Applying graph-based techniques, and graph neural networks (GNNs) in particular, has been shown to be a promising solution to the high-occupancy track reconstruction problems posed by the upcoming HL-LHC era. Simulations of this environment present noisy, heterogeneous and ambiguous data, which previous GNN-based algorithms for ATLAS ITk track reconstruction could not handle natively. We present a range of upgrades to the so-called GNN4ITk pipeline that allow detector regions to be handled heterogeneously, ambiguous and shared nodes to be reconstructed more rigorously, and tracks-of-interest to be treated with more importance in training. With these improvements, we are able to present for the first time apples-to-apples comparisons with existing reconstruction algorithms on a range of physics metrics, including reconstruction efficiency across particle type and pileup condition, jet reconstruction performance in dense environments, displaced tracking, and track parameter resolutions. We also demonstrate that our results are robust to misalignment of ITk modules, showing the GNN4ITk approach to perform well under changing experimental conditions. By integrating this solution with the offline ATLAS Athena framework, we also explore a range of reconstruction chain configurations, for example by using the GNN4ITk pipeline to build regions-of-interest while using traditional techniques for track cleaning and fitting.
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