Incremental Neural Network Verification via Learned Conflicts
Neural network verification is often used as a core component within larger analysis procedures, which generate sequences of closely related verification queries over the same network. In existing neural network verifiers, each query is typically solved independently, and information learned during previous runs is discarded, leading to repeated exploration of the same infeasible regions of the search space. In this work, we aim to expedite verification by reducing this redundancy. We propose an incremental verification technique that reuses learned conflicts across related verification queries. The technique can be added on top of any branch-and-bound-based neural network verifier. Durin...
| Authors | Raya Elsaleh, Liam Davis, Haoze Wu et al. |
|---|---|
| Primary category | cs.LO |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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