[Radar] Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving

[github-actions]

Candidate Resource

Field	Value
URL	http://arxiv.org/abs/2510.06784v2
Source	arxiv
Relevance Score	82/100
Suggested Category	Research Papers
Tags	zkml, mobile-proving, client-side, neural-networks, zero-knowledge
Authors	Dmytro Zakharov, Oleksandr Kurbatov, Artem Sdobnov, Lev Soukhanov, Yevhenii Sekhin, Vitalii Volovyk, Mykhailo Velykodnyi, Mark Cherepovskyi, Kyrylo Baibula, Lasha Antadze, Pavlo Kravchenko, Volodymyr Dubinin, Yaroslav Panasenko

Description

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.

LLM Reasoning

This paper directly addresses client-side zero-knowledge proving for machine learning on mobile devices, which aligns well with the list's focus on user-device proving. It fits under Research Papers and is also relevant to Mobile and Edge Proving, as it explicitly demonstrates proving on mobile devices.

Suggested Entry

- [Bionetta: Efficient Client-Side Zero-Knowledge Machine Learning Proving](http://arxiv.org/abs/2510.06784v2) - In this report, we compare the performance of our UltraGroth-based zero-knowledge machine learning f

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[moven0831]

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