Verifying AI: A Faster Path to Trustworthy Machine Learning

Author: Denis Avetisyan

New research introduces Jolt Atlas, a system designed to dramatically accelerate the process of verifying machine learning models using cryptographic techniques.

Jolt Atlas leverages ONNX, lookup arguments, and optimized polynomial commitments to achieve up to 17x speedups in verifiable inference for neural networks.

Existing approaches to zero-knowledge machine learning often struggle with computational overhead and scalability. This paper introduces ‘Jolt Atlas: Verifiable Inference via Lookup Arguments in Zero Knowledge’, a novel framework that enables efficient, verifiable inference by adapting lookup arguments to ONNX tensor operations. By leveraging polynomial commitments and optimizations-including neural teleportation-Jolt Atlas achieves up to 17x speedups compared to existing zkML systems while supporting streaming inference in resource-constrained environments. Could this represent a practical pathway towards deploying privacy-preserving and trustless AI applications on-device and at scale?

The Inevitable Demand for Algorithmic Transparency

The proliferation of machine learning extends far beyond recommendation systems and image recognition, now deeply impacting areas like financial lending, healthcare diagnostics, and even criminal justice. This increasing deployment in sensitive applications necessitates a heightened demand for trust and transparency; decisions made by these ‘black box’ algorithms directly affect individuals and require accountability. Without understanding how a model arrives at a specific conclusion, stakeholders – from patients to loan applicants – are left vulnerable, and the potential for bias or error remains largely unaddressed. Consequently, establishing verifiable and explainable machine learning systems is no longer simply a technical challenge, but a critical imperative for responsible innovation and societal acceptance.

Conventional machine learning, while powerful, often operates as a “black box,” presenting a significant obstacle to its implementation in high-stakes scenarios like healthcare, finance, and autonomous systems. The lack of transparency stems from the complex, non-linear nature of many algorithms, making it difficult to ascertain why a model arrived at a specific prediction. This limited verifiability creates challenges for regulatory compliance, safety certification, and building user trust; stakeholders require assurances that decisions are not based on spurious correlations or biases hidden within the model’s parameters. Consequently, the adoption of machine learning in these critical domains is often slowed or prevented, despite its potential benefits, as the inability to rigorously verify its behavior poses unacceptable risks.

Zero-Knowledge Machine Learning (zkML) represents a paradigm shift in how machine learning models are deployed, addressing growing concerns about trust and security. This innovative approach allows a computation – in this case, a machine learning inference – to be verified without revealing any information about the model itself or the input data. Utilizing cryptographic proofs, zkML demonstrates the correctness of a prediction without exposing the model’s parameters, architecture, or even the data used to generate it. This is achieved through techniques like succinct non-interactive arguments of knowledge (SNARKs) and zero-knowledge proofs, ensuring that a verifier can confidently accept a result without needing to trust the computational source. Consequently, zkML unlocks the potential for deploying powerful machine learning applications in sensitive areas like finance, healthcare, and privacy-preserving data analysis, where both accuracy and confidentiality are paramount.

Efficient Proof Systems: A Necessary Optimization

Lookup arguments represent a significant optimization in proof systems by reducing computational cost. Instead of directly proving the result of a complex calculation within a circuit, a lookup argument verifies that the result exists within a precomputed lookup table. This substitution transforms the proof task from evaluating the original function – which can be computationally intensive – to a simpler check of table membership. The efficiency gain stems from the fact that table lookups can be verified with significantly fewer operations than re-executing the original computation. These arguments typically rely on techniques like polynomial commitments to ensure the integrity of the lookup table and the validity of the lookup operation, effectively trading computation for precomputation and commitment overhead.

The Sumcheck protocol is a recursive proof system used to verify computations performed on a set of constraints, and is particularly efficient when combined with lookup arguments. Specifically, Sumcheck verifies that a solution satisfies a polynomial equation by iteratively reducing the verification problem to evaluating the polynomial at a randomly chosen point. This process is repeated for progressively simpler equations until a base case is reached. When used with lookup arguments, Sumcheck efficiently verifies the consistency of table lookups, ensuring the retrieved values are valid according to the underlying computation. The efficiency gain stems from Sumcheck’s ability to transform the verification of a complex lookup into a series of simpler polynomial evaluations, significantly reducing the overall proof size and verification time compared to directly verifying the original computation.

Zero-knowledge virtual machines (zkVMs), such as Jolt, utilize lookup arguments and the Sumcheck protocol to significantly improve proof generation performance for general-purpose computations. These systems represent computations as arithmetic circuits, then employ lookup arguments to replace expensive circuit operations with simple table lookups. The Sumcheck protocol efficiently verifies the consistency of these lookups, reducing the computational cost of proof verification and generation. By integrating these techniques, zkVMs achieve state-of-the-art proving times and reduced proof sizes compared to traditional methods, enabling practical applications of zero-knowledge proofs in areas like scaling blockchains and private machine learning.

JoltAtlas: Scaling Zero-Knowledge to Deep Learning

JoltAtlas adapts the Jolt framework to accommodate machine learning operations by substituting standard RISC-V instructions with Open Neural Network Exchange (ONNX) computational graphs. This substitution allows for the execution of complex machine learning models within the Jolt environment. Specifically, ONNX graphs representing model layers and operations are compiled and executed directly on the Jolt virtual machine, effectively leveraging its cryptographic properties for machine learning inference. This approach enables a transition from general-purpose computation to specialized machine learning tasks without requiring a complete overhaul of the underlying Jolt architecture.

JoltAtlas utilizes Virtual Polynomials to mitigate the computational expense associated with polynomial commitment schemes. Traditional commitment schemes require commitments for every layer in a neural network, incurring significant overhead. Virtual Polynomials enable the aggregation of multiple layers into a single commitment, effectively amortizing the commitment cost across a larger computational span. This is achieved by representing the computation of multiple layers as a single, larger polynomial, reducing the number of commitments required and thereby decreasing both computational cost and proof size. Consequently, the overall system performance is improved, and scalability is enhanced for deep learning workloads.

JoltAtlas incorporates lattice-based polynomial commitment schemes, notably Hachi, to address vulnerabilities posed by advancements in quantum computing. Traditional cryptographic methods are susceptible to attacks from quantum algorithms, such as Shor’s algorithm, which can compromise the integrity of zero-knowledge proofs. Lattice-based cryptography, however, relies on the hardness of problems in lattice spaces, offering a potential path to post-quantum security. By utilizing Hachi, JoltAtlas establishes a commitment scheme where proving relationships between polynomials requires only a small, constant-size proof, regardless of the polynomial degree. This approach ensures the continued validity and security of zkML computations even in the face of future quantum cryptographic threats, providing long-term resilience for sensitive machine learning applications.

JoltAtlas incorporates support for a range of commonly used activation functions crucial for building diverse deep learning models. Specifically, the system natively implements Rectified Linear Unit (ReLU), Sigmoid, Softmax, Hyperbolic Tangent (Tanh), and Error Function (Erf) activations. This allows developers to directly translate existing machine learning models, expressed using these standard functions, into the JoltAtlas framework without requiring functional approximations or modifications. The inclusion of these functions ensures broad compatibility with a variety of neural network architectures and facilitates the deployment of complex machine learning workloads within the zkML system.

Empirical Validation: Performance with Standard Models

JoltAtlas is designed to be compatible with deep learning models represented in the Open Neural Network Exchange (ONNX) format, a widely adopted standard for interoperability between different machine learning frameworks. This allows users to readily deploy existing, pre-trained models without requiring code modifications or framework-specific adaptations. The system parses and processes ONNX graphs, enabling the application of its optimizations and cryptographic techniques to a broad range of deep learning architectures. Support for the ONNX format is a core design principle, facilitating integration with the existing machine learning ecosystem and streamlining the deployment of verifiable machine learning solutions.

JoltAtlas incorporates TeleSparse optimization techniques to minimize the storage requirements of lookup tables used in circuit construction. Lookup tables, essential for representing non-linear operations within the neural network, can contribute significantly to the overall circuit size and computational cost. TeleSparse reduces redundancy within these tables by intelligently pruning and compressing data, effectively decreasing the memory footprint and improving both proof generation speed and efficiency. This optimization is particularly beneficial for large models where the size of lookup tables can become a limiting factor in verifiable machine learning applications.

The JoltAtlas architecture has been validated through implementation with nanoGPT and GPT-2 models, establishing concrete performance baselines. These models were selected to represent a range of common deep learning workloads and facilitate comparative analysis. Utilizing these established models allows for quantifiable evaluation of JoltAtlas’s capabilities in a practical context, demonstrating its compatibility with prevalent neural network structures and providing a foundation for benchmarking against other verifiable machine learning systems. The results obtained with nanoGPT and GPT-2 serve as representative data points for assessing the system’s overall efficiency and scalability.

Within JoltAtlas’ lattice-based commitment schemes, ABBA (Additive Blinding and Batching Algorithm) is implemented to minimize the size of commitments. Commitment schemes are crucial for concealing values while allowing verification of their correctness; however, large commitment sizes can impact performance and scalability. ABBA achieves size reduction by additively blinding individual commitments before batching them together. This technique leverages homomorphic properties, allowing verification to be performed on the blinded, batched commitment without revealing the underlying values, and ultimately decreasing the overall data that needs to be processed and stored.

Benchmarking demonstrates Jolt Atlas achieves a roughly 17x speed-up in proof generation for the nanoGPT model when compared to the ezkl framework. Specifically, Jolt Atlas completes nanoGPT proof generation in approximately 14 seconds, whereas ezkl requires approximately 237 seconds for the same task. This substantial reduction in processing time indicates significant improvements in the performance of verifiable machine learning workflows utilizing Jolt Atlas, offering a practical pathway to faster and more efficient deployment of privacy-preserving machine learning applications.

Benchmarking demonstrates that Jolt Atlas achieves a proof generation time of approximately 14 seconds for the nanoGPT model. This represents a substantial performance improvement when contrasted with the ezkl framework, which requires approximately 237 seconds to generate proof for the same model. This timing data highlights Jolt Atlas’s efficiency in verifiable machine learning applications and indicates a roughly 17x speed-up in proof generation for nanoGPT compared to ezkl.

Toward a Future Defined by Trustworthy Intelligence

Zero-Knowledge Machine Learning, or zkML, represents a paradigm shift in artificial intelligence, offering the potential to unlock secure and transparent AI applications across numerous fields. Systems like JoltAtlas are at the forefront of this revolution, enabling models to demonstrate their knowledge and make predictions without revealing the underlying data or model parameters. This capability is particularly impactful in sensitive areas such as healthcare, finance, and national security, where data privacy and algorithmic accountability are paramount. By leveraging cryptographic techniques, zkML facilitates verification of AI outputs, fostering trust and enabling deployment in scenarios where traditional machine learning would be impractical or unacceptable. The implications extend beyond simply protecting data; zkML allows for collaborative AI development where models can be shared and improved without compromising intellectual property, and for the creation of verifiable credentials and decentralized autonomous organizations powered by trustworthy AI systems.

The looming threat of quantum computers necessitates a proactive shift in cryptographic strategies for machine learning, and lattice-based cryptography presents a compelling solution. Unlike many current public-key systems vulnerable to Shor’s algorithm, lattice-based schemes rely on the hardness of problems in lattice spaces – highly structured arrangements of points in space. This foundation provides inherent resistance to known quantum attacks, promising a pathway to ‘post-quantum security’. When coupled with efficient proof systems, these cryptographic techniques enable verification of machine learning computations without revealing the underlying data or model, ensuring both security and trustworthiness. The result is a future where AI systems remain secure even in the face of increasingly powerful computational adversaries, safeguarding sensitive information and maintaining the integrity of critical applications.

The pursuit of faster, more efficient verifiable AI heavily relies on advancements in commitment schemes, and current research is intensely focused on optimizing these foundational components. These schemes allow a machine learning model’s computations to be verified without revealing the underlying data or model itself, but traditionally involve significant computational overhead. Researchers are actively exploring novel approaches, such as Hachi, and benchmarking them against established schemes like Greyhound, to identify performance bottlenecks and unlock substantial speed improvements. This iterative process of design, implementation, and comparative analysis is crucial, as even incremental gains in commitment scheme efficiency directly translate to more practical and scalable applications of verifiable AI – ultimately enabling broader deployment across sensitive domains like finance and healthcare.

Within the evolving landscape of verifiable AI, the architecture incorporates BlindFold to establish a robust framework for zero-knowledge proofs, fundamentally safeguarding both privacy and confidentiality. This innovative approach allows for the validation of machine learning computations without revealing the underlying data or model parameters. By concealing sensitive information during processing, BlindFold enables secure AI applications in domains where data privacy is paramount, such as healthcare and finance. The system operates by effectively ‘blinding’ the inputs and intermediate results, ensuring that only the final, verified output is exposed. This capability is crucial for building trust and fostering wider adoption of AI technologies, as it addresses growing concerns surrounding data security and algorithmic transparency – a significant step toward responsible and ethical AI development.

The pursuit of efficient verifiable computation, as demonstrated by Jolt Atlas, echoes a fundamental truth about complex systems. While the paper details optimizations to polynomial commitment schemes and leverages ONNX for speedups-achieving up to 17x performance gains-it ultimately addresses the inevitable decay of computational integrity over time. As Marvin Minsky observed, “The question isn’t what computers can do, but what they should do.” Jolt Atlas doesn’t halt this decay, but attempts to delay it gracefully through innovative techniques. The system represents a temporary bulwark against entropy, a carefully constructed stabilization against the eventual unraveling inherent in all processes. Sometimes stability is just a delay of disaster, and Jolt Atlas buys valuable time in the realm of zero-knowledge machine learning.

What Lies Ahead?

Jolt Atlas represents a localized victory in the ongoing contest against computational entropy. The speedups achieved through optimized polynomial commitments and the strategic use of lookup arguments are not, however, a negation of decay, but rather a temporary deferral. Every optimization introduces a new surface for brittleness, a new point of failure as the landscape of cryptographic assumptions shifts. The system’s reliance on ONNX, while practical, binds it to the fortunes-and limitations-of that particular model representation.

Future work will inevitably confront the tension between expressive power and verifiable efficiency. The current approach, like all constructions built upon polynomial commitments, faces scaling challenges as model complexity increases. The true measure of progress will not be incremental speedups, but the development of techniques that fundamentally alter the cost function of verification-perhaps by embracing alternative approaches to zero-knowledge proofs, or by accepting a carefully calibrated degree of probabilistic error.

Ultimately, the pursuit of verifiable machine learning is an exercise in managing technical debt. Each layer of abstraction, each optimization, adds to the accumulated cost of maintaining integrity. The goal, then, is not to eliminate this debt entirely-an impossible task-but to ensure that it ages gracefully, yielding a system that can withstand the inevitable currents of change.

Original article: https://arxiv.org/pdf/2602.17452.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/