PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams

Academic researchers face an overwhelming daily flood of new publications. Static recommendation systems, which treat reading as a one-time ranking exercise, fail to capture how research interests evolve over months and years. PaperFlow models scientific reading the way it actually happens — as a longitudinal process where feedback accumulates and curiosity shifts. By maintaining a living scholarly profile and adapting continuously, the system can surface relevant work that a fixed snapshot of interests would miss. Beyond academia, this framework applies to patent monitoring, competitive intelligence, and any professional domain where staying current requires filtering vast, fast-moving information streams with personalized precision. Authors: Fuqiang Wang, Song Tan, Zheng Guo, Jiaohao Fu, Xinglong Xu, Bihui Yu, Jie Dong, Zheng Sun, Siyuan Li, Jingxuan Wei, Cheng Tan Paper: https://arxiv.org/abs/2606.07454v1