Glyph: Scaling Context Windows via Visual-Text Compression
Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang

TL;DR
Glyph introduces a visual-text compression framework that renders long texts into images for processing by vision-language models, enabling efficient long-context understanding with significant token compression and speed improvements.
Contribution
This work presents a novel visual-text compression method using image rendering and VLMs, allowing scalable long-context modeling beyond traditional token-based approaches.
Findings
Achieves 3-4x token compression with maintained accuracy
Enables 4x faster prefilling and decoding
Supports 1M-token-level tasks with a 128K-context VLM
Abstract
Large language models (LLMs) increasingly rely on long-context modeling for tasks such as document understanding, code analysis, and multi-step reasoning. However, scaling context windows to the million-token level brings prohibitive computational and memory costs, limiting the practicality of long-context LLMs. In this work, we take a different perspective-visual context scaling-to tackle this challenge. Instead of extending token-based sequences, we propose Glyph, a framework that renders long texts into images and processes them with vision-language models (VLMs). This approach substantially compresses textual input while preserving semantic information, and we further design an LLM-driven genetic search to identify optimal visual rendering configurations for balancing accuracy and compression. Through extensive experiments, we demonstrate that our method achieves 3-4x token…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Generative Adversarial Networks and Image Synthesis · Natural Language Processing Techniques
