Towards Agentic AI on Particle Accelerators

TL;DR

This paper proposes a decentralized multi-agent AI framework utilizing Large Language Models for autonomous control and optimization of complex particle accelerators, emphasizing self-improvement and human-in-the-loop integration.

Contribution

It introduces a novel multi-agent system architecture for particle accelerators that leverages LLMs and autonomous agents for improved control and adaptability.

Findings

Demonstrated viability of multi-agent control architecture

Showcased potential for self-improvement through experience

Illustrated integration of human feedback for system guidance

Abstract

As particle accelerators grow in complexity, traditional control methods face increasing challenges in achieving optimal performance. This paper envisions a paradigm shift: a decentralized multi-agent framework for accelerator control, powered by Large Language Models (LLMs) and distributed among autonomous agents. We present a proposition of a self-improving decentralized system where intelligent agents handle high-level tasks and communication and each agent is specialized to control individual accelerator components. This approach raises some questions: What are the future applications of AI in particle accelerators? How can we implement an autonomous complex system such as a particle accelerator where agents gradually improve through experience and human feedback? What are the implications of integrating a human-in-the-loop component for labeling operational data and providing expert guidance? We show three examples, where we demonstrate the viability of such architecture.