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Kiyomi Moore

jaebumskiyomi
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8 New Types of RAG RAG techniques continuously evolve to enhance LLM response accuracy by retrieving relevant external data during generation. To keep up with current AI trends, new RAG types incorporate deep step-by-step reasoning, tree search, citations, multimodality and other effective techniques. Here's a list of 8 latest RAG advancements: 1. DeepRAG -> https://huggingface.co/papers/2502.01142 Models retrieval-augmented reasoning as a Markov Decision Process, enabling strategic retrieval. It dynamically decides when to retrieve external knowledge and when rely on parametric reasoning. 2. RealRAG -> https://huggingface.co/papers/2502.00848 Enhances  novel object generation by retrieving real-world images and using self-reflective contrastive learning to fill knowledge gap, improve realism and reduce distortions. 3. Chain-of-Retrieval Augmented Generation (CoRAG) -> https://huggingface.co/papers/2501.14342 Retrieves information step-by-step and adjusts it, also deciding how much compute power to use at test time. If needed it reformulates queries. 4. VideoRAG -> https://huggingface.co/papers/2501.05874 Enables unlimited-length video processing, using dual-channel architecture that integrates graph-based textual grounding and multi-modal context encoding. 5. CFT-RAG ->  https://huggingface.co/papers/2501.15098 A tree-RAG acceleration method uses an improved Cuckoo Filter to optimize entity localization, enabling faster retrieval. 6. Contextualized Graph RAG (CG-RAG) -> https://huggingface.co/papers/2501.15067 Uses Lexical-Semantic Graph Retrieval (LeSeGR) to integrate sparse and dense signals within graph structure and capture citation relationships 7. GFM-RAG -> https://huggingface.co/papers/2502.01113 A graph foundation model that uses a graph neural network to refine query-knowledge connections 8. URAG -> https://huggingface.co/papers/2501.16276 A hybrid system combining rule-based and RAG methods to improve lightweight LLMs for educational chatbots
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