A vision-language model reads a picture the way you might read a page: it chops the image into patches and turns every patch into a token. A high-resolution photo or a long video quickly becomes thousands of tokens, and every one of them costs attention compute and KV-cache memory. On October 6, 2026, researchers from Alibaba's Qwen team (Yuan Feng and colleagues, with the University of Science and Technology of China) posted VisionWeave (arXiv:2610.07987), a method that stops spending the same number of tokens everywhere. Built on Qwen3.8-27B, it saves 43.0% of visual tokens on average while retaining 98.9% of native performance across eight benchmarks — and on an SGLang serving deployment it turns that saving into 2.30x end-to-end throughput .

The headline is not the compression ratio.…