  In this work we demonstrate that significant gains in performance and data
efficiency can be achieved in High Energy Physics (HEP) by moving beyond the
standard paradigm of sequential optimization or reconstruction and analysis
components. We conceptually connect HEP reconstruction and analysis to modern
machine learning workflows such as pretraining, finetuning, domain adaptation
and high-dimensional embedding spaces and quantify the gains in the example
usecase of searches of heavy resonances decaying via an intermediate di-Higgs
system to four $b$-jets.
