Generative machine learning has become a powerful tool for unbinned, high-dimensional unfolding at the LHC.
This talk highlights recent progress on key open challenges: scaling to hundreds of dimensions, prior-independent parameter estimation, and the path toward fully analysis-ready unfolding.