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rag

检索增强、向量检索、重排、GraphRAG

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2026​

  • User as Code: Executable Memory for Personalized Agents — paper=user-as-code-executable-memory-for@arXiv:2606.16707v1

2025​

  • Making Large Language Models Efficient Dense Retrievers — paper=making-large-language-models-efficient-dense@arXiv:2512.20612v1
  • MCP-Zero: Active Tool Discovery for Autonomous LLM Agents — paper=mcp-zero@arXiv:2506.01056v4

2024​

  • Evaluating Very Long-Term Conversational Memory of LLM Agents — paper=evaluating-very-long-term-conversational-memory@arXiv:2402.17753v1
  • From Local to Global: A Graph RAG Approach to Query-Focused Summarization — paper=graphrag@arXiv:2404.16130v2
  • Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models — paper=late-chunking-contextual-chunk-embeddings-using@arXiv:2409.04701v3

2023​

  • Dense X Retrieval: What Retrieval Granularity Should We Use? — paper=dense-x-retrieval-what-retrieval-granularity@arXiv:2312.06648v3
  • Lost in the Middle: How Language Models Use Long Contexts — paper=lost-in-the-middle@arXiv:2307.03172v3
  • Ragas: Automated Evaluation of Retrieval Augmented Generation — paper=ragas@arXiv:2309.15217v2

2022​

  • MTEB: Massive Text Embedding Benchmark — paper=mteb@arXiv:2210.07316v3
  • Precise Zero-Shot Dense Retrieval without Relevance Labels — paper=hyde@arXiv:2212.10496v1

2020​

  • Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — paper=rag@arXiv:2005.11401v4

2018​

  • Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge — paper=think-you-have-solved-question-answering@arXiv:1803.05457v1