  Accurate simulation of detector responses to hadrons is paramount for all
physics programs at the Large Hadron Collider (LHC). Central to this simulation
is the modeling of hadronic interactions. Unfortunately, the absence of
first-principle theoretical guidance has made this a formidable challenge. The
state-of-the-art simulation tool, \textsc{Geant4}, currently relies on
phenomenology-inspired parametric models. Each model is designed to simulate
hadronic interactions within specific energy ranges and for particular types of
hadrons. Despite dedicated tuning efforts, these models sometimes fail to
describe the data in certain physics processes accurately. Furthermore,
fine-tuning these models with new measurements is laborious. Our research
endeavors to leverage generative models to simulate hadronic interactions.
While our ultimate goal is to train a generative model using experimental data,
we have taken a crucial step by training conditional normalizing flow models
with \textsc{Geant4} simulation data. Our work marks a significant stride
toward developing a fully differentiable and data-driven model for hadronic
interactions in High Energy and Nuclear Physics.
