AI看图文比读文字更聪明
大模型一直靠纯文本训练，但论文、网页里的图表、公式排版其实藏着大量文字说不清的信息。这篇直接拿原始文档的视觉图像（不提取文字）去预训练模型，在多个测试中都比纯文本训练效果更好。它不是你明天能用上的，但挑战了一个默认假设：也许让AI像人一样“看图”学知识，比只读文字更高效。
📄 原文摘要(英文)
The rapid progress of large foundation models has been driven predominantly by pretraining on large-scale text corpora. However, many forms of knowledge are conveyed through visual representations, where figures, typeset equations, and page layouts carry rich information that cannot be faithfully or completely captured by text alone. Yet current pretraining approaches discard these visual cues by converting visually rich sources, such as documents and web pages, into plain text for learning language intelligence. This paper challenges the default assumption that language models must be trained on text-only representations and shows that Visual Pretraining is a scalable learner for foundation model intelligence. To this end, we conduct a systematic study of unsupervised visual pretraining paradigms that directly leverage visual documents without text extraction. Across multiple backbones and benchmarks, visual pretraining on the same underlying corpora consistently outperforms text-only pretraining, offering an efficient pathway to scalable language intelligence.