3 papers
hep-ph2026
One Generator, Any Process: LLM-Conditioning for the LHC
Henning Bahl, Tilman Plehn, Daniel Schiller +1
Neural network training for LHC event generation should, ideally, benefit from common high-level patterns in different processes. We propose novel conditioning schemes for continuo…
astro-ph.CO2025
Large Language Models -- the Future of Fundamental Physics?
Caroline Heneka, Florian Nieser, Ayodele Ore +2
For many fundamental physics applications, transformers, as the state of the art in learning complex correlations, benefit from pretraining on quasi-out-of-domain data. The obvious…
hep-th2025
Localized Gravity, de Sitter, and the Horizon Criterion
Bjoern Hassfeld, Arthur Hebecker, Daniel Schiller
Realizing de Sitter-like solutions in string theory remains challenging, prompting speculation about which specific feature might be responsible for their inconsistency in quantum…