SEER: Long-Context Reasoning via Selective Visual-Text Compression

Abstract:Long-context reasoning remains computationally expensive for large language models due to the quadratic complexity of attention over text tokens. Visual-text compression offers a promising alternative by rendering text into images and processing them with vision-language models, often reducing token usage. However, existing approaches apply uniform compression regardless of query relevance, potentially sacrificing precision where detailed extraction is required. We present SEER, a framework that learns to select query-relevant images through visual scanning and retrieve textual content only where needed, combining the efficiency of visual compression with the precision of text-based reasoning. Through supervised fine-tuning on tool-interaction trajectories, SEER learns adaptive tool invocation for selection and retrieval. Experiments on long-context benchmarks show that SEER improves extraction precision through selective text retrieval while retaining average prompt-token savings relative to full-text baselines. On LongBench, SEER achieves 51.11% average accuracy, outperforming the visual-text baseline Glyph-9B by 2.33 points and Qwen3-8B by 3.49 points. Code can be accessed at this https URL

Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2608.15962 [cs.CL]

(or arXiv:2608.15962v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.15962 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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