Towards Agentic AI on Particle Accelerators
<|reference_start|>Towards Agentic AI on Particle Accelerators: 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 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 two examples, where we demonstrate viability of such architecture.<|reference_end|>
@article{sulc2024towards,
    title={Towards Agentic AI on Particle Accelerators},
    author={Antonin Sulc, Thorsten Hellert, Raimund Kammering, Hayden Houscher,
  Jason St. John},
    journal={arXiv preprint arXiv:2409.06336},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06336},
    primaryClass={physics.acc-ph cs.AI}
}