We present \textsc{WisPaper}, an end-to-end agent system that transforms how researchers discover, organize, and track academic literature. The system addresses two fundamental challenges. (1)~\textit{Semantic search limitations}: existing academic search engines match keywords but cannot verify whether papers truly address complex research questions; and (2)~\textit{Workflow fragmentation}: researchers must manually stitch together separate tools for discovery, organization, and monitoring. \textsc{WisPaper} tackles these through three integrated modules. \textbf{Scholar Search} combines rapid keyword retrieval with \textit{Deep Search}, in which an agentic model, \textsc{WisModel}, validates candidate papers against user queries through structured reasoning. Discovered papers flow seamlessly into \textbf{Library} with one click, where systematic organization progressively builds a user profile that sharpens the recommendations of \textbf{AI Feeds}, which continuously surfaces relevant new publications and in turn guides subsequent exploration, closing the loop from discovery to long-term awareness. On TaxoBench, \textsc{WisPaper} achieves 22.26\% recall, surpassing the O3 baseline (20.92\%). Furthermore, \textsc{WisModel} attains 93.70\% validation accuracy, effectively mitigating retrieval hallucinations.

翻译：我们提出 \textsc{WisPaper}，一个端到端代理系统，旨在革新研究人员发现、整理和追踪学术文献的方式。该系统解决了两个核心挑战：（1）\textit{语义搜索局限}：现有学术搜索引擎仅匹配关键词，但无法验证论文是否真正回应复杂研究问题；（2）\textit{工作流碎片化}：研究人员需手动拼接发现、整理与追踪等独立工具。\textsc{WisPaper} 通过三个集成模块应对这些挑战。\textbf{学者搜索}结合快速关键词检索与\textit{深度搜索}，其中代理模型 \textsc{WisModel} 通过结构化推理验证候选论文是否满足用户查询。发现的论文可一键无缝导入\textbf{图书馆}，系统性整理逐步构建用户画像，进而优化\textbf{AI动态}的推荐——该模块持续推送相关新出版物并回馈后续探索，形成从发现到长期追踪的闭环。在TaxoBench上，\textsc{WisPaper}实现22.26%召回率，超越O3基线（20.92%）。此外，\textsc{WisModel}达到93.70%验证准确率，有效缓解检索幻觉问题。