraction of robustness queries---roughly 80% in our evaluation---in a small fraction of the time required for direct verification. The key insight is that many inputs in a robustness neighborhood exit at an early layer. V
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