用AI代理系统实现粒子加速器自主控制，提升复杂设备运行效率。
Towards Agentic AI on Particle Accelerators
- 构建基于大模型的分布式智能代理框架，分工负责加速器各部件
- 通过人类反馈与经验积累，实现系统持续自我优化
- 适合加速器控制、智能运维领域研究者参考
随着粒子加速器复杂度提升，传统控制方法在实现最优性能方面面临越来越多挑战。本文提出一种范式转变：采用由大语言模型驱动、分布于各组件的去中心化多智能体框架进行加速器控制。该系统中，智能代理负责高层任务与通信，每个代理专门控制一个加速器部件，形成可自改进的分布式架构。我们探讨了人工智能在加速器中的未来应用、如何实现具备逐步学习能力的自主复杂系统，以及人机协同标注数据与提供专家指导的意义。通过三个实例验证了该架构的可行性。
原文摘要 · Abstract (English)
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.
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