研究库 论文知识库

内容库 / 论文

arXiv:2610.01936 · RAG 检索增强

Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning

Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning

类型：arxiv
标识：2610.01936
链接：http://arxiv.org/abs/2610.01936v1
主分类：rag
形态：survey
TLDR：Large Language Models (LLMs) have demonstrated remarkable fluency across many tasks but remain limited by their static, parameter bound knowledge and their susceptibility to hallucinating information. Retrieval Augmented Generation (RAG) addresses these issues by incorporating external retrieval into the generation process, grounding model outputs in verifiable and up to date sources. While prior surveys primarily focus on core RAG architectures and standard pipelines, recent research explores broader challenges and capabilities that extend beyond these foundational designs. This survey provid
待LLM分类：否
来源文件：
/inbox/tom/_candidates/2026-10-02-agent-rag-longcontext-candidates.json

相关 · 按语义匹配

仓库 ChlorineHi/ST-Path-Survey

笔记 Tom 文献雷达 · AI Agent / RAG / 长上下文 · 2026-09-17

笔记 2026-06-16 AI Agent / RAG / 长上下文文献雷达

笔记 Tom 文献雷达 · Agent/RAG/长上下文 · 2026-08-22 08:40 UTC

笔记 Tom 文献雷达 · Agent/RAG/长上下文 · 2026-09-16 08:40 UTC

笔记 Tom 文献雷达 · Agent RAG & Long Context · 2026-09-09

arXiv

2610.01936

TLDR

Large Language Models (LLMs) have demonstrated remarkable fluency across many tasks but remain limited by their static, parameter bound knowledge and their susceptibility to hallucinating information. Retrieval Augmented Generation (RAG) addresses these issues by incorporating external retrieval into the generation process, grounding model outputs in verifiable and up to date sources. While prior surveys primarily focus on core RAG architectures and standard pipelines, recent research explores broader challenges and capabilities that extend beyond these foundational designs. This survey provid

arXiv ↗ 中文全文