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arXiv Computation and Language By Tong Qi, Jingyu Wu, Youbing Yin, Spencer Hong, Daben Liu, Erin Babinsky

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

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CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation introduces a reranking framework that evaluates and enhances the coherence of retrieved passages across four dimensions—Intra-Domain Relevance, Noise Resistance, Informational Bonding, and Factual Consistency. The method transforms passages into directed heterogeneous entity graphs, reweights factual anchors, encodes structural patterns with a Relational Graph Convolutional Network, and fuses inter-chunk coherence with query relevance to produce a final ranking. Evaluations on four multi‑hop benchmarks show that CAGE matches or surpasses strong baselines, improving Recall@5 on bridge‑dominated datasets and consistently boosting downstream Exact Match scores, indicating that structurally coherent context leads to more precise answers even when retrieval recall is similar or lower.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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