SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature
Hang Ding, Yilun Zhao, Tiansheng Hu, Manasi Patwardhan, Arman Cohan

TL;DR
SciRAG is a novel framework that enhances scientific literature synthesis by integrating adaptive retrieval, citation-aware reasoning, and outline-guided synthesis, leading to more accurate and trustworthy scientific knowledge aggregation.
Contribution
The paper introduces SciRAG, a comprehensive system combining adaptive retrieval, citation graph reasoning, and outline-guided synthesis for improved scientific literature exploration.
Findings
Outperforms prior systems in factual accuracy
Achieves higher synthesis quality on benchmarks
Establishes a new foundation for scientific knowledge aggregation
Abstract
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…
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Advanced Graph Neural Networks · Information Retrieval and Search Behavior
