AI 中文总结
本文提出开源机构型AI平台Westlake Scholar，新增四项AI服务并依托机构知识层实现学术关联，其西湖大学部署实例验证了系统在实际环境的可用性。
AI 中文摘要
机构知识库（IR）为研究成果的保存与传播提供了成熟的基础设施，但传统的以记录和文献为中心的界面，在将已存档论文与相关研究及人员建立关联方面支持有限。我们提出Westlake Scholar，这是一个开源的、以机构为基础的平台，它为知识库基础设施增添了四项互补的人工智能（AI）服务：上下文化论文阅读、研究方向导向的论文发现、基于出版物的专家发现，以及为学者生成的AI研究年表。这些服务依托共享的机构知识层，该层连接了经批准的出版物记录、论文内容以及学者与出版物的关系，使得同一篇论文可支持上下文阅读、跨论文发现、专家匹配以及学术成果的纵向视图。Westlake Scholar提供了一个开放且可管控的机构控制AI层实现，该层连接了知识库内容、学术发现与研究者关系，同时保留了溯源、人工审核及机构治理机制。2026年4月起在西湖大学部署运行的实例表明，该集成系统可在实际机构环境中运行。
英文摘要
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.
Comments7 pages, 6 figures
