  Precision studies for top quark physics are a cornerstone of the Large Hadron
Collider program. Polarization, probed through decay kinematics, provides a
unique tool to scrutinize the top quark across its various production modes and
to explore potential new physics effects. However, the top quark most often
decays hadronically, for which unambiguous identification of its decay products
sensitive to top quark polarization is not possible. In this Letter, we
introduce a jet flavor tagging method to significantly improve spin analyzing
power in hadronic decays, going beyond exclusive kinematic information employed
in previous studies. We provide parametric estimates of the improvement from
flavor tagging with any set of measured observables and demonstrate this in
practice on simulated data using a Graph Neural Network (GNN). We find that the
spin analyzing power in hadronic decays can improve by approximately 20% (40%)
compared to the kinematic approach, assuming an efficiency of 0.5 (0.2) for the
network.
