SciRAG: Adaptive, Citation-Aware, and Outline-Guided Retrieval and Synthesis for Scientific Literature

2025-11-18

AI Summary

The explosive growth of scientific literature necessitates scalable, verifiable knowledge synthesis systems; however, existing retrieval-augmented generation (RAG) approaches ignore citation graph structure, struggle with complex queries, and produce fragmented, non-attributable outputs. To address these limitations, we propose the first open-source knowledge synthesis framework tailored for scientific literature. Our method innovatively integrates adaptive retrieval, citation-aware symbolic reasoning, and outline-guided generation: it dynamically retrieves evidence, filters and hierarchically organizes documents using citation graphs, and generates structured, fully traceable answers. The framework unifies RAG, graph analytics, sequence modeling, and iterative planning, supporting both parallel and sequential evidence collection. Evaluated on benchmarks including QASA and ScholarQA, it achieves significant improvements in factual accuracy and synthesis coherence. This work establishes a trustworthy, scalable paradigm for large-scale scientific knowledge aggregation.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Acquisition
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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 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.

Problem

• Addresses scalable synthesis of scientific literature for trustworthy knowledge aggregation
• Overcomes limitations in citation graph utilization and query adaptation
• Improves fragmented synthesis through structured organization and verification

Innovation

• Adaptive retrieval alternates sequential and parallel evidence gathering
• Citation-aware reasoning leverages citation graphs to filter documents
• Outline-guided synthesis plans and refines answers for coherence