SciRAG：面向科学文献的自适应、感知引用、大纲引导的检索与合成框架
SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature
AI总结：
针对现有科学文献RAG方法忽略引用结构、适配性差、合成内容难验证的问题，本文提出开源框架SciRAG，通过自适应检索、引用感知推理、大纲引导合成三项创新，在多个基准上取得了更优的事实准确性与合成质量。
摘要：
科学出版物的快速增长使得人们对可扩展、可信的跨文献知识合成系统的需求愈发迫切。尽管近期的检索增强生成（RAG）方法提升了科学信息的获取效率，但这类方法往往忽略引用图结构，对复杂查询的适配性较差，且生成的合成内容零散、难以验证。本文提出开源科学文献探索框架SciRAG，通过三项核心创新解决上述缺陷：一是自适应检索，可在顺序式与并行式证据收集间灵活切换；二是感知引用的符号推理，利用引用图组织并过滤支撑文档；三是大纲引导的合成，通过规划、评判、优化答案保障内容连贯性与归属透明性。在QASA、ScholarQA等多个基准上开展的大量实验表明，SciRAG在事实准确性与合成质量上优于现有系统，为可靠的大规模科学知识聚合奠定了新基础。
英文摘要：
The accelerating growth of scientific publications has intensified the need for scalable, trustworthy systems to synthesize knowledge across diverse literature. While recent retrieval-augmented generation (RAG) methods have improved access to scientific information, they often overlook citation graph structure, adapt poorly to complex queries, and yield fragmented, hard-to-verify syntheses. We introduce SciRAG, an open-source framework for scientific literature exploration that addresses these gaps through three key innovations: (1) adaptive retrieval that flexibly alternates between sequential and parallel evidence gathering; (2) citation-aware symbolic reasoning that leverages citation graphs to organize and filter supporting documents; and (3) outline-guided synthesis that plans, critiques, and refines answers to ensure coherence and transparent attribution. Extensive experiments across multiple benchmarks such as QASA and ScholarQA demonstrate that SciRAG outperforms prior systems in factual accuracy and synthesis quality, establishing a new foundation for reliable, large-scale scientific knowledge aggregation.
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。
