Zero knowledge verification for frontier AI training is possible

[2606.05433] Proposals and challenges for zero-knowledge verification of frontier AI training Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2606.05433 (cs) [Submitted on 3 Jun 2026 ( v1 ), last revised 21 Oct 2026 (this version, v3)] Title: Proposals and challenges for zero-knowledge verification of frontier AI training Authors: Pierre Peigné , Ky Nguyen , Paul Wang View PDF HTML (experimental) Abstract: Proposals for Frontier AI rules often use cumulative training compute as the main criterion for identifying high-impact models. Recent governance analyses call zero-knowledge proofs promising but currently impractical at frontier scale [26, 4]. We argue this barrier reflects the current paradigm, not a fundamental limit. Our frontier dense-pre-training design combines a pre-committed specification, inter-node network observations, and live Merkle commitments to intermediate computation. Our reference zero-knowledge virtual machine (zkVM) provides a succinct proof layer with native BF16/FP32 precompiles. We qualitatively compare zkVMs, custom circuits, and direct interactive audits by transferability, soundness, and cost. Public randomness beacons are a separate audit-selection choice. At challenged positions, proofs check the committed computation against declared BF16/FP32 semantics bit-for-bit. A private training specification targets model-architecture confidentiality. Genesis proofs cover initialisation; then, in-training step proofs verify selected steps across the run. Ex-ante attestations enforce policy-relevant claims as running invariants on every proved step. Together they create a governance-auditable training record. Detection-probability formulas quantify sampling guarantees from the sampling budget and the share of computation altered. We give numerical examples and explain what sampling does not cover. We note, however, that weight smuggling could reuse or merge weights trained elsewhere without charging that work to the current run. We then argue that regimes with global training budgets are more robust than regimes with per-run FLOP caps. This advantage depends on counting all contributing training. We catalogue remaining research and engineering problems for external contributors. Comments: 71 pages, 2 figures Subjects: Artificial Intelligence (cs.AI) ; Systems and Control (eess.SY) Cite as: arXiv:2606.05433 [cs.AI] (or arXiv:2606.05433v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2606.05433 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Paul Wang [ view email ] [v1] Wed, 3 Jun 2026 20:57:28 UTC (380 KB) [v2] Sat, 22 Aug 2026 05:46:47 UTC (381 KB) [v3] Wed, 21 Oct 2026 16:10:51 UTC (426 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-06 Change to browse by: cs cs.SY eess eess.SY References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• Zero knowledge verification for frontier AI training is possible#665796
• One Capability or Many? Testing the Economic Validity of Frontier AI Evaluation#622617
• Cooperative UAV swarms for zero knowledge verification of edge generative AI using trust-aware multiagent learning.#362046
• Contextual Multi-Objective Optimization: Rethinking Objectives in Frontier AI Systems#155333
• Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference#290628