Keeping You Informed
The Guardrail is your daily curated feed of AI safety research, automatically filtering and summarizing the latest papers so you never miss important work.
Why The Guardrail?
AI safety research is advancing rapidly. With hundreds of papers published daily across arXiv's AI and ML categories, staying current with safety-relevant work has become a significant challenge. The Guardrail solves this by automatically identifying, categorizing, and summarizing papers that matter for AI safety.
Whether you're a researcher, engineer, policy maker, or simply someone who cares about the safe development of AI systems, The Guardrail helps you stay informed without the overwhelming task of manual paper triage.
How It Works
Our fully automated pipeline runs daily, powered by Google's Gemini Flash 3 model for intelligent paper analysis.
Daily Paper Ingestion
Every day at 6:00 AM UTC, we fetch new papers from arXiv's AI/ML categories including cs.AI, cs.LG, cs.CL, cs.CV, stat.ML, and more using their public API.
AI-Powered Relevance Filtering
Each paper's title and abstract is analyzed by Google Gemini Flash 3 to determine AI safety relevance. Only papers scoring above 60% relevance confidence are included.
Intelligent Categorization
Relevant papers are automatically categorized into our 10-category AI safety taxonomy using Gemini Flash 3, enabling easy filtering and discovery.
Concise Summarization
Each paper receives a focused 1-2 sentence summary highlighting its key contribution to AI safety, making it easy to quickly assess relevance.
Automated Deployment
The processed papers are committed to our repository and the website is automatically rebuilt and deployed via GitHub Actions to GitHub Pages.
The pipeline runs automatically every day at 6:00 AM UTC. New papers typically appear within 24-48 hours of their arXiv submission, depending on arXiv's own processing times.
Category Taxonomy
Papers are automatically tagged with one or more categories from our AI safety taxonomy.
AI Control
Research on maintaining human oversight and control over AI systems
RLHF
Reinforcement Learning from Human Feedback and preference learning
I/O Classifiers
Input/output monitoring, content filtering, and safety classifiers
Mechanistic Interpretability
Understanding internal model representations and circuits
Position Paper
Opinion pieces, policy proposals, and theoretical frameworks
Alignment Theory
Foundational alignment research, goal specification, value learning
Robustness & Security
Adversarial robustness, jailbreaking, prompt injection defenses
Evaluations & Benchmarks
Safety evaluations, capability assessments, red-teaming
Governance & Policy
AI governance, regulation, responsible deployment practices
Agent Safety
Safety considerations for autonomous AI agents and tool use
Limitations
- • Imperfect classification: LLM-based filtering isn't perfect. Some relevant papers may be missed, and some less relevant papers may be included.
- • AI-generated summaries: Summaries are generated by Gemini Flash 3 and may not perfectly capture all nuances of the research.
- • Abstract-only analysis: Category assignments and relevance scores are based on titles and abstracts only, not full paper content.
- • Processing delays: Papers typically appear 24-48 hours after arXiv submission due to processing times.
Built By
Acknowledgments
Thank you to arXiv for use of its open access interoperability. This project is not affiliated with or endorsed by arXiv.
Paper metadata and abstracts are sourced from arXiv under their API Terms of Use. Individual papers may have their own licenses.
This project uses Google Gemini for AI-powered paper analysis, Astro for static site generation, and is deployed on GitHub Pages.
Open Source
The Guardrail is open source. Contributions, bug reports, and feature suggestions are welcome.
View on GitHub