The October 14, 2025 paper introduxes **RAG-Anything**, a novel and unified framework for **Retrieval-Augmented Generation (RAG)** designed to overcome the limitations of existing text-only systems when processing real-world multimodal documents. The core innovation is a **dual-graph construction strategy** that represents diverse content—text, images, tables, and equations—as interconnected knowledge entities, capturing both cross-modal relationships and textual semantics. The paper demonstrates that this approach, paired with a **cross-modal hybrid retrieval mechanism** combining structural graph navigation and semantic matching, significantly outperforms prior state-of-the-art methods, especially in tasks requiring reasoning over **long, complex multimodal documents** in domains like finance and academic research. The research validates its claims using established benchmarks and ablation studies, emphasizing the critical role of structure-aware knowledge graphs for robust document understanding.

Source:

https://arxiv.org/pdf/2510.12323