DeCoRAG: Cognitive Decoupling and Semantic-Aware Cropping for Complex Document Understanding
Advancing multimodal retrieval-augmented generation (RAG) for complex document understanding presents a formidable dual dilemma of accuracy and efficiency, particularly in graph RAG. Processing structurally sparse yet visually dense layouts, such as extracting a tiny data marker from a financial chart, often incurs computationally prohibitive token overhead while still triggering catastrophic hallucination. However, multimodal Graph RAG pipelines rely on graph-construction stages that assume Vision-Language Models (VLMs) can resolve sparse semantics within high-density layouts. We challenge this assumption, revealing that forcing VLMs to localize visual evidence, interpret semantics, and ...
| Authors | Shuo Wang, Kai Zhang, Wenyuan Huang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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