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arXiv · 2609.04647

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

Tong Qi·Jingyu Wu·Youbing Yin·Spencer Hong·Daben Liu·Erin Babinsky
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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.

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Tong Qi, Jingyu Wu, Youbing Yin, Spencer Hong, Daben Liu, Erin Babinsky. 2026-09-04. CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation. https://arxiv.org/abs/2609.04647

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