Knowledge is Overrated: A zero-knowledge machine learning and cryptographic hashing-based framework for verifiable, low latency inference at the LHC

2025-11-16

AI Summary

To address the challenge of achieving high-precision real-time event selection in the LHC trigger system under a strict 40 MHz (25 ns/event) latency constraint, this paper proposes a verifiable early-exit inference framework integrating zero-knowledge machine learning (zkML) with cryptographic hashing. The method introduces a lightweight certified early-exit mechanism that reduces per-inference latency to the nanosecond scale while preserving model classification accuracy; it incorporates an online anomaly detection module enabling real-time, trustable verification. This approach overcomes the fundamental bottleneck preventing conventional ML models from deployment in ultra-low-latency hardware triggers. Experimental results demonstrate that the framework achieves feasible deployment of large-scale models at the LHC online trigger level—without compromising offline performance—for the first time. It thus provides a key technical foundation for next-generation, dynamic, and verifiable low-level triggers.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 $40 ext{MHz}$ online latency constraint at the LHC. In this work, we propose exttt{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.

Problem

• Achieving low-latency ML inference under LHC's 40MHz constraint
• Enabling verifiable early-exit from large baseline ML models
• Implementing cryptographic hashing and zkML for trigger systems

Innovation

• Zero-knowledge machine learning for verifiable inference
• Cryptographic hashing enables nanosecond-order latency
• Certifiable early-exit mechanism from large baseline models