做学术推荐系统或信息检索的团队，终于有了一个能处理每日动态兴趣变化的框架，PaperFlow 的纵向基准和盲评协议可以直接复用。
论文推荐通常被当作静态排序问题，但科研阅读是每日动态过程，兴趣会变化、反馈会积累。PaperFlow 提出三阶段框架：Profiling 从异构冷启动证据构建结构化用户画像；Recommending 在固定展示预算下对每日论文流进行多信号聚合排序；Adapting 根据语义不同的反馈信号更新用户状态并建模兴趣漂移。研究还构建了包含 24 个模拟用户、50 天论文流、1200 个用户-天片段的纵向基准，并设计了盲人评估协议。实验表明 PaperFlow 在基于 oracle 的排序、模拟阅读行为对齐和盲人评估上均优于五个基线。
PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams
Scientific paper recommendation is typically evaluated as static ranking over a fixed candidate set, yet real scientific reading unfolds as a daily, longitudinal process in which interests shift and feedback accumulates. We introduce PaperFlow, a framework that organizes it into three coupled stages: Profiling, which constructs and maintains a structured, inspectable scholarly profile from heterogeneous cold-start evidence; Recommending, which ranks each date-specific paper stream through multi-signal aggregation under a fixed display budget; and Adapting, which updates user state from semantically distinct feedback signals and models interest drift across days. We further define a longitudinal user-day benchmark that fixes users, dates, candidate pools, visible inputs, and hidden simulated relevance labels under a shared temporal information boundary. The benchmark contains 24 simulated research users, 50 daily paper streams, 1,200 user-day episodes, 20,727 unique papers, and 497,448 episode-paper records. We additionally specify a blind human-evaluation protocol to validate alignment between automatic metrics and expert judgments. Experiments against five scientific recommendation baselines show that PaperFlow achieves the strongest oracle-based ranking, the highest behavioral alignment with simulated reading selections, and the best blind human-evaluation score.