在原有代码的基础上增加了对ollama与deepseek api的支持

详见 scripts/run_ollama.sh, run_deepseek.sh

📄 Read the paper on arXiv

PaperCoder is a multi-agent LLM system that transforms 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.

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

• 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

• 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

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

• 💡 To use the o3-mini version, make sure you have the latest openai package installed.
• 📦 Install only what you need:
  • For OpenAI API: openai
  • For open-source models: 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

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

• 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.

• 💵 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

• 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

• 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.

• 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.

pip install tiktoken
export OPENAI_API_KEY="<OPENAI_API_KEY>"

• 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

• 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

========================================
🌟 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
============================================