CAGE：用于检索增强生成的感知一致性图编码
CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation
AI总结：
本文针对传统RAG系统上下文集缺乏整体一致性的问题，提出CAGE重排序框架，通过多维度建模段落间一致性并结合关系图卷积网络等技术，在多跳基准上提升了检索增强生成的下游表现。
AI中文摘要：
传统检索增强生成（RAG）系统会针对查询独立对每个段落打分，所组装的上下文集可能各段落单独相关，但整体缺乏一致性。本文提出感知一致性图编码（Coherence-Aware Graph Encoding，CAGE），这是一种重排序框架，从四个维度对“段落间一致性”建模：域内相关性、抗噪性、信息联结性和事实一致性。该流程将检索到的段落转化为有向异质实体图，通过最小出度重加权放大事实锚点，利用关系图卷积网络编码结构模式，再将段落间一致性与查询相关性融合以完成最终排序。在四个多跳基准上的评估显示，在以桥接为主的数据集上，CAGE在Recall@5指标上与monoT5等强基准相当或更优，且始终提升下游的精确匹配指标，表明即便检索召回率相当或更低，结构一致的上下文仍能产生更精准的答案。
英文摘要：
Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individually relevant yet collectively incoherent. We introduce Coherence-Aware Graph Encoding (CAGE), a reranking framework that models "between-chunk coherence" across four dimensions: Intra-Domain Relevance, Noise Resistance, Informational Bonding, and Factual Consistency. Our pipeline transforms retrieved passages into directed heterogeneous entity graphs, amplifies factual anchors via min-out-degree reweighting, encodes structural patterns through a Relational Graph Convolutional Network, and fuses inter-chunk coherence with query relevance for final ranking. Evaluated across four multi-hop benchmarks, CAGE matches or outperforms strong baselines including monoT5 in Recall@5 on bridge-dominated datasets and consistently improves downstream Exact Match, demonstrating that structurally coherent context yields more precise answers even when retrieval recall is comparable or lower.
