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.

翻译：科学文献的快速增长加剧了对可扩展、可信赖的知识综合系统的需求，以整合跨领域文献中的知识。尽管近期的检索增强生成（RAG）方法提升了科学信息的可访问性，但它们往往忽视引文图结构、对复杂查询的适应性较差，且生成零散、难以验证的综合结果。本文提出SciRAG，一个用于科学文献探索的开源框架，通过三项关键创新解决上述不足：（1）自适应检索，灵活交替采用顺序与并行证据收集策略；（2）引文感知的符号推理，利用引文图组织与筛选支持文档；（3）提纲引导的综合生成，通过规划、批判与精炼答案确保连贯性与透明归因。在QASA和ScholarQA等多个基准上的广泛实验表明，SciRAG在事实准确性与综合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。