mlsanityData to 14 Oct 2026

Exploring Solver-Level Warmstarting for Neural Network Verification

arXiv 2609.25962 ↗First posted 22 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

Neural network verification has become a key tool for providing formal guarantees on the behaviour of neural networks. However, many verification problems remain computationally intractable in the worst case: even for common adversarial robustness specifications, verification is NP-complete. Here, we explore the application of solver-level warmstarting for neural network verification to exploit information from previous solutions. We study the effect on running time as several properties are modified, including perturbation radii, input data and the networks themselves, using a pipeline that is generalisable and potentially adaptable to state-of-the-art verifiers. Our results show that warmstarting can significantly reduce verification time in most cases. Moreover, warmstarting enables the successful verification of instances that could not be solved from scratch within the given time limit.

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References · 15

  1. 2013Intriguing properties of neural networkscited by 5,155
  2. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  3. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 536
  4. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 231
  5. 2019Cross-Domain Transferability of Adversarial Perturbationscited by 92
  6. 2021Improved Branch and Bound for Neural Network Verification via Lagrangian Decompositioncited by 44
  7. 2022Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspectivecited by 42
  8. 2021Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differentlycited by 22
  9. 2023Incremental Verification of Neural Networkscited by 14
  10. 2024Autonomous Driving with Spiking Neural Networkscited by 13
  11. 2021Shared Certificates for Neural Network Verificationcited by 10
  12. 2024Relational DNN Verification With Cross Executional Bound Refinementcited by 5
3 more
  1. 2025Exact Verification of Graph Neural Networks with Incremental Constraint Solvingcited by 4
  2. 2026Incremental Neural Network Verification via Learned Conflictscited by 2
  3. 2025Mini-Batch Robustness Verification of Deep Neural Networkscited by 1

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