Awesome Little Red Dots

An open-science initiative to centralize and analyze literature on "Little Red Dots" (LRDs)

🔴 What are Little Red Dots (LRDs)?

Little Red Dots (LRDs) are a newly discovered population of faint, red, compact sources revealed by JWST observations. They are believed to be active galactic nuclei (AGNs) at high redshifts (z~4-6), representing the early growth phase of supermassive black holes in the early universe. Understanding LRDs helps us trace the origins of supermassive black holes and galaxy formation in the early universe.

📚 About This Project

Awesome-Little-Red-Dots is an open-science initiative to centralize and analyze the rapidly growing literature on “Little Red Dots” (LRDs). This repository:

  • Maintains a comprehensive, up-to-date bibliography of LRD-related research
  • Provides an accessible website for browsing and filtering the literature
  • Uses AI to tag and categorize papers by their key focus areas
  • Facilitates community collaboration on understanding early SMBH growth and galaxy formation

🌐 Browse the bibliography: https://www.wenkeren.com/Awesome-Little-Red-Dots/

💻 GitHub Repository: https://github.com/WenkeRen/Awesome-Little-Red-Dots

🤖 AI-Powered Paper Tagging

One of the unique aspects of this project is the use of AI to automatically categorize papers:

  1. Structured Taxonomy: We maintain a structured taxonomy of LRD research areas
  2. AI Analysis: Our system processes papers through the Qwen-Max API (Aliyun’s large language model)
  3. Smart Categorization: For each paper, the AI analyzes the title and abstract to select up to 5 relevant tags
  4. Consistent Organization: Tags are stored in BibTeX entries and displayed on the website
  5. Easy Discovery: Tags help researchers quickly identify papers focusing on specific aspects

🔄 Automated Data Collection

The bibliography is automatically updated through several mechanisms:

  • Daily ADS API Queries: Automated searches of NASA’s ADS API for new LRD papers
  • Smart Categorization: Papers are sorted into articles and proposals
  • GitHub Actions: Daily updates at 6:00 AM UTC keep the bibliography current
  • Metrics Integration: DOI-based papers include Dimensions and Altmetric data
  • Community Curation: Manual contributions ensure comprehensive coverage

🌐 Website Features

The project website provides:

  • Searchable Bibliography: Filterable database of all LRD papers
  • Tag-Based Filtering: Find papers on specific research aspects
  • Comprehensive Links: DOIs, ADS entries, and arXiv preprints
  • Rich Metadata: Paper abstracts and citation information
  • Modern Interface: Clean, responsive design built with Jekyll

🎯 Impact & Goals

This project aims to:

  • Accelerate Research: Reduce time spent searching for relevant literature
  • Foster Collaboration: Connect researchers working on similar aspects of LRDs
  • Track Progress: Visualize the evolution of LRD research over time
  • Support Discovery: Help identify research gaps and emerging trends
  • Promote Open Science: Make scientific literature more accessible and organized

🚀 Technical Implementation

The project leverages modern tools and practices:

  • Python Scripts: Automated data collection and processing
  • API Integration: NASA ADS and AI services for content enrichment
  • Jekyll Website: Static site generation for fast, reliable hosting
  • GitHub Actions: Continuous integration for automated updates
  • BibTeX Management: Standard bibliography format for compatibility

This initiative represents a new approach to managing rapidly evolving scientific literature, combining traditional bibliography practices with modern AI and automation technologies.