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Abstract Vision-Language Models can process text as rendered images, but accuracy degrades with compression; LensVLM addresses this by scanning compressed images and selectively expanding relevant parts through learned tools, maintaining high accuracy even at high compression ratios. Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into long token sequences.
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