JOLT Atlas

JOLT Atlas is a zero-knowledge machine learning (zkML) framework that extends the JOLT proving system to support ML inference verification from ONNX models.

Made with ❤️ by ICME Labs.

Overview

JOLT Atlas enables practical zero-knowledge machine learning by leveraging Just One Lookup Table (JOLT) technology. Traditional circuit-based approaches are prohibitively expensive when representing non-linear functions like ReLU and SoftMax. Lookups eliminate the need for circuit representation entirely.

In JOLT Atlas, we eliminate the complexity that plagues other approaches: no quotient polynomials, no byte decomposition, no grand products, no permutation checks, and most importantly — no complicated circuits.

Examples

The examples/ directory contains practical demonstrations of zkML models:

Article Classification

A text classification model that categorizes articles into business, tech, sport, entertainment, and politics.

cargo run --release --example article_classification

This example:

• Tests model accuracy on sample texts
• Generates a SNARK proof for one classification
• Verifies the proof cryptographically

Transaction Authorization

A financial transaction authorization model that decides whether to approve or deny transactions based on features like budget, trust score, amount, etc.

cargo run --release --example authorization

This example:

• Tests the model on various transaction scenarios
• Shows authorization decisions with confidence scores
• Generates and verifies a SNARK proof for one transaction

Benchmarks

Transformer (self-attention) profile

Latest run (cargo run -r -- profile --name self-attention --format default):

Stage	Wall clock
Prove	20.8 s
Verify	143 ms
End-to-end CLI run	25.8 s

The prover hit a peak allocated footprint of roughly 5.6 GB during sumcheck round 10, which matches what we have seen in the integration test harness. Numbers were collected from this workstation; expect ±10% variance depending on CPU, memory bandwidth.

Cross-project snapshot

Article-classification workload comparison

Project	Latency	Notes
zkml-jolt	~0.7s	in-tree article-classification bench
mina-zkml	~2.0s
ezkl	4–5s
deep-prove	N/A	missing gather primitive
zk-torch	N/A	missing reduceSum primitive

Perceptron MLP baseline (easy sanity workload):

Project	Latency	Notes
zkml-jolt	~800ms
deep-prove	~200ms	lacks MCC

How to reproduce locally

# from repo root
cd zkml-jolt-core

cargo run -r -- profile --name article-classification --format default
cargo run -r -- profile --name self-attention --format default
cargo run -r -- profile --name mlp --format default

Add --format chrome if you want a tracing JSON for Chrome's chrome://tracing viewer instead of plain-text timings.

Getting Started

1. Clone the repository
2. Install Rust and Cargo
3. Run the examples:

   cargo run --example article_classification
   cargo run --example authorization

Acknowledgments

Thanks to the Jolt team for their foundational work. We are standing on the shoulders of giants.