Retrieval-Augmented Generation for Scientific Knowledge Discovery in Interdisciplinary Research Networks
Keywords:
Retrieval-Augmented Generation, scientific knowledge discovery, interdisciplinary research, large language models, knowledge graphs, system architecture, information retrieval, fairness, sustainability, socio-technical systemsAbstract
The accelerating pace of scientific discovery increasingly depends on the ability to integrate knowledge across disparate disciplines, yet traditional literature search and synthesis methods remain fragmented and labor-intensive. Retrieval-Augmented Generation (RAG) offers a transformative paradigm for scientific knowledge discovery by combining large language models with external knowledge bases, enabling context-aware, evidence-grounded responses that can bridge disciplinary boundaries. This paper presents a comprehensive examination of RAG systems within the context of interdisciplinary research networks, focusing on architectural design, structural trade-offs, governance challenges, and deployment infrastructure. We analyze the interplay between retrieval fidelity and generative coherence, the role of knowledge graph integration, and the socio-technical implications of scaling such systems across heterogeneous domains. Particular attention is given to issues of fairness, robustness, and sustainability, as well as the policy frameworks required to support responsible adoption. Through case illustrations from fields such as biomedicine, climate science, and materials engineering, we highlight both the potential and the current limitations of RAG for facilitating cross-domain synthesis. The paper concludes with forward-looking recommendations for system governance, benchmark development, and interdisciplinary collaboration that can guide future research and deployment of RAG-driven knowledge discovery platforms.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
