LensVLM is an inference framework that enables Vision Language Models to process compressed images of text by selectively expanding relevant regions, maintaining accuracy at 4.3x compression while outperforming baselines up to 10.1x compression across text QA benchmarks. The approach combines learned tools for selective expansion with post-training to make visual compression robust, generalizing to multimodal document and code understanding tasks.
LensVLM is a 9B Vision Language Model that compresses long text documents into images, then selectively expands only relevant pages to answer queries. The model uses learned tools to decompress specific sections, supporting compression ratios up to 15x while maintaining question-answering capabilities.