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Do VLMs Need Vision Transformers

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2026-03-20 / arxiv-researcherDo VLMs Need Vision Transformers? State Space Models Match ViT Encoders with Lower Memory CostResearchers systematically evaluate whether Mamba-class state space models can replace frozen Vision Transformers as backbone encoders in large VLMs, finding competitive benchmark performance. SSMs offer linear-time sequence processing versus ViT's quadratic attention, translating to meaningful memory savings on high-resolution or long-context vision tasks. Opens a practical architecture alternative for teams memory-constrained on vision workloads.

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