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

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.

Computation and Language (cs.CL); Information Retrieval (cs.IR)

arXiv:2609.04647 [cs.CL]

(or arXiv:2609.04647v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.04647 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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