Title: Topological Retrieval-Augmented Generation via Intersecting Evidence Paths
Primary area: generative models
Keywords: Retrieval-Augmented Generation, Topological Reranking, Evidence Path, Lowest Common Ancestor

Abstract: Retrieval-Augmented Generation (RAG) struggles with complex queries. While multi-query rewriting enhances recall by capturing diverse semantic dimensions, existing methods falter by consolidating retrieved documents into a flat list for reranking. This discards the crucial structural information from the rewriting process and fails to prioritize documents that bridge different query aspects. To address this issue, we propose HPT-TRACE, a framework that centers on a novel topology-aware reranking mechanism. This framework functions within a topological space defined by our Hierarchical Partition Tree (HPT), which is construction-efficient and does not rely on Large Language Models (LLMs). Our innovative Topological Reranking via Ancestor Convergence Evaluation (TRACE) algorithm operates within this HPT-defined space. Rather than scoring documents in isolation, TRACE considers each document's lineage in the tree as an evidence path. It then reranks candidates by assessing the intersection length of evidence paths originating from different semantic dimensions of the user's query. A document is deemed essential for synthesizing a comprehensive answer if its path contributes to an intersection of substantial length. By explicitly modeling the relationships between intersecting evidence paths, HPT-TRACE provides a framework that is both highly effective and computationally efficient, excelling at identifying the most salient and holistic information to significantly enhance retrieval for complex queries.