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arXiv · 2605.27696

Structure over Pixels: Learning Variable-Length Visual Programs

Piotr Wyrwiński·Kacper Dobek·Krzysztof Krawiec
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Abstract

Discrete visual tokenizers translate images into ordered sequences of codes, providing a natural representation for structural description of scenes. Yet existing adaptive tokenizers either require post-hoc search or select among a discrete set of pre-trained rates, rather than learning a continuous per-image sequence length coupled to the model and scene, and they typically train against pixel reconstruction, emphasizing texture rather than structure. We propose STROP, a discrete visual tokenizer architecture that forms structural scene representations and simultaneously learns how long an image's visual program should be. Using a four-phase curriculum supervised by local rate--distortion probes against frozen DINOv3 features, STROP optimizes a dedicated length head that estimates the active prefix length in a single forward pass. By bypassing pixel-level reconstruction gradients, the codebook is shaped entirely by the quality of higher-level latent representations. Program length grows with scene complexity, and signs of compositional structure emerge both in downstream dense-prediction transfer and in direct inspection of the learned code vocabulary.

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Piotr Wyrwiński, Kacper Dobek, Krzysztof Krawiec. 2026-05-28. Structure over Pixels: Learning Variable-Length Visual Programs. https://arxiv.org/abs/2605.27696

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