AI 中文总结
本文介绍因HEP-ML领域文献激增，原《粒子物理学机器学习活体综述》无法维持，将冻结为存档资料，取而代之的是用于筛选标注代表性研究的《HEP-ML活体指南》。
AI 中文摘要
我们于2020年启动了《粒子物理学机器学习活体综述》（HEP-ML Living Review），这是一份由社区维护、近乎全面的粒子物理学领域机器学习文献目录。当时该领域的发展速度远超单个研究人员所能跟进的速度，查找相关论文十分困难，而一份结构化、持续更新的参考资料立即发挥了作用。此后，该领域的文献数量增长了一个数量级以上，所用方法已远远超出早期的分类和生成任务范畴，且该社区已构建了自身的特定主题综述、基准论文及软件框架生态系统。原有的模式已无法很好地服务于该领域，我们也无力继续维持其运行。因此我们调整了方向：将《活体综述》冻结为截至2026年6月1日的存档参考资料，作为HEP-ML第一阶段的稳定记录；新资源《HEP-ML活体指南》将取而代之，它不会罗列所有内容，而是进行筛选、标注，并为读者指明基础及代表性研究，以便研究人员能在这个成熟且快速多样化的领域中找到方向。本文将解释我们做出这一变更的原因以及新资源的运作方式。
英文摘要
We started the Living Review of Machine Learning for Particle Physics (HEP-ML Living Review) in 2020 as a community-maintained, near-comprehensive bibliography of machine learning in particle physics. The field was then growing faster than any single researcher could follow, finding the relevant papers was hard, and a structured, continuously updated reference paid off immediately. Since then the literature has grown by more than an order of magnitude, the methods reach far beyond the classification and generation tasks of the early years, and the community has built its own ecosystem of topic-specific reviews, benchmark papers, and software frameworks. The original model no longer serves this field well, and we can no longer sustain it. We therefore change direction. We freeze the Living Review as an archival reference covering the literature up to 1 June 2026, where it remains a stable record of the first phase of HEP-ML. A new resource, the HEP-ML Living Guide, replaces it. It does not list everything. It curates, it annotates, and it points readers to foundational and representative work, so that researchers can find their way into a mature and rapidly diversifying field. In this article we explain why we make this change and how the new resource works.
Comments8 pages, 3 figures, GitHub repository of Living Guide https://github.com/iml-wg/HEPML-LivingGuide
