# JSTprove: Pioneering Verifiable AI for a Trustless Future

arXiv: https://arxiv.org/abs/2510.21024 · HTML: https://mlsanity.com/paper/2510.21024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning](https://mlsanity.com/paper/2502.18535/) (2025)
- [Verifiable evaluations of machine learning models using zkSNARKs](https://mlsanity.com/paper/2402.02675/) (2024)
- [A Survey on the Applications of Zero-Knowledge Proofs](https://mlsanity.com/paper/2408.00243/) (2024)
- [Zero-Knowledge Proof Frameworks: A Systematic Survey](https://mlsanity.com/paper/2502.07063/) (2025)
- [ZKTorch: Compiling ML Inference to Zero-Knowledge Proofs via Parallel Proof Accumulation](https://mlsanity.com/paper/2507.07031/) (2025)

## Abstract (arXiv)

The integration of machine learning (ML) systems into critical industries such as healthcare, finance, and cybersecurity has transformed decision-making processes, but it also brings new challenges around trust, security, and accountability. As AI systems become more ubiquitous, ensuring the transparency and correctness of AI-driven decisions is crucial, especially when they have direct consequences on privacy, security, or fairness. Verifiable AI, powered by Zero-Knowledge Machine Learning (zkML), offers a robust solution to these challenges. zkML enables the verification of AI model inferences without exposing sensitive data, providing an essential layer of trust and privacy. However, traditional zkML systems typically require deep cryptographic expertise, placing them beyond the reach of most ML engineers. In this paper, we introduce JSTprove, a specialized zkML toolkit, built on Polyhedra Network's Expander backend, to enable AI developers and ML engineers to generate and verify proofs of AI inference. JSTprove provides an end-to-end verifiable AI inference pipeline that hides cryptographic complexity behind a simple command-line interface while exposing auditable artifacts for reproducibility. We present the design, innovations, and real-world use cases of JSTprove as well as our blueprints and tooling to encourage community review and extension. JSTprove therefore serves both as a usable zkML product for current engineering needs and as a reproducible foundation for future research and production deployments of verifiable AI.