  Track reconstruction is a crucial task in particle experiments and is
traditionally very computationally expensive due to its combinatorial nature.
Recently, graph neural networks (GNNs) have emerged as a promising approach
that can improve scalability. Most of these GNN-based methods, including the
edge classification (EC) and the object condensation (OC) approach, require an
input graph that needs to be constructed beforehand. In this work, we consider
a one-shot OC approach that reconstructs particle tracks directly from a set of
hits (point cloud) by recursively applying graph attention networks with an
evolving graph structure. This approach iteratively updates the graphs and can
better facilitate the message passing across each graph. Preliminary studies on
the TrackML dataset show better track performance compared to the methods that
require a fixed input graph.
