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 https://huggingface.co/spaces/Elfsong/Paper_Espresso.

翻译：科研出版速度的加快使研究者越来越难以跟上最新进展。我们提出Paper Espresso，一个自动发现、总结并分析arXiv热门论文的开源平台。该系统利用大语言模型（LLMs）生成带有主题标签和关键词的结构化摘要，并通过LLM驱动的主题整合，提供日、周、月尺度的多粒度趋势分析。经过35个月持续部署，Paper Espresso已处理超过13,300篇论文，并公开所有结构化元数据，揭示了人工智能研究领域的丰富动态：2025年中旬强化学习在LLM推理中的激增、非饱和主题涌现（6,673个独特主题），以及主题新颖性与社区参与度之间的正相关性（最新颖论文的中位数点赞数达到2.0倍）。实时演示请访问https://huggingface.co/spaces/Elfsong/Paper_Espresso。