发表机构
University of Technology Sydney; University of New South Wales（悉尼科技大学; 新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统RAG在多跳问答中存在的语义漂移与延迟问题，提出几何感知的ISO-RAG框架，通过双曲庞加莱球投影与拓扑纯化优化检索，在多跳QA基准上实现准确率与效率的双重提升。
AI 中文摘要
检索增强生成（RAG）可缓解大语言模型（LLMs）的幻觉问题，但传统密集检索难以应对多跳问答（QA）的复杂推理路径。基于图的RAG虽能捕捉多步关系，却因全局图遍历的噪声引发严重语义漂移，且在线延迟较高。为此，我们提出ISO-RAG（等周检索增强生成，ISOperimetric Retrieval-Augmented Generation），一种几何感知的RAG框架。通过将底层知识图谱投影到双曲庞加莱球中预计算节点级等周轮廓，ISO-RAG在检索期间修剪虚假边，将搜索空间限制在严格的局部子图内。这种拓扑纯化调控了驱动检索过程的个性化PageRank（PPR）扩散，确保收敛准确且延迟较低。在多跳QA基准上的实验表明，ISO-RAG在检索召回率上较现有最优基线实现了10.0%的平均绝对提升，在下游精确匹配上实现了4.3%的提升，通过从根本上消除全局遍历的延迟瓶颈，达成了更优的准确率-效率权衡。我们的源代码可在指定链接获取。
英文摘要
Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex reasoning paths of multi-hop question answering (QA). Graph-based RAG captures multi-step relationships but suffers from severe semantic drift and high online latency due to noisy global graph traversals. Thus, we propose ISO-RAG (ISOperimetric Retrieval-Augmented Generation), a geometry-aware RAG framework. By projecting the underlying knowledge graph into a hyperbolic Poincare ball to precompute node-wise isoperimetric profiles, ISO-RAG prunes spurious edges during retrieval, restricting the search space to a strictly localized subgraph. This topological purification regulates Personalized PageRank (PPR) diffusion driving the retrieval process, ensuring exact and low-latency convergence. Experiments on multi-hop QA benchmarks demonstrate that ISO-RAG outperforms state-of-the-art baselines by average absolute gains of 10.0% in retrieval recall and 4.3% in downstream exact match, achieving a superior accuracy-efficiency trade-off by fundamentally eliminating the latency bottleneck of global traversals. Our source code is available at https://github.com/ZaiizaiZHANG/ISO-RAG.
