Research Paper → PyTorch Implementation

Drop a paper.
Get working code.

Four ADK agents analyze the paper, plan a minimal implementation, write complete PyTorch code, and repair it — producing a runnable Jupyter notebook in minutes.

CPU-runnable
Baseline vs paper model
Auto-repairs broken imports
Open in Colab
Upload PDF
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arXiv URL
Paste an arXiv abstract or PDF link
Example papers
The Pipeline
01
Analyze
Extract algorithms, equations, and architecture from the paper
02
Design
Plan a minimal CPU-scale implementation that captures the core idea
03
Generate
Write a complete 12-section Jupyter notebook with real PyTorch code
04
Validate
Review for missing imports, undefined variables, and broken logic
What You Get
Baseline vs Paper

Two nn.Module implementations: the competing method and the paper's proposed approach — trained side by side.

12-Section Notebook

Title, intuition, setup, data pipeline, models, training loop, experiment, inference, visualizations, and reflection.

Auto-Repaired Code

A dedicated review agent catches undefined variables, missing imports, and shape mismatches before you run a single cell.

CPU-Friendly Scale

Architectures are reduced to train in minutes on a laptop — preserve the core idea, skip the GPU cluster.

Draft + Final

Download the generated draft while validation runs in the background. Both versions available.

Open in Colab

One click publishes the notebook as a GitHub Gist and opens it directly in Google Colab.