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GAIA: A General AI Assistant for Intelligent Accelerator Operations

Frank Mayet

TL;DR

GAIA demonstrates a practical framework for assisting accelerator operators by marrying ReAct-inspired prompting with a multi-expert tool suite. By integrating an open-weights LLM, a high-level control framework, and knowledge bases, the system can generate executable scripts, retrieve contextual information, and interact with the machine safely. The approach leverages RAG and tool-use to overcome context limits and coordinate expertise across subsystems, offering a scalable path to faster, more reliable operations. The work highlights both the potential and the challenges of real-world deployment, including compute efficiency and the need for metrics and multimodal data handling.

Abstract

Large-scale machines like particle accelerators are usually run by a team of experienced operators. In case of a particle accelerator, these operators possess suitable background knowledge on both accelerator physics and the technology comprising the machine. Due to the complexity of the machine, particular subsystems of the machine are taken care of by experts, who the operators can turn to. In this work the reasoning and action (ReAct) prompting paradigm is used to couple an open-weights large language model (LLM) with a high-level machine control system framework and other tools, e.g. the electronic logbook or machine design documentation. By doing so, a multi-expert retrieval augmented generation (RAG) system is implemented, which assists operators in knowledge retrieval tasks, interacts with the machine directly if needed, or writes high level control system scripts. This consolidation of expert knowledge and machine interaction can simplify and speed up machine operation tasks for both new and experienced human operators.

GAIA: A General AI Assistant for Intelligent Accelerator Operations

TL;DR

GAIA demonstrates a practical framework for assisting accelerator operators by marrying ReAct-inspired prompting with a multi-expert tool suite. By integrating an open-weights LLM, a high-level control framework, and knowledge bases, the system can generate executable scripts, retrieve contextual information, and interact with the machine safely. The approach leverages RAG and tool-use to overcome context limits and coordinate expertise across subsystems, offering a scalable path to faster, more reliable operations. The work highlights both the potential and the challenges of real-world deployment, including compute efficiency and the need for metrics and multimodal data handling.

Abstract

Large-scale machines like particle accelerators are usually run by a team of experienced operators. In case of a particle accelerator, these operators possess suitable background knowledge on both accelerator physics and the technology comprising the machine. Due to the complexity of the machine, particular subsystems of the machine are taken care of by experts, who the operators can turn to. In this work the reasoning and action (ReAct) prompting paradigm is used to couple an open-weights large language model (LLM) with a high-level machine control system framework and other tools, e.g. the electronic logbook or machine design documentation. By doing so, a multi-expert retrieval augmented generation (RAG) system is implemented, which assists operators in knowledge retrieval tasks, interacts with the machine directly if needed, or writes high level control system scripts. This consolidation of expert knowledge and machine interaction can simplify and speed up machine operation tasks for both new and experienced human operators.
Paper Structure (5 sections, 7 figures)

This paper contains 5 sections, 7 figures.

Table of Contents

  1. Introduction
  2. Background – Accelerator Controls
  3. Multi-Expert System – GAIA
  4. Examples
  5. Conclusions

Figures (7)

  • Figure 1: A selection of procedures and actions available via the doocs_generic_experiment Python module.
  • Figure 2: A selection of tools to be used by GAIA.
  • Figure 3: Example: "Can you summarize the last operations meeting?". Names are redacted.
  • Figure 4: Example: "I want to write values to multiple devices in parallel using doocs_generic_experiment. How do I do this?".
  • Figure 5: Example: "Did they manage to define the new hexapod parking position today?". Names are redacted.
  • ...and 2 more figures