ceci n'est pas un tagger, can an LLM flavour-tag a jet?
Abstract
Large language models (LLMs) are increasingly capable of reasoning about scientific problems, effectively mastering and synthesizing entire fields of research and providing, for the first time, access to a distilled and queryable form of scientific knowledge. As the integration of LLMs rapidly spreads across the large collaborations of high-energy physics, aiming to re-think and optimize analysis workflows, it remains unclear whether the knowledge acquired during pretraining can support quantitative inference on physical data. Moving beyond the applications currently pursued by the community, we pose a simple question: can an LLM classify the flavour of a hadronic jet? An LLM is therefore asked to assign flavour probabilities (b, c, light, τ) to jets from a public ATLAS flavour-tagging sample, using only reconstructed constituents. Under a small set of constraints, the model is not allowed to write code or implement conventional algorithms and must instead perform the inference directly from its pretrained knowledge and the available inputs. We report two complementary results and make publicly available, through a GitHub repository, all the code required to reproduce the experiment and obtain the quantitative results presented in this work. First, the model achieves non-trivial discrimination between flavours, which we compare with recent ATLAS flavour-tagging algorithms. Second, the model accompanies each prediction with a natural language rationale. The physical concepts invoked in these rationales correlate with the truth flavour and reproduce the features traditionally associated with each jet category. Flavour tagging thus provides a laboratory for broader questions: how can we actively leverage the corpus of scientific knowledge accumulated over decades of research? Could LLMs serve as a general source of scientific context for task-specific algorithms?
Related
- ResoSeg: Resonance Tagger using Transformer and Segment Model
- A compact, interrogable Tree Tensor Network Jet Tagger on Lund declustering trees
- What the Primary Lund Plane Omits: A Model-Stitching Diagnosis of a Jet-Tagger Representation Gap
- Search for Neutrino-Produced Long-Lived Particles Using the SBND Cosmic Ray Tagger Beam Telescope
- Improving lepton flavour universality tests with KL decays
BibTeX
@misc{coccaro2026ceci,
title = {{ceci n'est pas un tagger, can an LLM flavour-tag a jet?}},
author = {Andrea Coccaro and Andrea Milici and Lucio Derin},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.24954},
eprint = {2610.24954},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.24954},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - ceci n'est pas un tagger, can an LLM flavour-tag a jet?
AU - Andrea Coccaro
AU - Andrea Milici
AU - Lucio Derin
PY - 2026
DA - 2026/10/18
PB - arXiv
DO - 10.48550/arxiv.2610.24954
UR - https://arxiv.org/abs/2610.24954
AB - Large language models (LLMs) are increasingly capable of reasoning about scientific problems, effectively mastering and synthesizing entire fields of research and providing, for the first time, access to a distilled and queryable form of scientific knowledge. As the integration of LLMs rapidly spreads across the large collaborations of high-energy physics, aiming to re-think and optimize analysis workflows, it remains unclear whether the knowledge acquired during pretraining can support quantitative inference on physical data. Moving beyond the applications currently pursued by the community, we pose a simple question: can an LLM classify the flavour of a hadronic jet? An LLM is therefore asked to assign flavour probabilities (b, c, light, τ) to jets from a public ATLAS flavour-tagging sample, using only reconstructed constituents. Under a small set of constraints, the model is not allowed to write code or implement conventional algorithms and must instead perform the inference directly from its pretrained knowledge and the available inputs. We report two complementary results and make publicly available, through a GitHub repository, all the code required to reproduce the experiment and obtain the quantitative results presented in this work. First, the model achieves non-trivial discrimination between flavours, which we compare with recent ATLAS flavour-tagging algorithms. Second, the model accompanies each prediction with a natural language rationale. The physical concepts invoked in these rationales correlate with the truth flavour and reproduce the features traditionally associated with each jet category. Flavour tagging thus provides a laboratory for broader questions: how can we actively leverage the corpus of scientific knowledge accumulated over decades of research? Could LLMs serve as a general source of scientific context for task-specific algorithms?
ER -