Partial Ring Scan: Revisiting Scan Order in Vision State Space Models

Yi-Kuan Hsieh, Kuan-Chuan Peng, Xin Li, Ming-Ching Chang, Yu-Chee Tseng, Jun Wei Hsieh
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:44412-44432, 2026.

Abstract

State Space Models (SSMs) provide linear-time alternatives to attention for vision, but require serializing 2D images into 1D sequences using a predefined scan order. We identify scan order as a previously underexplored inductive bias that fundamentally shapes spatial dependency modeling in Vision SSMs. Fixed scan paths distort local adjacency, fragment object structure, and induce anisotropic representations that are brittle under geometric transformations such as rotation. We propose Partial RIng Scan Mamba (PRIS-Mamba), a rotation-robust traversal that decomposes images into concentric rings, performs permutation-invariant aggregation within each ring, and models cross-ring dependencies via short radial SSMs. This design induces a structured factorization of spatial dependencies that preserves isotropy while maintaining linear complexity. To improve efficiency without sacrificing expressivity, we introduce partial channel filtering, selectively applying recurrent modeling to informative channels while routing others through a residual pathway. Empirically, PRIS-Mamba improves accuracy, efficiency, and rotation robustness over prior Vision SSMs on ImageNet-1K. Our results position scan-order design as a core representational choice in Vision SSMs, with implications for robustness and generalization beyond architectural scaling. The code will be released upon paper acceptance.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-hsieh26a, title = {Partial Ring Scan: Revisiting Scan Order in Vision State Space Models}, author = {Hsieh, Yi-Kuan and Peng, Kuan-Chuan and Li, Xin and Chang, Ming-Ching and Tseng, Yu-Chee and Hsieh, Jun Wei}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {44412--44432}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/hsieh26a/hsieh26a.pdf}, url = {https://proceedings.mlr.press/v306/hsieh26a.html}, abstract = {State Space Models (SSMs) provide linear-time alternatives to attention for vision, but require serializing 2D images into 1D sequences using a predefined scan order. We identify scan order as a previously underexplored inductive bias that fundamentally shapes spatial dependency modeling in Vision SSMs. Fixed scan paths distort local adjacency, fragment object structure, and induce anisotropic representations that are brittle under geometric transformations such as rotation. We propose Partial RIng Scan Mamba (PRIS-Mamba), a rotation-robust traversal that decomposes images into concentric rings, performs permutation-invariant aggregation within each ring, and models cross-ring dependencies via short radial SSMs. This design induces a structured factorization of spatial dependencies that preserves isotropy while maintaining linear complexity. To improve efficiency without sacrificing expressivity, we introduce partial channel filtering, selectively applying recurrent modeling to informative channels while routing others through a residual pathway. Empirically, PRIS-Mamba improves accuracy, efficiency, and rotation robustness over prior Vision SSMs on ImageNet-1K. Our results position scan-order design as a core representational choice in Vision SSMs, with implications for robustness and generalization beyond architectural scaling. The code will be released upon paper acceptance.} }
Endnote
%0 Conference Paper %T Partial Ring Scan: Revisiting Scan Order in Vision State Space Models %A Yi-Kuan Hsieh %A Kuan-Chuan Peng %A Xin Li %A Ming-Ching Chang %A Yu-Chee Tseng %A Jun Wei Hsieh %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-hsieh26a %I PMLR %P 44412--44432 %U https://proceedings.mlr.press/v306/hsieh26a.html %V 306 %X State Space Models (SSMs) provide linear-time alternatives to attention for vision, but require serializing 2D images into 1D sequences using a predefined scan order. We identify scan order as a previously underexplored inductive bias that fundamentally shapes spatial dependency modeling in Vision SSMs. Fixed scan paths distort local adjacency, fragment object structure, and induce anisotropic representations that are brittle under geometric transformations such as rotation. We propose Partial RIng Scan Mamba (PRIS-Mamba), a rotation-robust traversal that decomposes images into concentric rings, performs permutation-invariant aggregation within each ring, and models cross-ring dependencies via short radial SSMs. This design induces a structured factorization of spatial dependencies that preserves isotropy while maintaining linear complexity. To improve efficiency without sacrificing expressivity, we introduce partial channel filtering, selectively applying recurrent modeling to informative channels while routing others through a residual pathway. Empirically, PRIS-Mamba improves accuracy, efficiency, and rotation robustness over prior Vision SSMs on ImageNet-1K. Our results position scan-order design as a core representational choice in Vision SSMs, with implications for robustness and generalization beyond architectural scaling. The code will be released upon paper acceptance.
APA
Hsieh, Y., Peng, K., Li, X., Chang, M., Tseng, Y. & Hsieh, J.W.. (2026). Partial Ring Scan: Revisiting Scan Order in Vision State Space Models. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:44412-44432 Available from https://proceedings.mlr.press/v306/hsieh26a.html.

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