README.md

metadata

title: Paper to Code
emoji: 🧪
colorFrom: yellow
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: mit

Paper to Code

Question

What is the engineering step after reading a paper?

System Boundary

This Space turns a method description into an implementation scaffold. It does not claim to reproduce a paper automatically; it decomposes the method into code boundaries and a checklist.

Method

The app extracts method signals from the text, optionally asks a Hugging Face-hosted language model for a structured plan, and falls back to a deterministic scaffold if no token is configured.

Technique

This is research-to-code decomposition. The method text is converted into modules, interfaces, evaluation notes, and a minimal implementation scaffold.

The key idea is to separate the scientific claim from the engineering surfaces needed to test it.

Output

The app returns a technique summary, module plan, PyTorch or evaluation code scaffold, and reproducibility checklist.

Why It Matters

Research engineering is translation: claims become modules, datasets, baselines, metrics, and tests. This Space makes that translation explicit.

What To Notice

The scaffold should not be trusted as final code. It should reveal the implementation questions: what is the encoder, what is the loss, what is the metric, and what baseline is required?

Effect In Practice

This workflow helps readers move from passive paper reading to active reproduction planning.

Hugging Face Extension

The Space can connect to paper pages, model repositories, datasets, and reproducibility reports on the Hub.

Limitations

The scaffold is a starting point. Reproducing a paper requires reading the full method, matching datasets, validating hyperparameters, and comparing against baselines.

Run Locally

pip install -r requirements.txt
python app.py