mlsanityData to 7 Oct 2026

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

arXiv 2512.19007 ↗First posted 22 Dec 2025cs.LG cs.AIcited by 26

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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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2025.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 26.

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  1. 2026TorchLean: Formalizing Neural Networks in Leancited by 4
  2. 2026Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllerscited by 2
  3. 2026Provably Explaining Neural Additive Modelscited by 1
  4. 2026IoUCert: Robustness Verification for Anchor-based Object Detectorscited by 1
  5. 2026VNN-LIB 2.0: Rigorous Foundations for Neural Network Verificationcited by 1
  6. 2026Random Parameter Noise Does Not Make Exact ReLU Verification Easycited by 1
  7. 2026On the Extreme Variance of Certified Local Robustness Across Model Seedscited by 0
  8. 2026Perception with Guarantees: Certified Pose Estimation via Reachability Analysiscited by 0
  9. 2026Lipschitz-Based Robustness Certification Under Floating-Point Executioncited by 0
  10. 2026The Luna Bound Propagator for Formal Analysis of Neural Networkscited by 0
  11. 2026Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopescited by 0
  12. 2026Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinementcited by 0
14 more
  1. 2026Encrypted Neural Networks without Overflowscited by 0
  2. 2026Lipschitz Optimization for Formal Verification of Homographiescited by 0
  3. 2026Neural Network Verification using Partial Multi-Neuron Relaxationcited by 0
  4. 2026Rethinking Evaluation Paradigms in IBP-based Certified Trainingcited by 0
  5. 2026MLSkip: Data Skipping for ML Filters via Lightweight Metadatacited by 0
  6. 2026Shifting-based Optimizable Linear Relaxations for General Activation Functionscited by 0
  7. 2026The Misery of Mechanistic Interpretability: A Formal Perspectivecited by 0
  8. 2026Validating, Not Sampling: Region-Level Robustness of Vision-Language and Vision-Language-Action Modelscited by 0
  9. 2026SoK: Formal Methods for Fact-Checking and Information Integritycited by 0
  10. 2026Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordinationcited by 0
  11. 2026NNV3: Expanding Neural Network Verification to New Architectures and Domainscited by 0
  12. 2026Verifying Neural Networks with Reinforcement Learningcited by 0
  13. 2026CLAD: Constrained Abstract Domain for Neural Network Verificationcited by 0
  14. 2026Neural Network Verification for Deep Joint Source-Channel Codingcited by 0