[2603.20179] AI Agents Can Already Autonomously Perform Experimental High Energy Physics

High Energy Physics - Experiment arXiv:2603.20179 (hep-ex) [Submitted on 20 Mar 2026] Title:AI Agents Can Already Autonomously Perform Experimental High Energy Physics Authors:Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak, Dolores Garcia, Philip Harris View a PDF of the paper titled AI Agents Can Already Autonomously Perform Experimental High Energy Physics, by Eric A. Moreno and 4 other authors View PDF Abstract: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 present a proof-of-concept framework, Just Furnish Context (JFC), that integrates autonomous analysis agents with literature-based knowledge retrieval and multi-agent review, and show that this is sufficient to plan, execute, and document a credible high energy physics analysis. We demonstrate this by conducting analyses on open data from ALEPH, DELPHI, and CMS to perform el...