# Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator

arXiv: https://arxiv.org/abs/2509.17255 · HTML: https://mlsanity.com/paper/2509.17255/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 21 Sep 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 9 of 9)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [CRISPR-GPT for Agentic Automation of Gene-editing Experiments](https://mlsanity.com/paper/2404.18021/) (2024)
- [VISION: A Modular AI Assistant for Natural Human-Instrument Interaction at Scientific User Facilities](https://mlsanity.com/paper/2412.18161/) (2024)
- [Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language](https://mlsanity.com/paper/2405.08888/) (2024)
- [GAIA: A General AI Assistant for Intelligent Accelerator Operations](https://mlsanity.com/paper/2405.01359/) (2024)
- [Towards Agentic AI on Particle Accelerators](https://mlsanity.com/paper/2409.06336/) (2024)

## Built on by (most cited first, 6 of 6)

- [GRACE: an Agentic AI for Particle Physics Experiment Design and Simulation](https://mlsanity.com/paper/2602.15039/) (2026)
- [From Natural Language to Control Signals: A Conceptual Framework for Semantic Channel Finding in Complex Experimental Infrastructure](https://mlsanity.com/paper/2512.18779/) (2025)
- [Plausible but Wrong: A case study on Agentic Failures in Astrophysical Workflows](https://mlsanity.com/paper/2604.25345/) (2026)
- [It's not the Language Model, it's the Tool: Deterministic Mediation for Scientific Workflows](https://mlsanity.com/paper/2605.13245/) (2026)
- [A corrective agentic hybrid RAG and an operations-grounded evaluation for a scientific facility](https://mlsanity.com/paper/2607.24663/) (2026)
- [Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery](https://mlsanity.com/paper/2610.12293/) (2026)

## Abstract (arXiv)

We present the first language-model-driven agentic artificial intelligence (AI) system to autonomously execute multi-stage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by two orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.