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Towards Agentic AI on Particle Accelerators

Created by
  • Haebom

Author

Antonin Sulc, Thorsten Hellert, Raimund Kammering, Hayden Hoschouer, Jason St. John

Outline

To overcome the limitations of existing centralized approaches for particle accelerator control, this paper presents a distributed multi-agent framework based on a large-scale language model (LLM). Each agent controls an individual component of the accelerator, communicates with each other, and handles high-level tasks. The system aims to be self-improving, improving through experience and human feedback, emphasizing the importance of data labeling and expert guidance through human intervention. Three examples demonstrate the feasibility of the proposed architecture.

Takeaways, Limitations

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Takeaways:
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A new paradigm for particle accelerator control: Efficient control and optimization potential through distributed multi-agent systems.
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Presenting the possibility of building an intelligent control system based on LLM.
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Potential for improved system performance and reduced maintenance costs through self-learning and improvement capabilities.
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The potential to increase the efficiency of system operation through human-machine collaboration.
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Limitations:
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Further research is needed on the practical implementation and stability of the proposed system.
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The need to address the issues of reliability and unpredictability of LLM.
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There is a need to develop efficient communication mechanisms between distributed systems and agents.
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There is a need to develop clear protocols and guidelines for human intervention.
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Challenges of collecting and labeling large-scale data.
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