WisPaper: Your AI Scholar Search Engine

TL;DR

WisPaper is an integrated AI-powered research tool that enhances academic discovery, organization, and tracking through semantic search, structured reasoning, and continuous recommendation, improving research workflow efficiency.

Contribution

The paper introduces WisPaper, a novel end-to-end system combining deep search and structured reasoning to address semantic search limitations and workflow fragmentation in academic research.

Findings

Achieves 22.26% recall on TaxoBench, surpassing baseline.

WisModel attains 93.70% validation accuracy.

Seamlessly integrates discovery, organization, and monitoring modules.

Abstract

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.