  With the advent of advanced machine learning techniques, boosted object
tagging has witnessed significant progress. In this article, we take this field
further by introducing novel architectural modifications compatible with a wide
array of Graph Neural Network (GNN) architectures. Our approach advocates for
integrating capsule layers, replacing the conventional decoding blocks in
standard GNNs. These capsules are a group of neurons with vector activations.
The orientation of these vectors represents important properties of the objects
under study, with their magnitude characterizing whether the object under study
belongs to the class represented by the capsule. Moreover, capsule networks
incorporate a regularization by reconstruction mechanism, facilitating the
seamless integration of expert-designed high-level features into the analysis.
We have studied the usefulness of our architecture with the LorentzNet
architecture for quark-gluon tagging. Here, we have replaced the decoding block
of LorentzNet with a capsulated decoding block and have called the resulting
architecture CapsLorentzNet. Our new architecture can enhance the performance
of LorentzNet by 20 \% for the quark-gluon tagging task.
