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

TL;DR

The paper reports on the 6th VNN-COMP 2025, an annual competition that benchmarks neural network verification tools, highlighting standardized formats, evaluation procedures, and key results from participating teams.

Contribution

It introduces standardized formats and evaluation pipelines for neural network verification, and provides a comprehensive summary of the 2025 competition's results and lessons learned.

Findings

8 teams participated with diverse tools

16 regular and 9 extended benchmarks used

Standardized evaluation procedures implemented

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.