arXiv: cs.AI preprintsInternational6 November 2026
V-LUMEN: Visual Lookup Memory for Embedding Scaling in Vision-Language Models
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arXiv:2610.37019v2 Announce Type: replace-cross Abstract: Embedding-level scaling expands the capacity of language models through lookup-based memory rather than by increasing model depth, width, or dense computation. Extending this idea to vision-language models (VLMs) is challenging because visual features are continuous, spatially structured, and query-dependent, unlike discrete text tokens that naturally support n-gram lookup. We introduce V-LUMEN, a visual lookup memory for VLMs. V-LUMEN discretizes visual features with a frozen codebook, builds spatially aware memory keys through spatial
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