  Particle tracking is a challenging pattern recognition task at the Large
Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms,
such as those based on the Kalman Filter, achieve excellent performance in
reconstructing the prompt tracks from the collision points. However, they
require dedicated configuration and additional computing time to efficiently
reconstruct the large radius tracks created away from the collision points. We
developed an end-to-end machine learning-based track finding algorithm for the
HL-LHC, the Exa.TrkX pipeline. The pipeline is designed so as to be agnostic
about global track positions. In this work, we study the performance of the
Exa.TrkX pipeline for finding large radius tracks. Trained with all tracks in
the event, the pipeline simultaneously reconstructs prompt tracks and large
radius tracks with high efficiencies. This new capability offered by the
Exa.TrkX pipeline may enable us to search for new physics in real time.
