English(EN)When Is Graph Structure Worth Its Cost? The Case for Structure Pricing in Retrieval-Augmented Generation
新的RAG研究聚焦于查询感知路由和归因
作者PulseAugur 编辑部·[8 个来源]·
三篇新研究论文探讨了检索增强生成（RAG）技术的进展。第一篇论文介绍了一个查询感知的源风险分类层，通过路由查询进行审查和分配检索到的页面来改进RAG管道。第二篇论文ORDER提出了一个任务条件路由框架，该框架动态地将索引和检索适应于特定查询。第三篇论文研究了RAG中上下文压缩与引用归因之间的权衡，分析了各种压缩方法及其对答案质量和出处的影响。
AI
arXiv:2506.17277v2 Announce Type: replace-cross Abstract: The retrieval stage of retrieval-augmented generation (RAG) for scientific question answering depends on how documents are segmented and how chunks are represented in embedding space. This dependence is especially relevant…
arXiv:2609.18099v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (RAG) can help answer questions that require information from many documents. However, building a graph often requires many language-model calls during ingestion. It is therefore important …
arXiv cs.AI
TIER_1English(EN)·Kainan Zhou (Google LLC), Gangzhen Qian (Google LLC), Chuhong Xu (Sony Corporate of America), Lu Yi (Google LLC)·
arXiv:2609.16564v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) pipelines may omit a source's material relationship to the query. We study a pre-generation triage layer that treats this relationship as query dependent. The method routes canonical query famili…
arXiv cs.AI
TIER_1English(EN)·Aur\'elien Pellet (LRE), Julien Perez, Marie Puren·
arXiv:2609.17012v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) pipelines typically rely on a fixed indexing and retrieval configuration determined at preprocessing time. This one-size-fits-all design is ill-suited to domain-expert settings, where heterogeneo…
arXiv:2609.14245v1 Announce Type: cross Abstract: Context compression reduces generator input in retrieval-augmented generation, but answer quality alone does not characterize citation attribution. We measure citation attribution across compression methods and budgets, comparing …
Agentic Retrieval-Augmented Generation (RAG) has become a promising paradigm for multi-hop question answering, where a reasoning model iteratively issues queries to a retriever and incorporates newly retrieved context into subsequent reasoning steps. While this iterative process …
Integrating recall and pre-ranking in e-commerce search requires candidate generation to account for relevance, personalization, and business value before final ranking. To this end, we present VARG, a generative retrieval system for Tmall App search that directly admits generate…
dev.to — LLM tag
TIER_1English(EN)·Amaresh Pelleti·
<blockquote> <p>Originally published on <a href="https://devtoolhub.com/what-is-rag/" rel="noopener noreferrer">DevToolHub</a>.</p> </blockquote> <p>What is RAG, in one sentence? A way to make an LLM answer questions using documents it was never trained on, by searching those doc…
