AI助手帮你精准找论文，自动整理并持续推送新文献。
WisPaper: Your AI Scholar Search Engine
- 用智能代理结合关键词与深度推理验证论文相关性
- 在TaxoBench上召回率达22.26%，验证准确率93.70%
- 适合需要长期跟踪领域进展的研究者
我们提出 extsc{WisPaper}，一个端到端的智能研究助理系统，革新学者发现、组织与追踪学术文献的方式。针对现有搜索工具仅匹配关键词而无法验证论文是否真正回应复杂问题（语义搜索局限性），以及研究流程分散需手动整合多个工具（工作流碎片化）两大挑战， extsc{WisPaper} 通过三个模块协同解决： extbf{学者搜索} 结合快速关键词检索与 extit{深度搜索}，由智能体模型 extsc{WisModel} 通过结构化推理验证候选论文与用户问题的一致性；被发现的论文可一键导入 extbf{文献库}，系统基于用户行为逐步构建个人画像，优化 extbf{AI 推荐流} 的推荐质量，实现从发现到持续感知的闭环。在 TaxoBench 基准测试中， extsc{WisPaper} 召回率达 22.26%，优于 O3 基线（20.92%）； extsc{WisModel} 验证准确率达 93.70%，有效缓解了检索幻觉。
原文摘要 · Abstract (English)
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.
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