自动分析arXiv论文趋势，助你快速捕捉AI研究热点。
Paper Espresso: From Paper Overload to Research Insight
- 用大模型自动生成带主题标签的结构化摘要。
- 35个月处理1.33万篇论文，发现2025年强化学习推理爆发。
- 新颖话题获赞率高2倍，适合追踪前沿的研究者。
科研出版速度加快，研究人员难以跟上最新进展。我们推出开源平台 Paper Espresso，可自动发现、总结并分析 arXiv 上的热门论文。系统利用大语言模型（LLMs）生成带主题标签和关键词的结构化摘要，并通过 LLM 驱动的主题聚合，实现日、周、月多粒度趋势分析。在连续部署35个月中，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动态：2025年中强化学习用于大模型推理出现显著增长；话题非饱和涌现（共6,673个独特主题）；且话题新颖性与社区参与度呈正相关（最新颖论文的平均点赞数为2.0倍中位数）。在线演示可在 https://huggingface.co/spaces/Elfsong/Paper_Espresso 查看。
原文摘要 · Abstract (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.
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