SparseMM shows that under 5% of attention heads carry visual understanding in MLLMs and uses OCR-derived head scores to allocate KV-cache budgets asymmetrically, preserving accuracy at low cache sizes.
An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models
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SparseMM: Head Sparsity Emerges from Visual Concept Responses in MLLMs
SparseMM shows that under 5% of attention heads carry visual understanding in MLLMs and uses OCR-derived head scores to allocate KV-cache budgets asymmetrically, preserving accuracy at low cache sizes.