Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator
arXiv:2509.17255v2 Announce Type: replace-cross Abstract: 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 preparat
Advances in large language models and reinforcement learning have reached a point where AI systems can interpret natural language and autonomously execute complex, multi-stage physical experiments.
This demonstration marks a significant step towards fully autonomous scientific discovery, potentially accelerating research timelines and reducing human intervention in hazardous or complex experimental environments.
Scientific experimentation can now be driven by natural language prompts and executed by AI, automating not just data collection but also experimental design, control, and initial analysis.
- · Scientific research institutions
- · Particle accelerator facilities
- · AI agents developers
- · Materials science
- · Manual experimentalists
- · Traditional research methods
- · Data analysis software requiring explicit human command chains
Increased pace of scientific discovery in fields utilizing large-scale experimental facilities.
Development of more sophisticated AI 'scientist' agents capable of hypothesis generation and iterative experimental design.
Potential for entirely new scientific discoveries enabled by AI exploring experimental spaces too vast or complex for human intuition.
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Read at arXiv cs.AI