Towards Agentic AI on Particle Accelerators
This work addresses the problem of optimizing performance in particle accelerators for researchers and engineers, but it is incremental as it builds on existing AI and multi-agent concepts.
The paper tackles the challenge of controlling increasingly complex particle accelerators by proposing a decentralized multi-agent framework powered by Large Language Models, demonstrating its viability through three examples.
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.