  The particle-flow (PF) algorithm, which infers particles based on tracks and
calorimeter clusters, is of central importance to event reconstruction in the
CMS experiment at the CERN LHC, and has been a focus of development in light of
planned Phase-2 running conditions with an increased pileup and detector
granularity. In recent years, the machine learned particle-flow (MLPF)
algorithm, a graph neural network that performs PF reconstruction, has been
explored in CMS, with the possible advantages of directly optimizing for the
physical quantities of interest, being highly reconfigurable to new conditions,
and being a natural fit for deployment to heterogeneous accelerators. We
discuss progress in CMS towards an improved implementation of the MLPF
reconstruction, now optimized using generator/simulation-level particle
information as the target for the first time. This paves the way to potentially
improving the detector response in terms of physical quantities of interest. We
describe the simulation-based training target, progress and studies on
event-based loss terms, details on the model hyperparameter tuning, as well as
physics validation with respect to the current PF algorithm in terms of
high-level physical quantities such as the jet and missing transverse momentum
resolutions. We find that the MLPF algorithm, trained on a generator/simulator
level particle information for the first time, results in broadly compatible
particle and jet reconstruction performance with the baseline PF, setting the
stage for improving the physics performance by additional training statistics
and model tuning.
