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：本文介绍\textsc{WisPaper}，一个端到端的智能体系统，旨在彻底改变研究人员发现、组织与追踪学术文献的方式。该系统解决了两个根本性挑战：(1)~\textit{语义搜索的局限性}：现有学术搜索引擎仅能匹配关键词，无法验证论文是否真正回应复杂的研究问题；(2)~\textit{工作流碎片化}：研究人员需手动拼接用于发现、组织与监测的独立工具。\textsc{WisPaper}通过三个集成模块应对这些挑战。\textbf{学术搜索}结合快速关键词检索与\textit{深度搜索}，后者由智能体模型\textsc{WisModel}通过结构化推理，依据用户查询验证候选论文。发现的论文可一键无缝流入\textbf{文献库}，其中系统化的组织逐步构建用户画像，从而优化\textbf{AI动态}的推荐效果；该模块持续推送相关新出版物，并反过来指导后续探索，由此形成从发现到长期学术关注的闭环。在TaxoBench基准测试中，\textsc{WisPaper}实现了22.26\%的召回率，超越了O3基线（20.92\%）。此外，\textsc{WisModel}达到了93.70\%的验证准确率，有效缓解了检索幻觉问题。