Unverified50% confidenceFactExact time
Multimodal RAG is an architecture that extends retrieval-augmented generation from plain text to mixed image-text corpora; a single retrieval often recalls multiple images, so the input is inherently multi-image.
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Long-term
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10/14/2026
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Unverified在RAG架构中，文本块比图片具有更高的向量化质量，图片因无法直接向量化成为信息孤岛，预先转化为文字描述能纳入检索体系77% similarUnverifiedGraph-based RAG enhances multi-hop reasoning by constructing knowledge graphs and traversing entity relationships, but introduces higher reasoning complexity and latency.62% similarUnverifiedThe core technique for achieving multi-style fusion in diffusion models is the cross-attention mechanism, where in each layer of the U-Net, image features serve as Query and text embeddings serve as Key and Value.61% similarUnverifiedThe core mechanism for addressing hallucinations is Retrieval-Augmented Generation (RAG), which injects knowledge base content into the context in real time.61% similarUnverifiedMultimodal embedding requires data of different modalities (text, images, audio, etc.) to be mapped into the same coordinate system, so that no cross-model score alignment is needed at retrieval time.61% similar
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