论文浓缩咖啡：从文献过载到研究洞见

摘要

科学出版加速化使得研究人员愈发难以追踪前沿动态。本文推出开源平台Paper Espresso，该系统能自动发现、总结并分析热门的arXiv论文。通过大语言模型生成带主题标签与关键词的结构化摘要，并借助LLM驱动的主题整合技术实现日/周/月多粒度趋势分析。在持续部署的35个月间，该系统已处理超13,300篇论文并公开全部结构化元数据，揭示了AI研究领域的丰富动态：2025年中旬涌现的LLM推理强化学习热潮、非饱和性主题增长（6,673个独立主题）以及主题新颖度与社区参与度的正相关性（最具新颖性论文的中位数点赞量提升2.0倍）。实时演示详见：https://huggingface.co/spaces/Elfsong/Paper_Espresso。

English

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.

论文浓缩咖啡：从文献过载到研究洞见

Paper Espresso: From Paper Overload to Research Insight

摘要

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