Machine learning for the LHC physics program: a 2025-2026 stocktake
Abstract
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.
Related
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BibTeX
@misc{thaler2026machine,
title = {{Machine learning for the LHC physics program: a 2025-2026 stocktake}},
author = {Jesse Thaler},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.32874},
eprint = {2609.32874},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.32874},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Machine learning for the LHC physics program: a 2025-2026 stocktake
AU - Jesse Thaler
PY - 2026
DA - 2026/09/26
PB - arXiv
DO - 10.48550/arxiv.2609.32874
UR - https://arxiv.org/abs/2609.32874
AB - 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.
ER -