AI Agents Can Already Autonomously Perform Experimental High Energy Physics
Massachusetts Institute of Technology（麻省理工学院）；NSF AI Institute for Artificial Intelligence and Fundamental Interactions（国家科学基金会人工智能与基本相互作用研究所）；CERN（欧洲核子研究中心）
Eric A. Moreno, Samuel Bright-Thonney, Andrzej Novak, Dolores Garcia, Philip Harris
基于大语言模型的AI智能体现在能够以最少的专家输入自主执行高能物理分析流水线的大部分内容。在获得高能物理数据集、执行框架和先前实验文献库的情况下，我们发现Claude Code成功自动化了典型分析的所有阶段：事件选择、背景估计、不确定性量化、统计推断和论文起草。我们认为实验高能物理社区低估了这些系统当前的能力，并且大多数提出的智能体工作流范围过于狭窄或局限于特定分析结构。我们提出了一个概念验证框架Just Furnish Context (JFC)，该框架将自主分析智能体与基于文献的知识检索和多智能体审查相结合，并证明这足以规划、执行和记录一个可信的高能物理分析。我们通过使用ALEPH、DELPHI和CMS的开放数据进行电弱、QCD和希格斯玻色子测量来证明这一点。我们以简短的论文形式展示了其中两个结果——CMS Run1开放数据$H\ o \ au^+\ au^-$以展示在成熟结果上的性能，以及LEP数据上的首个Lund平面测量——这是一个真正新颖的结果，据我们所知，是由AI智能体自主产生的首个结果。这些工具并非取代物理学家，而是有望减轻分析代码开发的重复性技术负担，使研究人员能够专注于物理洞察、真正新颖的方法开发和严格的验证。鉴于这些发展，我们倡导社区在培养学生、组织分析工作和分配人类专业知识方面采取新策略。
英文摘要：
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. We present two of those results in a condensed short paper form -- a CMS Run1 Open Data $H\to τ^+τ^-$ to demonstrate performance on a well-established result, and the first Lund plane measurement on LEP data -- a genuinely novel result and, to our knowledge, the first produced autonomously by an AI agent. 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.
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