Hybrid Token Compression for Vision-Language Models
Quick summary
arXiv:2512.08240v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) rely on hundreds of visual tokens, leading to high computational and memory costs. Existing compression methods face a trade-off: continuous compression can weaken high-level semantics, while discrete quantization may lose fine-grained appearance details. We introduce HTC-VLM, a hybrid visual token compression framework that disentangles semantics and appearance through two complementary pathways. A continuous pathway preserves detailed ViT patch features, while a discrete pathway provides semantic anchors
Key takeaways
- arXiv:2512.08240v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) rely on hundreds of visual tokens, leading to high computational and memory costs.
- Existing compression methods face a trade-off: continuous compression can weaken high-level semantics, while discrete quantization may lose fine-grained appearance details.
- We introduce HTC-VLM, a hybrid visual token compression framework that disentangles semantics and appearance through two complementary pathways.
Why it matters
“Hybrid Token Compression for Vision-Language Models” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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