arXiv:2510.16152v2 Announce Type: replace-cross Abstract: Scientific literature is increasingly fragmented by disciplinary boundaries, specialized terminology, and potentially sparse keyword systems, making it difficult to capture the evolving structure of modern science. This study introduces a large language model (LLM)-driven framework for mapping scientific literature from a topic modeling perspective. The approach is demonstrated on a 20-year corpus of more than 1,500 engineering-related articles published in the Proceedings of the National Academy of Sciences (PNAS). A two-stage classifi
The proliferation of scientific literature and the advancement of large language models create an immediate need and opportunity for better navigation and understanding of fragmented knowledge domains.
This development could significantly enhance knowledge discovery and interdisciplinary research by providing more effective tools for mapping and understanding scientific fields, impacting innovation cycles and research efficiency.
The ability to automatically map scientific literature using AI will change how researchers find relevant information, identify emerging trends, and potentially foster new connections across disciplines.
- · Academic researchers
- · R&D intensive industries
- · AI/ML developers
- · Science funding bodies
Researchers gain improved tools for literature review and trend identification within specific fields.
Interdisciplinary collaboration and the identification of new research frontiers become more efficient and commonplace.
The pace of scientific discovery and technological innovation accelerates due to enhanced knowledge synthesis and reduced fragmentation.
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