Apple open-sources LensVLM-9B: 100-page documents are first compressed into images, reducing KV cache usage by 84%
40 minutes ago
Beating AI Express News: Apple has open-sourced LensVLM-9B, a vision-language model purpose-built for processing long documents, trained on Qwen3.5-9B-Base. When handling long documents, the model first compresses the entire document into low-resolution pages for a quick scan to locate relevant pages, then reads the original text or high-definition images on those pages—eliminating the need to load the full document into the model’s context window. Per the paper’s tests: For a 100-page document, reading the full text directly requires 51,273 tokens, while LensVLM uses just 8,090 tokens. The corresponding KV cache drops from approximately 1.6GB to 253MB, an 84.2% reduction, with no significant accuracy loss after compression. Among 7 document question-and-answer tasks, direct full-text reading yields an average accuracy of 72.4%, while LensVLM hits 68.9% at roughly 4.3x compression. Compared to the approach of converting text to images for model recognition (at around 5x compression), LensVLM lifts accuracy from 31.3% to 68.9%. That said, the model is currently slower: After locating the relevant page, LensVLM must call an additional tool to read the original text, requiring two consecutive inference runs. The paper notes each answer takes approximately 17 seconds; without the compression scheme, directly inputting the full document as text into Qwen3.5-9B takes about 8 seconds to generate a response.
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