  Combinatorial inverse problems in high energy physics span enormous
algorithmic challenges. This work presents a new deep learning driven
clustering algorithm that utilizes a space-time non-local trainable graph
constructor, a graph neural network, and a set transformer. The model is
trained with loss functions at the graph node, edge and object level, including
contrastive learning and meta-supervision. The algorithm can be applied to
problems such as charged particle tracking, calorimetry, pile-up
discrimination, jet physics, and beyond. We showcase the effectiveness of this
cutting-edge AI approach through particle tracking simulations. The code is
available online.
