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

• URL: http://arxiv.org/abs/2512.19007v1
• Date: Mon, 22 Dec 2025 03:48:31 GMT
• Title: The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results
• Authors: Konstantin Kaulen, Tobias Ladner, Stanley Bak, Christopher Brix, Hai Duong, Thomas Flinkow, Taylor T. Johnson, Lukas Koller, Edoardo Manino, ThanhVu H Nguyen, Haoze Wu,
• Abstract summary: This report summarizes the 6th International Verification of Neural Networks Competition (VNN-COMP 2025)<n>VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools.<n>This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.
• Score: 10.109500196139662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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