📄 Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

📄 Read the paper on arXiv

PaperCoder is a multi-agent LLM system that transforms paper into code repository. It follows a three-stage pipeline: planning, analysis, and code generation, each handled by specialized agents.
Our method outperforms strong baselines both on Paper2Code and PaperBench and produces faithful, high-quality implementations.

---

⚡ QuickStart

• Note: The following command runs example paper (Attention Is All You Need).
• 💵 Estimated cost for using o3-mini: $0.50–$0.70

pip install openai

export OPENAI_API_KEY="<OPENAI_API_KEY>"

cd scripts
bash run.sh

Output Folder Structure (Only Important Files)

outputs
├── Transformer
│   ├── analyzing_artifacts
│   ├── coding_artifacts
│   └── planning_artifacts
└── Transformer_repo  # Final output repository

---

📚 Detailed Setup Instructions

🛠️ Environment Setup

• Note: If you wish to use the o3-mini version, please make sure to install the latest version of the OpenAI package.

pip install openai

📄 Convert PDF to JSON

1. Clone the s2orc-doc2json repository to convert your PDF file into a structured JSON format.
   (For detailed configuration, please refer to the official repository.)

git clone https://github.com/allenai/s2orc-doc2json.git

2. Running the PDF processing service.

cd ./s2orc-doc2json/grobid-0.7.3
./gradlew run

3. Convert your PDF into JSON format.

mkdir -p ./s2orc-doc2json/output_dir/paper_coder
python ./s2orc-doc2json/doc2json/grobid2json/process_pdf.py \
    -i ${PDF_PATH} \
    -t ./s2orc-doc2json/temp_dir/ \ 
    -o ./s2orc-doc2json/output_dir/paper_coder

🚀 Runing PaperCoder

• Note: The following command runs example paper (Attention Is All You Need).
  If you want to run PaperCoder on your own paper, please modify the environment variables accordingly.

export OPENAI_API_KEY="<OPENAI_API_KEY>"

cd scripts
bash run.sh