NotebookLM for
Research Papers
Transform any ML/AI paper into structured reviews, visual summaries, and actionable insights. Stop drowning in arxiv. Start understanding.
How It Works
Three steps. One minute. Complete understanding.
Paste a Link
Drop an arxiv or OpenReview URL. Or upload a PDF directly.
AI Analyzes
Multi-agent pipeline extracts, reviews, critiques, and refines.
Get Insights
Structured review + visual summary + saved to your library.
Built for ML Researchers
Every feature designed for how researchers actually work.
Structured Reviews
Get TL;DR, key contributions, technical details, and limitations — the format researchers actually need.
Instant Analysis
Paste an arxiv or OpenReview link. Get a comprehensive review in under 60 seconds.
Visual Summaries
AI-generated comic panels that capture key ideas. Perfect for presentations and sharing.
Paper Library
Build your personal knowledge base. Search, organize, and revisit papers you've analyzed.
Custom Criteria
Define your own review templates. Focus on what matters for your research area.
Deep Research
AI agents that go beyond single papers. Multi-paper synthesis and literature review.
Team Workspaces
Shared libraries for labs. Assign papers, discuss, build collective knowledge.
See It In Action
Example output from our review pipeline
The paper introduces a novel attention mechanism that reduces computational complexity from O(n²) to O(n log n) while maintaining accuracy on long-context tasks...
- → Hierarchical sparse attention pattern
- → 3.2x throughput improvement on 128K context
- → Minimal accuracy degradation (-0.3% on MMLU)
Tested only on encoder models. Decoder-only scaling unclear. No code released yet.
See 200+ real reviews at ArXivIQ →
Built by Grigory Sapunov
PhD in AI • Google Developer Expert in ML • CTO at Intento • Author of "Deep Learning with JAX" (Manning)
"I've been reading ML papers for 20 years. In 2024, I admitted defeat — I couldn't keep up anymore. So I built an AI system to help. Now I'm turning it into a tool for everyone."
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