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

Minju Seo, Jinheon Baek†, Seongyun Lee, and Sung Ju Hwang† († denotes equal advising)
International Conference on Learning Representations (ICLR), 2026
📄 Read the paper

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

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🗺️ Table of Contents

• ⚡ Quick Start
• 📚 Detailed Setup Instructions
• 📦 Paper2Code Benchmark Datasets
• 📊 Model-based Evaluation of Repositories

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⚡ Quick Start

• Note: The following command runs example paper (Attention Is All You Need).
• For more setup options, including LaTeX-based inputs and PDF-to-JSON conversion, see 📚 Detailed Setup Instructions.

Using OpenAI API

• 💵 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

Using Open Source Models with vLLM

• If you encounter any issues installing vLLM, please refer to the official vLLM repository.
• The default model is deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct.

pip install vllm

cd scripts
bash run_llm.sh

Output Folder Structure (Only Important Files)

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

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📚 Detailed Setup Instructions

🛠️ Environment Setup

• 💡 To use the o3-mini version, make sure you have the latest openai package installed.
• We recommend using a Python virtual environment before installing dependencies.
• 📦 Install only what you need:
  • For OpenAI API, install openai.
  • For open-source models, install vllm.
  • If you encounter any issues installing vLLM, please refer to the official vLLM repository.

pip install openai 
pip install vllm 

• Or, if you prefer, you can install all dependencies using pip:

pip install -r requirements.txt

📄 (Option) Convert PDF to JSON

The following process describes how to convert a paper PDF into JSON format.
If you have access to the LaTeX source and plan to use it with PaperCoder, you may skip this step and proceed to 🚀 Running PaperCoder.
Note: In our experiments, we converted all paper PDFs to JSON format.

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. Run 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

🚀 Running 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.

Using OpenAI API

• 💵 Estimated cost for using o3-mini: $0.50–$0.70

# Using the PDF-based JSON format of the paper
export OPENAI_API_KEY="<OPENAI_API_KEY>"

cd scripts
bash run.sh

# Using the LaTeX source of the paper
export OPENAI_API_KEY="<OPENAI_API_KEY>"

cd scripts
bash run_latex.sh

Using Open Source Models with vLLM

• The default model is deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct.

# Using the PDF-based JSON format of the paper
cd scripts
bash run_llm.sh

# Using the LaTeX source of the paper
cd scripts
bash run_latex_llm.sh

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📦 Paper2Code Benchmark Datasets

• Huggingface dataset: paper2code

• You can find the description of the Paper2Code benchmark dataset in data/paper2code.

• For more details, refer to Section 4.1 "Paper2Code Benchmark" in the paper.

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📊 Model-based Evaluation of Repositories Generated by PaperCoder

• We evaluate repository quality using a model-based approach, supporting both reference-based and reference-free settings.
  The model critiques key implementation components, assigns severity levels, and generates a 1–5 correctness score averaged over 8 samples using o3-mini-high.

• For more details, please refer to Section 4.3.1 (Paper2Code Benchmark) of the paper.

• Note: The following examples evaluate the sample repository (Transformer_repo).
  Please modify the relevant paths and arguments if you wish to evaluate a different repository.

🛠️ Environment Setup

pip install tiktoken
export OPENAI_API_KEY="<OPENAI_API_KEY>"

📝 Reference-free Evaluation

• target_repo_dir is the generated repository.

cd codes/
python eval.py \
    --paper_name Transformer \
    --pdf_json_path ../examples/Transformer_cleaned.json \
    --data_dir ../data \
    --output_dir ../outputs/Transformer \
    --target_repo_dir ../outputs/Transformer_repo \
    --eval_result_dir ../results \
    --eval_type ref_free \
    --generated_n 8 \
    --papercoder

📝 Reference-based Evaluation

• target_repo_dir is the generated repository.
• gold_repo_dir should point to the official repository (e.g., author-released code).

cd codes/
python eval.py \
    --paper_name Transformer \
    --pdf_json_path ../examples/Transformer_cleaned.json \
    --data_dir ../data \
    --output_dir ../outputs/Transformer \
    --target_repo_dir ../outputs/Transformer_repo \
    --gold_repo_dir ../examples/Transformer_gold_repo \
    --eval_result_dir ../results \
    --eval_type ref_based \
    --generated_n 8 \
    --papercoder

📄 Example Output

========================================
🌟 Evaluation Summary 🌟
📄 Paper name: Transformer
🧪 Evaluation type: ref_based
📁 Target repo directory: ../outputs/Transformer_repo
📊 Evaluation result:
        📈 Score: 4.5000
        ✅ Valid: 8/8
========================================
🌟 Usage Summary 🌟
[Evaluation] Transformer - ref_based
🛠️ Model: o3-mini
📥 Input tokens: 44318 (Cost: $0.04874980)
📦 Cached input tokens: 0 (Cost: $0.00000000)
📤 Output tokens: 26310 (Cost: $0.11576400)
💵 Current total cost: $0.16451380
🪙 Accumulated total cost so far: $0.16451380
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