Title: PaX-RAG: Path-augmented and Cross relational Graph Retrieval Augmented Generation via Structural and Semantic Reasoning
Primary area: interpretability and explainable AI
Keywords: RAG

Abstract: Retrieval-augmented generation (RAG) has significantly advanced knowledge-intensive tasks by integrating external knowledge retrieval with large language models (LLMs). However, traditional RAG methods, which rely on chunk-based retrieval, overlook the complex interrelationships between entities, limiting their ability to effectively model multi-entity facts. To overcome these limitations, we introduce PaX-RAG, a novel framework that combines retrieval-augmented generation with multi-path summaries for a more structured and coherent representation of multi-entity relationships. Unlike existing methods like GraphRAG and HyperGraphRAG, which model binary and n-ary relations, respectively, PaX-RAG aggregates multiple paths into a single summary, offering a more holistic and ordered knowledge representation without the complexity of hypergraphs. Additionally, PaX-RAG employs an optimal transport-based retrieval strategy, which enhances retrieval accuracy by preserving the structural integrity of path sets and allowing for better semantic similarity matching between multi-entity queries. We show through theoretical analysis that PaX-RAG generalizes and extends existing graph-based methods, making them special cases of our framework. Extensive experiments across diverse knowledge-intensive domains, including healthcare, law, and computer science, demonstrate that PaX-RAG outperforms traditional RAG methods as well as other graph-based methods in terms of retrieval accuracy, generation quality, and computational efficiency. Our results highlight the practical potential of PaX-RAG for real-world applications.