Coherence-Aware Graph Encoding (CAGE) is introduced, a reranking framework that models"between-chunk coherence" across four dimensions: Intra-Domain Relevance, Noise Resistance, Informational Bonding, and Factual Consistency.
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.
Retrieval-Augmented Generation (RAG) systems typically apply identical retrieval logic to all user queries, overlooking that different query intents depend on fundamentally different document relationships. We propose QMEG, a query-aware retrieval framework that constructs a multi-relational evidence graph with five ac...
Jia-Run Pan, Yu-Ling Fan, Li Ma et al.· 2026 3rd International Confe...· 0 citations
SelfGraphRAG, a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever, suggests that knowledge graph structure can provide useful supervision for training graph retrievers when labeled data are unavailable.
This study proposes STaR, a novel retriever fine-tuning framework that integrates BM25 similarity graph-based soft labeling with a triplet similarity learning strategy based on Sentence-BERT (SBERT), and introduces a triplet-aware SBERT training architecture that explicitly models relative semantic distances between qu...
Jiali Jiang, Chih-Yung Chang, Youxi Li et al.· Multimedia Systems· 0 citations
Analysis of a document hierarchy-based Chunk Graph approach to improve retrieval grounding in RAG systems concludes that integrating structural-semantic relationships significantly improves context selection, specifically for documents with explicit hierarchical structures.
P. Cristin, Hilmil Pradana· JOURNAL OF APPLIED INFORMATI...· 0 citations
Retrieval-Augmented Generation (RAG) has become a fundamental paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, while recent structure-augmented approaches organize documents into graphs to improve information access, their retrieval strategies remain largely static, relying on simil...
Jinghong Lei, Wang Kun, Zhigang Chen et al.· Proceedings of the 32nd ACM...· 0 citations
Graph-based retrieval-augmented generation supports multi-hop retrieval by organizing corpus information into graphs. However, existing relation-free graph retrieval methods rely primarily on query-sentence similarity to search for evidence. This can exclude useful bridging evidence with low query similarity and activa...
Ba-Xian Liu, Tong Wei· 0 citations
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