# Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving

arXiv: https://arxiv.org/abs/2510.06784 · HTML: https://mlsanity.com/paper/2510.06784/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 1 of 1)

- [Verifiable evaluations of machine learning models using zkSNARKs](https://mlsanity.com/paper/2402.02675/) (2024)

## Built on by (most cited first, 1 of 1)

- [Jolt Atlas: Verifiable Inference via Lookup Arguments in Zero Knowledge](https://mlsanity.com/paper/2602.17452/) (2026)

## Abstract (arXiv)

In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning framework Bionetta to other tools of similar purpose such as EZKL, Lagrange's deep-prove, or zkml. The results show a significant boost in the proving time for custom-crafted neural networks: they can be proven even on mobile devices, enabling numerous client-side proving applications. While our scheme increases the cost of one-time preprocessing steps, such as circuit compilation and generating trusted setup, our approach is, to the best of our knowledge, the only one that is deployable on the native EVM smart contracts without overwhelming proof size and verification overheads.