arXiv: cs.AI preprintsInternational5 November 2026
Learning Hierarchical Patch Splitting Policies for Faster Vision Transformers
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arXiv:2611.05742v1 Announce Type: cross Abstract: Vision Transformers (ViTs) are bottlenecked by the length of their input token sequence. Standard patchification fixes this length with a single patch size applied across the image, regardless of regional content. Prior work on adaptive patch sizing reduces token count by assigning coarser patches to visually simple regions, but relies on noisy pixel-level heuristics such as local entropy to make patchification decisions. We argue that this compute allocation should be grounded in the model's interpretation of task relevant information, not in
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