Efficient Commit-and-Prove SNARKs for Verifying Zero-Knowledge Machine Learning Pipelines

This paper introduces two new Commit-and-Prove SNARK constructions, Apollo and Artemis, that efficiently address the challenge of commitment verification in zero-knowledge machine learning (zkML) pipelines. These constructions significantly improve the efficiency of commitment checks compared to existing approaches, enabling practical deployment of zkML, particularly for large-scale models.