# First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)

Source: [First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)](<https://www.sota2.com/research/paper/68fa7b1f1e209131cea86aa9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T07:01:39.647Z

Authors: Christopher Brix, Mark Niklas M\\"uller, Stanley Bak, Taylor T. Johnson, Changliu Liu

Published: 2023-01-14T04:04:12.000Z

## Abstract

This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2301.05815>)

[PDF](<https://arxiv.org/pdf/2301.05815.pdf>)