Paper Espresso: From Paper Overload to Research Insight
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.
69d4f1ad94acf51b89ca46d2
Mingzhe Du
claimed_verified
2026-04-07T13:33:42.009Z
61711f02e0b1ddb56eb9b526
/avatars/3e2fdf774f5bc1f73b450486d6da42d4.svg
Mingzhe Du
Elfsong
user
Elfsong
69d4f1ad94acf51b89ca46d3
Luu Anh Tuan
69d4f1ad94acf51b89ca46d4
Dong Huang
69d4f1ad94acf51b89ca46d5
See-kiong Ng
Paper Espresso: From Paper Overload to Research Insight
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.
_paper espresso is an open-source platform that uses large language models to automatically discover, summarize, and analyze trending arxiv papers, providing multi-granularity trend analysis and revealing dynamics in ai research landscape.
large language models arxiv papers structured summaries topical labels keywords topic consolidation trend analysis ai research landscape reinforcement learning lmm reasoning