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[2602.17452] Jolt Atlas: Verifiable Inference via Lookup Arguments in Zero Knowledge
Summary
Jolt Atlas introduces a zero-knowledge machine learning framework that enhances inference verification through lookup arguments, optimizing for memory-constrained environments.
Why It Matters
This framework addresses the growing need for privacy and security in machine learning applications, particularly in adversarial contexts. By enabling verifiable inference without specialized hardware, Jolt Atlas could significantly impact trust in AI systems and their deployment across various platforms.
Key Takeaways
- Jolt Atlas utilizes a lookup-centric approach for zero-knowledge inference.
- It simplifies memory consistency verification using the ONNX computational model.
- The framework is optimized for non-linear functions, crucial for modern machine learning.
- It enables cryptographic verification that can be executed on-device.
- Jolt Atlas is designed for privacy-centric applications and can operate in memory-constrained environments.
Computer Science > Cryptography and Security arXiv:2602.17452 (cs) [Submitted on 19 Feb 2026] Title:Jolt Atlas: Verifiable Inference via Lookup Arguments in Zero Knowledge Authors:Wyatt Benno, Alberto Centelles, Antoine Douchet, Khalil Gibran View a PDF of the paper titled Jolt Atlas: Verifiable Inference via Lookup Arguments in Zero Knowledge, by Wyatt Benno and 3 other authors View PDF HTML (experimental) Abstract: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 envir...
