  Efficient and accurate algorithms are necessary to reconstruct particles in
the highly granular detectors anticipated at the High-Luminosity Large Hadron
Collider and the Future Circular Collider. We study scalable machine learning
models for event reconstruction in electron-positron collisions based on a full
detector simulation. Particle-flow reconstruction can be formulated as a
supervised learning task using tracks and calorimeter clusters. We compare a
graph neural network and kernel-based transformer and demonstrate that we can
avoid quadratic operations while achieving realistic reconstruction. We show
that hyperparameter tuning significantly improves the performance of the
models. The best graph neural network model shows improvement in the jet
transverse momentum resolution by up to 50% compared to the rule-based
algorithm. The resulting model is portable across Nvidia, AMD and Habana
hardware. Accurate and fast machine-learning based reconstruction can
significantly improve future measurements at colliders.
