arXiv:2609.15830 · RAG 检索增强
CiteGuard-RAG: A Validation-Centered AI System for Evidence-Grounded Question Answering
CiteGuard-RAG：一种以验证为中心的、基于证据的问答 AI 系统
CiteGuard-RAG: A Validation-Centered AI System for Evidence-Grounded Question Answering
- 类型：arxiv
- 标识：2609.15830
- 链接：http://arxiv.org/abs/2609.15830v1
- 主分类：rag
- 形态：method
- TLDR：Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are grounded, citation-valid, or appropriately refused. This paper introduces CiteGuard-RAG, a validation-centered AI system for evidence-grounded question answering. The system integrates hybrid semantic-lexical retrieval, citation-constrained generation, sentence-level grounding validation, and single-pass regeneration. Validation is used at runtime to determine whether a candidate answer should be accepted, refused, or regenerated before final deliv
- 待LLM分类：否
- 标题中文：CiteGuard-RAG：一种以验证为中心的、基于证据的问答 AI 系统
- TLDR中文：Retrieval-Augmented Generation (RAG) 可改善对复杂信息的访问，但仅检索证据并不能保证答案有据可依、引用有效或被合理拒绝。本文提出 CiteGuard-RAG，一种以验证为中心的、基于证据的问答 AI 系统。该系统集成了混合语义-词法检索、引用约束生成、句子级 grounding 验证与单遍重新生成机制。验证在运行时用于判断候选答案应被接受、拒绝或在最终交付前重新生成。
- 来源文件：
- /inbox/tom/_candidates/2026-09-16-agent-rag-longcontext-candidates.json