✨RAG-Anything: All-in-One RAG Framework
📝 Summary:
RAG-Anything is a unified framework extending RAG to all modalities, not just text. It integrates cross-modal relationships and semantic matching via dual-graph construction and hybrid retrieval. This significantly improves performance on complex multimodal benchmarks.
🔹 Publication Date: Published on Oct 14
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/rag-anything-all-in-one-rag-framework
• PDF: https://arxiv.org/pdf/2510.12323
• Github: https://github.com/HKUDS/RAG-Anything
==================================
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✓ https://t.iss.one/DataScienceT
#RAG #MultimodalAI #MachineLearning #InformationRetrieval #GraphAI
📝 Summary:
RAG-Anything is a unified framework extending RAG to all modalities, not just text. It integrates cross-modal relationships and semantic matching via dual-graph construction and hybrid retrieval. This significantly improves performance on complex multimodal benchmarks.
🔹 Publication Date: Published on Oct 14
🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/rag-anything-all-in-one-rag-framework
• PDF: https://arxiv.org/pdf/2510.12323
• Github: https://github.com/HKUDS/RAG-Anything
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#RAG #MultimodalAI #MachineLearning #InformationRetrieval #GraphAI
✨LightRAG: Simple and Fast Retrieval-Augmented Generation
📝 Summary:
LightRAG improves Retrieval-Augmented Generation by addressing limitations of flat data representations and inadequate contextual awareness. It integrates graph structures into text indexing and retrieval, enhancing accuracy, efficiency, and response times through a dual-level system.
🔹 Publication Date: Published on Oct 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.05779
• PDF: https://arxiv.org/pdf/2410.05779
• Github: https://github.com/hkuds/lightrag
✨ Spaces citing this paper:
• https://huggingface.co/spaces/rm-lht/lightrag
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#RAG #AI #NLP #GraphAI #InformationRetrieval
📝 Summary:
LightRAG improves Retrieval-Augmented Generation by addressing limitations of flat data representations and inadequate contextual awareness. It integrates graph structures into text indexing and retrieval, enhancing accuracy, efficiency, and response times through a dual-level system.
🔹 Publication Date: Published on Oct 8, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.05779
• PDF: https://arxiv.org/pdf/2410.05779
• Github: https://github.com/hkuds/lightrag
✨ Spaces citing this paper:
• https://huggingface.co/spaces/rm-lht/lightrag
==================================
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#RAG #AI #NLP #GraphAI #InformationRetrieval
✨MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
📝 Summary:
MAGMA is a multi-graph memory architecture that improves AI agent long-context reasoning. It decouples memory representation from retrieval logic across semantic, temporal, causal, and entity graphs for query-adaptive selection, outperforming existing agentic memory systems.
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03236
• PDF: https://arxiv.org/pdf/2601.03236
==================================
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✓ https://t.iss.one/DataScienceT
#AIAgents #MemoryArchitecture #LongContextReasoning #GraphAI #ArtificialIntelligence
📝 Summary:
MAGMA is a multi-graph memory architecture that improves AI agent long-context reasoning. It decouples memory representation from retrieval logic across semantic, temporal, causal, and entity graphs for query-adaptive selection, outperforming existing agentic memory systems.
🔹 Publication Date: Published on Jan 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03236
• PDF: https://arxiv.org/pdf/2601.03236
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#AIAgents #MemoryArchitecture #LongContextReasoning #GraphAI #ArtificialIntelligence