  We present the current stage of research progress towards a one-pass,
completely Machine Learning (ML) based imaging calorimeter reconstruction. The
model used is based on Graph Neural Networks (GNNs) and directly analyzes the
hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of
hits originating from the same incident particle by labeling the hits with the
same cluster index. We impose simple criteria to assess whether the hits
associated as a cluster by the prediction are matched to those hits resulting
from any particular individual incident particles. The algorithm is studied by
simulating two tau leptons in each of the two HGCAL endcaps, where each tau may
decay according to its measured standard model branching probabilities. The
simulation includes the material interaction of the tau decay products which
may create additional particles incident upon the calorimeter. Using this
varied multiparticle environment we can investigate the application of this
reconstruction technique and begin to characterize energy containment and
performance.
