AI Agents Can Already Autonomously Perform Experimental High Energy Physics

TL;DR

AI agents based on large language models can autonomously perform significant portions of high energy physics analyses, including data processing, interpretation, and documentation, with minimal expert input.

Contribution

The paper introduces a proof-of-concept framework demonstrating autonomous high energy physics analysis using LLM-based agents integrated with literature retrieval and multi-agent review.

Findings

AI agents can automate event selection, background estimation, and statistical inference.

Demonstrated analyses on open data from ALEPH, DELPHI, and CMS.

Tools can offload repetitive coding, enabling physicists to focus on insights.

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 electroweak, QCD, and Higgs boson measurements. Rather than replacing physicists, these tools promise to offload the repetitive technical burden of analysis code development, freeing researchers to focus on physics insight, truly novel method development, and rigorous validation. Given these developments, we advocate for new strategies for how the community trains students, organizes analysis efforts, and allocates human expertise.