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arXiv · 2609.05072

Westlake Scholar: AI-Enhanced Scholarly Discovery over an Institutional Repository

Junshu Pan·Luodan Zhang·Yifeng Lu·Mengfan Zhao·Ming Luo·Zijie Yang·Yue Zhang·Rui Shang
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Abstract

Institutional repositories (IRs) provide mature infrastructure for preserving and disseminating research outputs, but conventional record- and document-centric interfaces provide limited support for connecting deposited papers to related research and people. We present Westlake Scholar, an open-source, institution-grounded platform that adds four complementary artificial intelligence (AI) services to repository infrastructure: contextual paper reading, research-direction-guided paper discovery, publication-grounded expert discovery, and AI-generated research chronologies for scholars. The services draw on a shared institutional knowledge layer connecting approved publication records, paper content, and scholar--publication relationships. This allows the same paper to support contextual reading, cross-paper discovery, expert matching, and longitudinal views of scholarly work. Westlake Scholar provides an open and governable implementation of an institution-controlled AI layer that connects repository content, scholarly discovery, and researcher relationships while preserving provenance, human review, and institutional governance. A deployment at Westlake University, in operation since April 2026, demonstrates that the integrated system can operate in a live institutional setting.

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Junshu Pan, Luodan Zhang, Yifeng Lu, Mengfan Zhao, Ming Luo, Zijie Yang, Yue Zhang, Rui Shang. 2026-09-04. Westlake Scholar: AI-Enhanced Scholarly Discovery over an Institutional Repository. https://arxiv.org/abs/2609.05072

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