文本-图协同：一种双向验证与完成框架用于RAG
Text-Graph Synergy: A Bidirectional Verification and Completion Framework for RAG
AI总结：
本文提出TGS-RAG框架，通过双向机制融合文本与图结构，解决传统RAG中信息孤岛问题，提升多跳推理性能。
摘要：
检索增强生成（RAG）已成为增强大型语言模型（LLMs）事实性和多跳推理能力的核心范式。传统基于文本的RAG常检索逻辑无关的伪证据，而基于图的RAG常受限于搜索时剪枝，可能丢弃潜在有效推理路径。现有混合方法主要采用简单证据拼接或单向增强，未能解决由文本与图结构间非对称推理流导致的根本性“信息孤岛”问题。本文提出TGS-RAG，一种统一的文本-图协同增强框架。TGS-RAG引入双向机制：（i）图到文本通道采用从访问图节点的全局投票策略重新排序和细化文本证据，过滤掉语义噪声；（ii）文本到图通道利用基于记忆的孤儿实体桥接算法。该算法利用文本线索主动复活搜索历史中被剪枝的有效推理路径，无需额外数据库开销。在多个多跳推理基准上的实验结果表明，TGS-RAG显著优于现有最先进基线，实现了检索精度与计算效率之间的优越平衡。
英文摘要：
Retrieval-Augmented Generation (RAG) has become a core paradigm for enhancing factual grounding and multi-hop reasoning in Large Language Models (LLMs). Traditional text-based RAG often retrieves logically irrelevant pseudo-evidence, while graph-based RAG is frequently hindered by search-time pruning, which may discard potentially valid reasoning paths. Existing hybrid approaches primarily adopt simple evidence concatenation or unidirectional enhancement, which fails to address the fundamental "Information Island" problem caused by asymmetric reasoning flows between unstructured text and structured graphs. We propose \textbf{TGS-RAG}, a unified framework for \textbf{T}ext-\textbf{G}raph \textbf{S}ynergistic enhancement. TGS-RAG introduces a bidirectional mechanism: (i) a \textbf{Graph-to-Text} channel that employs a Global Voting strategy from visited graph nodes to re-rank and refine textual evidence, filtering out semantic noise; and (ii) a \textbf{Text-to-Graph} channel that utilizes the \textbf{Memory-based Orphan Entity Bridging} algorithm. This algorithm utilizes textual cues to proactively resurrect valid but previously pruned reasoning paths from the search history without additional database overhead. Experimental results on multiple multi-hop reasoning benchmarks demonstrate that TGS-RAG significantly outperforms state-of-the-art baselines, achieving a superior balance between retrieval precision and computational efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
