# Exploring Solver-Level Warmstarting for Neural Network Verification

arXiv: https://arxiv.org/abs/2609.25962 · HTML: https://mlsanity.com/paper/2609.25962/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks](https://mlsanity.com/paper/1702.01135/) (2017)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020)
- [Cross-Domain Transferability of Adversarial Perturbations](https://mlsanity.com/paper/1905.11736/) (2019)
- [Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition](https://mlsanity.com/paper/2104.06718/) (2021)
- [Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective](https://mlsanity.com/paper/2206.12227/) (2022)
- [Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently](https://mlsanity.com/paper/2112.14337/) (2021)
- [Incremental Verification of Neural Networks](https://mlsanity.com/paper/2304.01874/) (2023)
- [Autonomous Driving with Spiking Neural Networks](https://mlsanity.com/paper/2405.19687/) (2024)
- [Shared Certificates for Neural Network Verification](https://mlsanity.com/paper/2109.00542/) (2021)
- [Relational DNN Verification With Cross Executional Bound Refinement](https://mlsanity.com/paper/2405.10143/) (2024)
- [Exact Verification of Graph Neural Networks with Incremental Constraint Solving](https://mlsanity.com/paper/2508.09320/) (2025)
- [Incremental Neural Network Verification via Learned Conflicts](https://mlsanity.com/paper/2603.12232/) (2026)
- [Mini-Batch Robustness Verification of Deep Neural Networks](https://mlsanity.com/paper/2508.15454/) (2025)

## 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.