Towards Agentic AI on Particle Accelerators
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.
Cite
@article{arxiv.2409.06336,
title = {Towards Agentic AI on Particle Accelerators},
author = {Antonin Sulc and Thorsten Hellert and Raimund Kammering and Hayden Hoschouer and Jason St. John},
journal= {arXiv preprint arXiv:2409.06336},
year = {2025}
}
Comments
5 pages, 3 figures, Machine Learning and the Physical Sciences at Workshop at the 38th conference on Neural Information Processing Systems (NeurIPS)