MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2512.20626v1
• Date: Wed, 26 Nov 2025 05:00:03 GMT
• Title: MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation
• Authors: Chi-Hsiang Hsiao, Yi-Cheng Wang, Tzung-Sheng Lin, Yi-Ren Yeh, Chu-Song Chen,
• Abstract summary: Large language models (LLMs) struggle with high-level conceptual understanding and holistic comprehension due to limited context windows.<n>We introduce a multimodal knowledge graph-based RAG that enables cross-modal reasoning for better content understanding.<n>Our method incorporates visual cues into the construction of knowledge graphs, the retrieval phase, and the answer generation process.
• Score: 17.382062394739588
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retrieval-augmented generation (RAG) enables large language models (LLMs) to dynamically access external information, which is powerful for answering questions over previously unseen documents. Nonetheless, they struggle with high-level conceptual understanding and holistic comprehension due to limited context windows, which constrain their ability to perform deep reasoning over long-form, domain-specific content such as full-length books. To solve this problem, knowledge graphs (KGs) have been leveraged to provide entity-centric structure and hierarchical summaries, offering more structured support for reasoning. However, existing KG-based RAG solutions remain restricted to text-only inputs and fail to leverage the complementary insights provided by other modalities such as vision. On the other hand, reasoning from visual documents requires textual, visual, and spatial cues into structured, hierarchical concepts. To address this issue, we introduce a multimodal knowledge graph-based RAG that enables cross-modal reasoning for better content understanding. Our method incorporates visual cues into the construction of knowledge graphs, the retrieval phase, and the answer generation process. Experimental results across both global and fine-grained question answering tasks show that our approach consistently outperforms existing RAG-based approaches on both textual and multimodal corpora.

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