arXiv Artificial Intelligence

LensVLM: Selective Context Expansion for Compressed Visual Representation of Text

1 hour ago 02.10.2026 07:00 👁 1 views
LensVLM: Selective Context Expansion for Compressed Visual Representation of Text

Quick summary

arXiv:2605.07019v2 Announce Type: replace-cross Abstract: 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. Since VLM image encoders map fixed-size images to a fixed number of visual tokens, varying rendering resolution provides a fine-grained compression knob. However, accuracy deteriorates quickly as compression increases: characters shrink below the vision encoder's effective resolution, making them indistinguishable. To address this, we propose LensVLM, an inference frame

Key takeaways

  • arXiv:2605.07019v2 Announce Type: replace-cross Abstract: 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.
  • Since VLM image encoders map fixed-size images to a fixed number of visual tokens, varying rendering resolution provides a fine-grained compression knob.
  • However, accuracy deteriorates quickly as compression increases: characters shrink below the vision encoder's effective resolution, making them indistinguishable.

Why it matters

The importance of “LensVLM: Selective Context Expansion for Compressed Visual Representation of Text” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗