Messages, Not Tokens: Grounded Coresets for Faithful VLM Compression
Modern vision language models (VLMs) turn high-resolution images into long sequences of visual tokens. Every token traverses the language decoder and persists in its prompt KV cache, inflating inference cost and motivating aggressive visual compression. Existing score-based methods assign each token an independent importance score and retain the Top-K. However, text queries consume collective, signed attention messages from the visual population, not isolated patches. Consequently, equally sized Top-K sets can repeatedly cover one salient region, omit sparse but complementary evidence and discard information carried by the removed population. We therefore formulate faithful visual compres...
| Authors | Long Qian, Jiaqi Wei, Bingke Zhu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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