arXiv AI
zkLLMPoT: Efficient Zero Knowledge Proof of Training for Large Language Models
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Proposals and challenges for zero-knowledge verification of frontier AI training
The paper proposes a zero‑knowledge verification framework for frontier AI training, combining pre‑committed specifications, network observations, and live Merkle commitments to create a succinct proof layer via a zero‑knowledge virtual machine (zkVM). It outlines how proofs can verify BF16/FP32 semantics step‑by‑step, enforce policy‑relevant claims, and provide a governance‑auditable training record, while also discussing sampling guarantees, weight smuggling risks, and the relative robustness of global training budgets versus per‑run FLOP caps. The authors compare zkVMs, custom circuits, and interactive audits, and identify remaining research and engineering challenges for external contributors.
arXiv Machine Learning
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OPEN-1B: A Fully Auditable Training Run
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arXiv Machine Learning
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Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification
arXiv:2607. 21839v1 Announce Type: cross Abstract: Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing their internals or training data.
arXiv AI
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NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs
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arXiv Machine Learning
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Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates
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arXiv Machine Learning
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Not to Break, but to Attest: Adversarial Probes for Privacy-Preserving LLM Verification
The paper introduces a privacy‑preserving zk‑SNARK audit framework that uses adversarial‑style probes to detect logit drift between an approved large language model and a modified deployment. It offers three probe families—token‑based (black‑box), embedding‑based (gray‑box), and stress probes (partial white‑box)—allowing users to balance sensitivity, access, and cost. Experiments across LLM architectures and GPU platforms show token‑based probes achieve the highest mean sensitivity while remaining practical in a black‑box setting, with Groth16 proving times scaling modestly from 1.02 to 1.78 seconds and constant proof size.