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Generative Trajectory Prediction over Hexagonal Spatial Grids

Predicting an entity's future movement path by treating space as a grid of hexagonal cells and modeling the sequence of visited cells with a deep autoregressive generative model. Learners see how a Transformer trained on higher-order mobility flows, combined with a spatially constrained beam search, forecasts the next k cells while preserving path continuity, and how mixed-resolution hex maps trade detail for efficiency.