Paper2Code: Automating Code Generation from Scientific Papers
a year ago
- #code-generation
- #machine-learning
- #llm
- PaperCoder is a multi-agent LLM framework designed to automate code generation from machine learning papers.
- The framework operates in three stages: planning, analysis, and generation, each handled by specialized agents.
- Planning involves creating a roadmap, system architecture, file dependencies, and configuration files.
- Analysis focuses on interpreting implementation-specific details from the paper.
- Generation produces modular, dependency-aware code based on the analysis.
- PaperCoder was evaluated using model-based and human evaluations, including feedback from original paper authors.
- It outperforms strong baselines in the PaperBench benchmark, demonstrating high-quality, faithful implementations.
- The tool addresses the gap in code availability for machine learning research, aiding reproducibility and further development.