Recent improvements in large language models have opened new opportunities for accelerating and automating scientific workflows. In parallel, modern collider analyses are becoming increasingly complex and demand substantial programming and deep learning expertise. \coll alleviates this workload by using pretrained large language models to generate physically consistent analysis code for event selection. Additionally, it automates subsequent deep learning analyses. To further reduce reliance on programming or deep learning experience, \coll provides a graphical user interface that allows users to perform end-to-end analyses through an interactive interface. The main motivation behind \coll is to lower the coding burden and simplify the technical complexity of collider analyses, which increasingly depend on sophisticated event selections and advanced deep learning methods.
@article{arxiv.2602.06496,
title = {CoLLM: AI engineering toolbox for end-to-end deep learning in collider analyses},
author = {W. Esmail and A. Hammad and M. Nojiri},
journal= {arXiv preprint arXiv:2602.06496},
year = {2026}
}