arXiv AI July 21, 2026 By Zhaohui Wang

NanoZK: Privacy-Preserving Verifiable Inference for Large Language Models via Layerwise Zero-Knowledge Proofs

arXiv:2603. 18046v2 Announce Type: replace-cross Abstract: We present NanoZK, a zero-knowledge proof system for verifiable LLM inference: clients and third-party auditors check that a provider executed the advertised model on a committed input without learning weights or activations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 30

Towards Verifiable Transformers: Solver-Checkable Circuit Explanations

arXiv:2605. 24033v2 Announce Type: replace Abstract: Mechanistic interpretability typically discovers circuits and then argues what they do from examples and ablations.

By Neel Somani

llms rag safety

arXiv AI

Jun 6

Zero knowledge verification for frontier AI training is possible

arXiv:2606. 05433v1 Announce Type: new Abstract: Frontier AI governance frameworks increasingly use cumulative training compute as the primary criterion for designating high-impact models, but enforcement rests on self-reporting because no technical verification primitive for training exists.

By Pierre Peign\'e, Ky Nguyen, Paul Wang

arXiv AI

Aug 5

Privacy-Preserving AI Verification via Minimal Information Disclosure

arXiv:2608. 02774v1 Announce Type: cross Abstract: AI verification crosses a trust boundary: a verifier must learn enough to establish an authorized claim, yet the same evidence can reveal sensitive details about the model, workload, or hardware.

By Sleem Abdelghafar, Gabriel Kulp

safety

arXiv AI

Jun 16

Communication-Efficient Verifiable Attention for LLM Inference

arXiv:2606. 16352v1 Announce Type: cross Abstract: Computation integrity of remote large language model (LLM) serving can be questionable.

By Ziqun Chen, Ming Wu, Michael Heinrich, Jason Zeng, Huiying Lan, Tianwei Zhang, Rui Tan

llms

arXiv AI

Aug 10

Policy-Masked Private Experts: Auditable and Reversible Capability Access Control in Sparse MoE Models

arXiv:2608. 06690v1 Announce Type: cross Abstract: Most language-model access controls regulate behavior while leaving the same computation available to every request.

By Zhuoheng Huang, Mukesh Singh

agents fine-tuning benchmarks safety

arXiv AI

Jun 9

TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

arXiv:2510. 16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces).

By Jianzhu Yao, Hongxu Su, Taobo Liao, Zerui Cheng, Huan Zhang, Xuechao Wang, Pramod Viswanath

llms rag diffusion efficiency safety

Related stories

Towards Verifiable Transformers: Solver-Checkable Circuit Explanations

Zero knowledge verification for frontier AI training is possible

Privacy-Preserving AI Verification via Minimal Information Disclosure

Communication-Efficient Verifiable Attention for LLM Inference

Policy-Masked Private Experts: Auditable and Reversible Capability Access Control in Sparse MoE Models

TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks