We present Jolt Atlas, a zero-knowledge machine learning (zkML) framework that extends the Jolt proving system to model inference. Unlike zkVMs (zero-knowledge virtual machines), which emulate CPU instruction execution, Jolt Atlas adapts Jolt's lookup-centric approach and applies it directly to ONNX tensor operations. The ONNX computational model eliminates the need for CPU registers and simplifies memory consistency verification. In addition, ONNX is an open-source, portable format, which makes it easy to share and deploy models across different frameworks, hardware platforms, and runtime environments without requiring framework-specific conversions. Our lookup arguments, which use sumcheck protocol, are well-suited for non-linear functions -- key building blocks in modern ML. We apply optimisations such as neural teleportation to reduce the size of lookup tables while preserving model accuracy, as well as several tensor-level verification optimisations detailed in this paper. We demonstrate that Jolt Atlas can prove model inference in memory-constrained environments -- a prover property commonly referred to as \textit{streaming}. Furthermore, we discuss how Jolt Atlas achieves zero-knowledge through the BlindFold technique, as introduced in Vega. In contrast to existing zkML frameworks, we show practical proving times for classification, embedding, automated reasoning, and small language models. Jolt Atlas enables cryptographic verification that can be run on-device, without specialised hardware. The resulting proofs are succinctly verifiable. This makes Jolt Atlas well-suited for privacy-centric and adversarial environments. In a companion work, we outline various use cases of Jolt Atlas, including how it serves as guardrails in agentic commerce and for trustless AI context (often referred to as \textit{AI memory}).

翻译：本文提出Jolt Atlas，一个将Jolt证明系统扩展至模型推理的零知识机器学习（zkML）框架。与模拟CPU指令执行的零知识虚拟机（zkVM）不同，Jolt Atlas继承了Jolt以查找为核心的设计思路，并将其直接应用于ONNX张量运算。ONNX计算模型无需CPU寄存器支持，并简化了内存一致性验证。此外，ONNX作为开源可移植格式，无需框架特定转换即可在不同框架、硬件平台和运行时环境中轻松共享与部署模型。我们基于和校验协议构建的查找论证特别适用于非线性函数——现代机器学习的关键组成部分。通过神经传送等优化技术，我们在保持模型精度的同时缩减了查找表规模，并实现了本文详述的多项张量级验证优化。实验表明，Jolt Atlas能在内存受限环境中完成模型推理证明——这种证明者特性通常被称为\textit{流式处理}。此外，我们阐述了Jolt Atlas如何通过Vega论文提出的BlindFold技术实现零知识特性。相较于现有zkML框架，我们在分类、嵌入、自动推理及小型语言模型等任务中展现了实用的证明耗时。Jolt Atlas支持在无专用硬件设备上运行密码学验证，生成的证明具备简洁可验证性，使其特别适用于注重隐私及对抗性环境。在配套研究中，我们概述了Jolt Atlas的多种应用场景，包括其在智能体商业中的护栏作用，以及为无需信任的AI上下文（常称为\textit{AI记忆}）提供支持。