AI Agents Can Already Autonomously Perform Experimental High Energy Physics
Large language model-based AI agents are now able to autonomously execute substantial portions of a high energy physics (HEP) analysis pipeline with minimal expert-curated input. Given access to a HEP dataset, an execution framework, and a corpus of prior experimental literature, we find that Claude Code succeeds in automating all stages of a typical analysis: event selection, background estimation, uncertainty quantification, statistical inference, and paper drafting. We argue that the experimental HEP community is underestimating the current capabilities of these systems, and that most proposed agentic workflows are too narrowly scoped or scaffolded to specific analysis structures. We p...
| Authors | Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak et al. |
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| Primary category | hep-ex |
| Published | 20 March 2026 |
| Last updated at source | 20 June 2026 |
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