The Living Guide of Machine Learning for Particle Physics
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.