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Cited article:

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A roadmap toward scaling, reasoning and self-evolving foundation models for nuclear and particle physics

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Hierarchical Graph Neural Networks for Particle Track Reconstruction

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Prospects for novel track reconstruction algorithms based on Graph Neural Network models using telescope detector testbed

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Quantum Machine Learning for Track Reconstruction

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From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP

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Deep Learning Methods as a Tool for Overcoming the Crisis of Particle Tracking in High Luminosity HEP Experiments

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Transformers for Charged Particle Track Reconstruction in High-Energy Physics

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