发表机构
Massachusetts Institute of Technology; Center for Theoretical Physics -- a Leinweber Institute（麻省理工学院; 理论物理中心——莱因韦伯研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本盘点综述2025-2026年LHC物理项目中机器学习的发展，指出HEPML已从工具构建转变为基础设施，并总结智能体、基础模型融合、信任问题等六大关键趋势。
AI 中文摘要
本摘要的第一句话——以及这些会议记录的前言——由人类撰写，但本文档的大部分内容由智能体AI系统生成。在本次演讲中，我对2025年5月至2026年5月这十二个月期间机器学习（ML）在LHC物理项目中的应用进行了盘点。语料库为HEPML Living Review，按2025年5月划分为1,756篇早期论文和569篇后期论文。一个AI流水线调查了这569篇摘要，根据引用量、时效性、主题和合作参与度进行排序，并完整阅读了103篇论文（其中95篇来自划分之后，加上8篇早期基线论文），为每篇论文生成了结构化笔记。随后，这些笔记被综合成关于该领域现状的六项主张，由独立评审智能体进行核查，并对照原始论文重新验证。首要主张是：（1）高能物理机器学习（HEPML）已不再是一个构建工具的研究领域，而成为LHC物理项目所依赖的基础设施：ATLAS和CMS现在发布的物理结果依赖于神经网络，且存档的ALEPH数据已重新投入生产。其余五项主张为：（2）基于模拟的推断和基础模型是两场开始融合的革命；（3）AI智能体是真正的新前沿，十二个月内发表47篇论文，但尚无被采用的测量成果；（4）“我们信任它吗？”是增长最快的研究议程，最近五篇论文中约有一篇涉及不确定性、校准或可解释性；（5）进展放缓的原因具有启发性，因为等变性已被吸收进工具中，而特定模型的现象学让位于与模型无关的搜索；（6）理论机器学习在多个研究领域跨越了能力门槛。最后，我总结了哪些问题已解决、哪些即将到来、哪些尚待解决，并简要讨论了这种与AI合作方式所引发的担忧。
英文摘要
The first sentence of this abstract--and the introduction to these proceedings--was authored by a human, but the bulk of this document was generated by an agentic AI system. In this talk, I take stock of machine learning (ML) for the LHC physics program over the twelve months from May 2025 to May 2026. The corpus is the HEPML Living Review, split at May 2025 into 1,756 earlier papers and 569 later ones. An AI pipeline surveyed the 569 abstracts, ranked them by citations, recency, theme, and collaboration involvement, and read 103 papers in full (95 from after the split, plus 8 earlier baseline papers), producing a structured note for each. The notes were then synthesized into six claims about the state of the field, checked by independent reviewer agents, and re-verified against the source papers. The headline claim is that (1) ML for high-energy physics (HEPML) stopped being a research area that builds tools and became infrastructure that the LHC physics program depends on: ATLAS and CMS now publish physics results that depend on neural networks, and the archived ALEPH data have re-entered production. The other five claims are: (2) simulation-based inference and foundation models are two revolutions starting to merge; (3) AI agents are the genuinely new front, with 47 papers in twelve months and no adopted measurement yet; (4) "do we trust it?" is the fastest-growing agenda, with one recent paper in five about uncertainty, calibration, or interpretability; (5) what is slowing down is informative, since equivariance was absorbed into a tool and model-specific phenomenology ceded ground to model-agnostic searches; and (6) theory ML crossed a capability threshold in multiple research areas. I close with what is settled, what is incoming, and what is open, and briefly discuss the concerns raised by this way of working with AI.
Comments18 pages, 1 figure. Plenary talk at the 14th Large Hadron Collider Physics Conference (LHCP 2026), Paris, 18-22 May 2026. Conversations welcome; tomatoes expected
