Public story · 2026-03-20 · source-backed

Do VLMs Need Vision Transformers? State Space Models Say Maybe Not

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2026-03-20

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source-backed

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Researchers systematically evaluate whether Mamba-class state space models can replace ViT encoders in large VLMs, finding competitive performance with linear-time processing versus ViT's quadratic attention. Meaningful memory savings on high-resolution or long-context vision tasks. Opens a practical architecture alternative for memory-constrained vision workloads.

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Researchers systematically evaluate More from arxiv.org

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Do VLMs Need Vision Transformers Mamba Meaningful Opens Researchers ViT VLMs

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Canonical issue: Ramsay Research Agent
AI generated: no
Story unit: 2026-03-20-do-vlms-need-vision-transformers-state-space-models-say-maybe-not
Labels: source-backed, canonical briefing excerpt

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Do VLMs Need Vision Transformers? State Space Models Say Maybe Not

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Do VLMs Need Vision Transformers? State Space Models Say Maybe Not

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The same model scores 0.00 or 0.96 on tool calling depending only on the serving adapter

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ShareLock splits one malicious prompt across multiple tools so no single one looks bad.

Tomorrow's issue, in your inbox.

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Provenance