Norm or Direction? Decoding Vision Mambas for High-Resolution Vision

Vision Mamba models replace quadratic self-attention with linear complexity selective state space models (SSMs), emerging as efficient visual backbones. However, MambaOut demonstrates that a Gated CNN block can match or exceed VMamba on image classification, questioning the necessity of SSMs for vision. This raises a fundamental question: do VMamba and MambaOut encode visual information differently at the representation level? To investigate, we apply cross model centered kernel alignment (CKA)

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ethics.ai (21 July 2026), “Norm or Direction? Decoding Vision Mambas for High-Resolution Vision,” evidence record 12352, https://ethics.ai/record/12352 (originally published by arXiv).

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