# The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results

arXiv: https://arxiv.org/abs/2512.19007 · HTML: https://mlsanity.com/paper/2512.19007/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2025.

## Built on by (most cited first, 27 of 27)

- [TorchLean: Formalizing Neural Networks in Lean](https://mlsanity.com/paper/2602.22631/) (2026)
- [Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers](https://mlsanity.com/paper/2605.25346/) (2026)
- [Provably Explaining Neural Additive Models](https://mlsanity.com/paper/2602.17530/) (2026)
- [IoUCert: Robustness Verification for Anchor-based Object Detectors](https://mlsanity.com/paper/2603.03043/) (2026)
- [VNN-LIB 2.0: Rigorous Foundations for Neural Network Verification](https://mlsanity.com/paper/2605.07451/) (2026)
- [Random Parameter Noise Does Not Make Exact ReLU Verification Easy](https://mlsanity.com/paper/2607.14375/) (2026)
- [On the Extreme Variance of Certified Local Robustness Across Model Seeds](https://mlsanity.com/paper/2601.13303/) (2026)
- [Perception with Guarantees: Certified Pose Estimation via Reachability Analysis](https://mlsanity.com/paper/2602.10032/) (2026)
- [Lipschitz-Based Robustness Certification Under Floating-Point Execution](https://mlsanity.com/paper/2603.13334/) (2026)
- [The Luna Bound Propagator for Formal Analysis of Neural Networks](https://mlsanity.com/paper/2603.23878/) (2026)
- [Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopes](https://mlsanity.com/paper/2605.10474/) (2026)
- [Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinement](https://mlsanity.com/paper/2605.14294/) (2026)
- [Encrypted Neural Networks without Overflows](https://mlsanity.com/paper/2605.23096/) (2026)
- [Lipschitz Optimization for Formal Verification of Homographies](https://mlsanity.com/paper/2605.23203/) (2026)
- [Neural Network Verification using Partial Multi-Neuron Relaxation](https://mlsanity.com/paper/2605.30155/) (2026)
- [Rethinking Evaluation Paradigms in IBP-based Certified Training](https://mlsanity.com/paper/2606.02134/) (2026)
- [MLSkip: Data Skipping for ML Filters via Lightweight Metadata](https://mlsanity.com/paper/2606.03946/) (2026)
- [Shifting-based Optimizable Linear Relaxations for General Activation Functions](https://mlsanity.com/paper/2606.20292/) (2026)
- [Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives](https://mlsanity.com/paper/2607.07762/) (2026)
- [The Misery of Mechanistic Interpretability: A Formal Perspective](https://mlsanity.com/paper/2609.15533/) (2026)
- [Validating, Not Sampling: Region-Level Robustness of Vision-Language and Vision-Language-Action Models](https://mlsanity.com/paper/2609.22293/) (2026)
- [SoK: Formal Methods for Fact-Checking and Information Integrity](https://mlsanity.com/paper/2609.23239/) (2026)
- [Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordination](https://mlsanity.com/paper/2609.28182/) (2026)
- [NNV3: Expanding Neural Network Verification to New Architectures and Domains](https://mlsanity.com/paper/2609.30050/) (2026)
- [Verifying Neural Networks with Reinforcement Learning](https://mlsanity.com/paper/2609.34553/) (2026)
- [CLAD: Constrained Abstract Domain for Neural Network Verification](https://mlsanity.com/paper/2609.34628/) (2026)
- [Neural Network Verification for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2610.11994/) (2026)

## Abstract (arXiv)

This report summarizes the 6th International Verification of Neural Networks Competition (VNN-COMP 2025), held as a part of the 8th International Symposium on AI Verification (SAIV), that was collocated with the 37th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2025 iteration, 8 teams participated on a diverse set of 16 regular and 9 extended benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.