English(EN) CAMI: Cost-Aware Agent-Guided Multi-Indexing for Semantic Retrieval

CAMI框架通过成本感知型多索引优化RAG管道

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-30 04:00

研究人员开发了CAMI（成本感知型代理引导的多索引），一个旨在优化检索增强生成（RAG）管道的语义丰富索引创建过程的新框架。该框架解决了由丰富类型和生成器模型的组合性质引起的计算瓶颈。CAMI将索引构建形式化为一个有预算的多目标投资组合选择问题，能够在严格的预算限制下有效识别高召回率的投资组合。评估表明，与仅基于内容的基线相比，CAMI的recall@10可以提高高达9.4%，并且比随机搜索方法使用的预算少5倍，使其在生产环境中具有实用性。 AI

影响优化RAG管道，可能提高AI应用的搜索效率和召回率。

排序理由该项目是一篇研究论文，详细介绍了一种新的语义检索框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该项目是一篇研究论文，详细介绍了一种新的语义检索框架。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 100 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Adnan Qidwai, Anand Eswaran, Sonam Mishra, Jaydeep Sen, Sachindra Joshi · 2026-06-30 04:00

CAMI：成本感知型代理引导多索引语义检索

arXiv:2606.28365v1 Announce Type: cross Abstract: RAG ingestion pipelines frequently augment search corpus index with semantic enrichment indices (e.g., synthetic queries or summaries generated from corpus chunks) that are subsequently queried alongside the base index to improve …

本文如何被排名

报道来源 [1]

CAMI：成本感知型代理引导多索引语义检索

相关实体

相关话题