going-doer/Paper2Code
Paper2Code
Paper2Code 是一个 AI 研究自动化套件和大语言模型代码生成流水线，旨在将机器学习研究论文转换为可执行的代码仓库。它作为一种将科学文献和理论描述自动翻译为功能性机器学习实现的工具。
该系统采用多阶段生成流水线，利用文档到计划的分解和自动化仓库脚手架来生成完整的项目结构。它集成了一个自动化代码评估框架，该框架使用迭代的批评与改进循环以及基于参考的黄金评估，来根据验证过的仓库对生成的代码进行正确性评分。
该项目涵盖了多个功能领域，包括科学论文数字化、自动化代码合成和机器学习代码验证。
Features
- Program Synthesis Models - Utilizes program synthesis models to translate mathematical descriptions and architectural diagrams into executable code.
- Code Validation Pipelines - Implements code validation pipelines to evaluate the correctness of generated ML implementations via gold-standard comparison.
- Generative Code Models - Leverages generative code models to produce complete repositories based on high-level technical specifications from papers.
- Paper-to-Code Frameworks - Provides a framework for automating the translation of scientific paper descriptions into functional ML implementation and analysis.
- Automated Code Refinement Loops - Provides automated code refinement loops where an LLM critiques and corrects generated code to improve accuracy.
- Paper Digitization Tools - Converts complex theoretical descriptions and equations from academic papers into executable software components.
- Paper-to-Code Implementations - Automates the full pipeline of translating machine learning research papers into functional code implementations.
- Implementation Automation Tools - Functions as an AI research automation suite for planning and generating software directly from scientific literature.
- Code Generation Pipelines - Implements an end-to-end code generation pipeline that transforms research papers into deployable software artifacts.
- Research Generation Pipelines - Employs a multi-stage generation pipeline that sequences planning and analysis phases before triggering code synthesis.
- Feature Decomposition Plans - Decomposes complex research papers into structured feature decomposition plans to guide modular code generation.
- Code Generation Evaluators - Provides a code generation evaluator that scores implementation correctness against reference gold repositories.
- Gold Standard Evaluators - Implements reference-based gold evaluation to score generated code against verified gold repositories.
- Machine Learning Implementations - Generates functional machine learning implementations to reproduce experiments described in scientific literature.
- Project Scaffolders - Implements automated project scaffolders to generate directory hierarchies and dependency files from analyzed research papers.
