  The physics reach of the HL-LHC will be limited by how efficiently the
experiments can use the available computing resources, i.e. affordable software
and computing are essential. The development of novel methods for charged
particle reconstruction at the HL-LHC incorporating machine learning techniques
or based entirely on machine learning is a vibrant area of research. In the
past two years, algorithms for track pattern recognition based on graph neural
networks (GNNs) have emerged as a particularly promising approach. Previous
work mainly aimed at establishing proof of principle. In the present document
we describe new algorithms that can handle complex realistic detectors. The new
algorithms are implemented in ACTS, a common framework for tracking software.
This work aims at implementing a realistic GNN-based algorithm that can be
deployed in an HL-LHC experiment.
