Paper2Code
Automating code generation from scientific ML papers using LLMs
Paper2Code automates the process of converting machine learning research papers into runnable code — bridging the gap between theory and reproducibility.
Motivation
ML research reproducibility is a known challenge. Paper2Code reduces the barrier by automatically extracting algorithms and experimental setups from papers and generating corresponding implementation code via LLMs.
Approach
- Parse scientific paper structure (abstract, methods, experiments)
- Extract algorithmic pseudocode and architecture descriptions
- Generate runnable Python/PyTorch implementations via LLM prompting
- Validate against paper-reported results where possible
Tech Stack
Python · LLMs · PDF Parsing · PyTorch · LangChain