Abstract
The expansion of retrieval-augmented generation (RAG) into multimodal domains has intensified the challenge for processing complex visual documents, such as financial reports. While page-level chunking and retrieval is a natural starting point, it creates a critical bottleneck: delivering entire pages to the generator introduces excessive extraneous context. This not only overloads the generator's attention mechanism but also dilutes the most salient evidence. Moreover, compressing these information-rich pages into a limited visual token budget further increases the risk of hallucinations. To address this, we introduce AgenticOCR, a dynamic parsing paradigm that transforms optical character recognition (OCR) from a static, full-text process into a query-driven, on-demand extraction system. By autonomously analyzing document layout in a "thinking with images" manner, AgenticOCR identifies and selectively recognizes regions of interest. This approach performs on-demand decompression of visual tokens precisely where needed, effectively decoupling retrieval granularity from rigid page-level chunking. AgenticOCR has the potential to serve as the "third building block" of the visual document RAG stack, operating alongside and enhancing standard Embedding and Reranking modules. Experimental results demonstrate that AgenticOCR improves both the efficiency and accuracy of visual RAG systems, achieving expert-level performance in long document understanding. Code and models are available at this https URL.
Abstract (translated)
将检索增强生成（RAG）扩展到多模态领域，特别是处理复杂的视觉文档如财务报告时，面临的挑战愈发严峻。虽然页面级别的切分和检索是一个自然的起点，但它产生了一个关键瓶颈：向生成器提供整页内容会引入过多不必要的上下文信息。这不仅会使生成器的注意力机制过载，还会稀释最相关的证据。此外，在有限的视觉标记预算内压缩这些富含信息的页面进一步增加了幻觉的风险。 为了解决这些问题，我们提出了AgenticOCR，这是一种动态解析范式，它将光学字符识别（OCR）从静态、全文处理过程转变为一种查询驱动、按需提取系统。通过以“用图像思考”的方式自主分析文档布局，AgenticOCR能够识别并有选择地识别感兴趣区域。这种方法实现了视觉标记的按需解压缩，精确到所需之处，有效区分了检索粒度和固定页面级切分之间的耦合关系。AgenticOCR有可能成为视觉文档RAG栈中的“第三块基石”，与标准的嵌入式模块和重新排序模块并行工作且增强其功能。 实验结果表明，AgenticOCR能够提升视觉RAG系统的效率和准确性，在长文档理解方面达到了专家级水平。代码和模型可以在[此处](https://this https URL)获取。
URL
https://arxiv.org/abs/2602.24134
