# Visual Text Compression

FocusVTC introduces an adaptive-resolution visual text compression framework that keeps global coverage with low-DPI page images and uses tool calls to re-read reasoning-relevant regions at aligned 144 DPI, trained with multi-resolution supervised fine-tuning and GRPO on 29.4K Reasoning-Evidence Localization chain-of-thought examples that link reasoning traces to page indices and bounding boxes, reaching 87.4 on RULER v1 at 72 DPI (versus 57.5 for Glyph), 56.40 on LongBench (versus 55.86 for its text-input backbone), a 13.91-point MRCR macro-average gain, and a 51.19 VTCBench macro-average, while MMMU rises from 65.12 to 66.73 and MME from 2424.02 to 2457.62.

FocusVTC introduces an adaptive-resolution visual text compression framework that keeps global coverage with low-DPI page images and uses tool calls to re-read reasoning-relevant regions at aligned 144 DPI, trained with multi-resolution supervised fine-tuning and GRPO on 29.4K Reasoning-Evidence Localization chain-of-thought examples that link reasoning traces to page indices and bounding boxes, reaching 87.4 on RULER v1 at 72 DPI (versus 57.5 for Glyph), 56.40 on LongBench (versus 55.86 for its text-input backbone), a 13.91-point MRCR macro-average gain, and a 51.19 VTCBench macro-average, while MMMU rises from 65.12 to 66.73 and MME from 2424.02 to 2457.62.

RenderRank renders document text as images and encodes them into compressed visual tokens, aligning relevance scores to a text-based teacher and then refining positive-versus-negative scores within each query, reaching an average NDCG@10 of 55.96 on 11 BEIR datasets with 290.07 average input tokens, 16.5–35.5% fewer than the evaluated text-based rerankers, and an average NDCG@10 of 88.27 on four long-document datasets at roughly half their average input token count.

RenderRank renders document text as images and encodes them into compressed visual tokens, aligning relevance scores to a text-based teacher and then refining positive-versus-negative scores within each query, reaching an average NDCG@10 of 55.96 on 11 BEIR datasets with 290.07 average input tokens, 16.5–35.5% fewer than the evaluated text-based rerankers, and an average NDCG@10 of 88.27 on four long-document datasets at roughly half their average input token count.