AI Agents Can Already Autonomously Perform Experimental High Energy Physics

2026-03-20

AI Summary

This work proposes the Just Furnish Context (JFC) framework, a novel approach that eliminates the need for manual scaffolding in high-energy physics analyses, which have traditionally relied on labor-intensive, error-prone human coding. By providing only a dataset, execution environment, and relevant literature corpus, an autonomous agent powered by Claude Code—a large language model—can perform end-to-end analysis, including event selection, background estimation, uncertainty quantification, statistical inference, and scientific paper drafting. The system integrates literature-aware retrieval with a multi-agent review mechanism to ensure rigor and reproducibility. Validated on public datasets from ALEPH, DELPHI, and CMS experiments, JFC successfully reproduces established measurements in electroweak physics, QCD, and Higgs boson studies, generating credible, publication-ready reports while substantially reducing human effort.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Evaluation and Analysis
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• AI agents
• high energy physics
• autonomous analysis
• analysis automation
• large language models

Innovation

• AI agents
• autonomous analysis
• high energy physics
• large language models
• Just Furnish Context