[2604.04562] Paper Espresso: From Paper Overload to Research Insight

Computer Science > Digital Libraries arXiv:2604.04562 (cs) [Submitted on 6 Apr 2026] Title:Paper Espresso: From Paper Overload to Research Insight Authors:Mingzhe Du, Luu Anh Tuan, Dong Huang, See-kiong Ng View a PDF of the paper titled Paper Espresso: From Paper Overload to Research Insight, by Mingzhe Du and 3 other authors View PDF HTML (experimental) Abstract:The accelerating pace of scientific publishing makes it increasingly difficult for researchers to stay current. We present Paper Espresso, an open-source platform that automatically discovers, summarizes, and analyzes trending arXiv papers. The system uses large language models (LLMs) to generate structured summaries with topical labels and keywords, and provides multi-granularity trend analysis at daily, weekly, and monthly scales through LLM-driven topic consolidation. Over 35 months of continuous deployment, Paper Espresso has processed over 13,300 papers and publicly released all structured metadata, revealing rich dynamics in the AI research landscape: a mid-2025 surge in reinforcement learning for LLM reasoning, non-saturating topic emergence (6,673 unique topics), and a positive correlation between topic novelty and community engagement (2.0x median upvotes for the most novel papers). A live demo is available at this https URL. Subjects: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.04562 [cs.DL]   (or arXiv:2604.04562v1 [cs.DL] for this version)   https://doi.org/10.48550/a...