Towards Agentic AI on Particle Accelerators

2024-09-10

arXiv.org

AI Summary

To address the growing complexity of particle accelerator control, this paper proposes an LLM-driven decentralized multi-agent control system. Methodologically, it introduces a subsystem-specific agent architecture leveraging large language models (LLMs) for high-level decision-making, cross-agent coordination, and continuous autonomous optimization—integrated for the first time with a human-in-the-loop feedback loop and a progressive self-improvement mechanism. Key contributions include: (1) the first LLM-based multi-agent system (LLM-MAS) framework tailored specifically for accelerator control; and (2) a hybrid learning paradigm enabling active operational data annotation and expert knowledge injection. Experimental validation across three representative control scenarios demonstrates substantial improvements in task response flexibility and environmental adaptability. The system establishes a novel paradigm for next-generation intelligent accelerator control—characterized by high reliability, evolutionary capability, and scalable autonomy.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Decentralized multi-agent control for complex particle accelerators
• Autonomous system improvement via experience and human feedback
• Integrating human expertise for operational data labeling and guidance

Innovation

• Decentralized multi-agent framework for control
• Large Language Models power autonomous agents
• Self-improving system with human feedback integration