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The mass assembly history (MAH) of dark matter halos plays a crucial role inshaping the formation and evolution of galaxies. MAHs are used extensively insemi-analytic and empirical models of galaxy formation, yet current analyticmethods to generate them are inaccurate and unable to capture theirrelationship with the halo internal structure and large-scale environment. Thispaper introduces FLORAH, a machine-learning framework for generating assemblyhistories of ensembles of dark matter halos. We train FLORAH on the assemblyhistories from the GUREFT and VSMDPL N-body simulations and demonstrate itsability to recover key properties such as the time evolution of mass andconcentration. We obtain similar results for the galaxy stellar mass versushalo mass relation and its residuals when we run the Santa Cruz semi-analyticmodel on FLORAH-generated assembly histories and halo formation historiesextracted from an N-body simulation. We further show that FLORAH alsoreproduces the dependence of clustering on properties other than mass (assemblybias), which is not captured by other analytic methods. By combining multiplenetworks trained on a suite of simulations with different redshift ranges andmass resolutions, we are able to construct accurate main progenitor branches(MPBs) with a wide dynamic mass range from $z = 0$ up to an ultra-high redshift$z \approx 20$, currently far beyond that of a single N-body simulation. FLORAHis the first step towards a machine learning-based framework for planting fullmerger trees; this will enable the exploration of different galaxy formationscenarios with great computational efficiency at unprecedented accuracy.

FLORAH: A generative model for halo assembly histories