PAGE-RAG：面向固定预算多跳检索增强生成的来源感知图证据提升方法
PAGE-RAG: Provenance-Aware Graph Evidence Promotion for Fixed-Budget Multi-hop Retrieval-Augmented Generation
AI总结：
PAGE-RAG是一种来源感知图证据提升方法，可作为插件插入现有RAG系统，在三个多跳QA基准测试中显著提升支持F1和答案F1指标
AI中文摘要：
检索增强生成（RAG）中的多跳问答通常需要检索超出最终读取范围的少量候选内容：狭窄的检索会遗漏必不可少的跳转步骤，而扩展的检索则会引入主题干扰项。这一挑战与特定的知识库格式无关，候选池可能来自独立检索器、标准RAG后端或基于图的检索流程。所需的是一个查询感知选择层，能够在生成前利用关系结构过滤候选内容。PAGE-RAG针对该场景，将图用作临时选择结构，而非假设知识库为图结构；它在检索到的候选内容上构建查询局部图，记录候选内容之间的连接原因，并将每个连接视为支持假设而非实际支持。我们将由此产生的失败模式识别为连接-支持差距：相连的候选内容不一定能支持答案。我们提出PAGE-RAG，即来源感知图证据提升方法，该方法通过相关性、源追踪元数据、特异性、中心性、噪声和连贯性信号对候选路径进行评分，并应用最小充分选择将支持事实提升为紧凑的阅读器上下文。PAGE-RAG可作为完整的检索到阅读流程，且同一提升阶段可插入现有检索或RAG系统之后，无需替换其上游检索逻辑。在相同最终预算下的三个多跳QA基准测试中，PAGE-RAG在强检索器的加权平均上，将支持F1和答案F1分别提升了10.4和3.3个百分点；作为插件，PAGE-RAG进一步改进了所有报告的RAG后端，包括面向推理的、基于压缩的、基于图的以及文档/块级系统。
英文摘要：
Multi-hop question answering in retrieval-augmented gener?ation (RAG) often benefits from retrieving beyond the few candidates that will finally be read: narrow retrieval can miss an indispensable hop, while expanded retrieval introduces topical distractors. This challenge is not tied to a particu?lar knowledge-base format. Candidate pools may come from standalone retrievers, standard RAG backends, or graph-based retrieval pipelines. What is needed is a query-aware selection layer that can use relational structure to filter candidates be?fore generation. PAGE-RAG addresses this setting by using a graph as a temporary selection structure, rather than assum?ing a graph-structured knowledge base. It builds a query-local graph over retrieved candidates, records why candidates are connected, and treats each connection as a support hypothe?sis rather than support itself. We identify the resulting failure mode as a connectivity-support gap: connected candidates do not necessarily support the answer. We propose PAGE-RAG, a Provenance-Aware Graph Evidence promotion method that scores candidate paths with relevance, source-tracing meta?data, specificity, hubness, noise, and coherence signals, and applies minimal sufficient selection to promote supporting facts into a compact reader context. PAGE-RAG can serve as a complete retrieval-to-reading pipeline, and the same promo?tion stage can be inserted after existing retrieval or RAG sys?tems without replacing their upstream retrieval logic. Across three multi-hop QA benchmarks under the same final bud?get, PAGE-RAG improves support F1 and answer F1 by 10.4 and 3.3 points on a weighted average over a strong retriever. As a plug-in, PAGE-RAG further improves all reported RAG backends, including reasoning-oriented, compression-based, graph-based, and document/chunk-level systems.
