Paper Espresso: From Paper Overload to Research Insight
This addresses the challenge for researchers to stay current with rapidly publishing scientific literature, though it is incremental as it applies existing LLM methods to new data for analysis.
The paper tackles the problem of information overload in scientific publishing by developing Paper Espresso, an open-source platform that automatically processes and analyzes arXiv papers, revealing trends such as a surge in reinforcement learning for LLM reasoning and a positive correlation between topic novelty and engagement (2.0x median upvotes for the most novel papers).
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.