Knowledge is Overrated: A zero-knowledge machine learning and cryptographic hashing-based framework for verifiable, low latency inference at the LHC
This addresses the critical need for low-latency inference in LHC operations, enabling improved trigger performance for physics analyses, though it appears incremental as it builds on existing cryptographic and ML techniques.
The paper tackles the problem of high latency in machine learning models for event-selection triggers at the Large Hadron Collider, proposing a framework called PHAZE that uses cryptographic hashing and zero-knowledge machine learning to achieve nanosecond-order latency with a certifiable early-exit mechanism.
Low latency event-selection (trigger) algorithms are essential components of Large Hadron Collider (LHC) operation. Modern machine learning (ML) models have shown great offline performance as classifiers and could improve trigger performance, thereby improving downstream physics analyses. However, inference on such large models does not satisfy the $40\text{MHz}$ online latency constraint at the LHC. In this work, we propose \texttt{PHAZE}, a novel framework built on cryptographic techniques like hashing and zero-knowledge machine learning (zkML) to achieve low latency inference, via a certifiable, early-exit mechanism from an arbitrarily large baseline model. We lay the foundations for such a framework to achieve nanosecond-order latency and discuss its inherent advantages, such as built-in anomaly detection, within the scope of LHC triggers, as well as its potential to enable a dynamic low-level trigger in the future.