# GAIA: A General AI Assistant for Intelligent Accelerator Operations

arXiv: https://arxiv.org/abs/2405.01359 · HTML: https://mlsanity.com/paper/2405.01359/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 3 of 3)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)

## Built on by (most cited first, 8 of 8)

- [Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator](https://mlsanity.com/paper/2509.17255/) (2025)
- [Towards Unlocking Insights from Logbooks Using AI](https://mlsanity.com/paper/2406.12881/) (2024)
- [Towards Agentic AI on Particle Accelerators](https://mlsanity.com/paper/2409.06336/) (2024)
- [eLog analysis for accelerators: status and future outlook](https://mlsanity.com/paper/2506.12949/) (2025)
- [Application Of Large Language Models For The Extraction Of Information From Particle Accelerator Technical Documentation](https://mlsanity.com/paper/2509.02227/) (2025)
- [Archi: Agentic Operations at the CMS Experiment](https://mlsanity.com/paper/2606.04755/) (2026)
- [A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility](https://mlsanity.com/paper/2607.24663/) (2026)
- [Autonomous discovery of accelerator commissioning algorithms](https://mlsanity.com/paper/2608.07138/) (2026)

## Abstract (arXiv)

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.