CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation

2026-09-03

AI Summary

为解决检索增强生成系统中段落集合不连贯问题，提出CAGE框架，通过构建实体图和重排序方法提升上下文一致性与回答准确性。

Abstract

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.

Problem

• Retrieval-Augmented Generation
• Coherence
• Context Set

Innovation

• Coherence-Aware Graph Encoding
• Relational Graph Convolutional Network
• between-chunk coherence