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

**Authors:** Minju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang  
**arXiv:** [2504.17192](https://arxiv.org/abs/2504.17192)  
**Date:** 2025-04-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2504.17192v1/

## Key points

- Lack of available code implementations hinders researchers from reproducing results and building upon prior work
- Recent advancements in Large Language Models (LLMs) show promise in understanding scientific documents and generating high-quality code
- PaperCoder framework introduced to automatically transform machine learning papers into functional code repositories through planning, analysis, and generation stages
- Relies on multi-agent LLM technology to generate executable code repositories directly from research papers without partial implementations or human inputs
- Extensive evaluations showed PaperCoder outperformed baselines in generating valid and helpful code bases, with 77% of generated repositories rated as best by evaluators

## Summary

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In the rapidly evolving field of machine learning research, the lack of available code implementations often hinders researchers from reproducing results and building upon prior work. However, recent advancements in Large Language Models (LLMs) have shown promise in understanding scientific documents and generating high-quality code. Building on this progress, a new framework called PaperCoder has been introduced to address the challenge of automatically transforming machine learning papers into functional code repositories. PaperCoder operates through three key stages: planning, analysis, and generation. In the planning stage, the framework constructs a roadmap for implementation, designs system architecture with diagrams, identifies file dependencies, and generates configuration files for experimental workflows. The analysis stage focuses on interpreting implementation-specific details from the research paper, while the generation stage produces modular, dependency-aware code based on earlier stages' outputs. What sets PaperCoder apart is its reliance on multi-agent LLM technology to generate executable code repositories directly from research papers without requiring partial implementations or human inputs. By emulating the typical workflow of human developers and researchers, PaperCoder aims to provide accurate and faithful code implementations that can support further research efforts. To validate the effectiveness of PaperCoder, extensive evaluations were conducted using a subset of recent machine learning papers accepted at top-tier venues in 2024. The evaluations included automated model-based assessments and expert human evaluations based on original paper authors' feedback. Results showed that PaperCoder outperformed baselines in generating valid and helpful code bases, with 77% of generated repositories rated as best by evaluators. Furthermore, detailed analyses revealed that each component of PaperCoder - planning, analysis, and generation - contributed to performance gains. Notably, generated code bases were found to be executable with minor modifications in cases where errors occurred during execution. Overall, PaperCoder represents a significant advancement in automating the process of translating machine learning research papers into functional code repositories. Its success in producing high-quality implementations demonstrates its potential to streamline research reproducibility and facilitate knowledge dissemination within the machine learning community.