#ml-research-automation

Paper2Code is a tool powered by PaperCoder, a multi-agent Large Language Model (LLM) system, designed to automate the generation of executable code repositories directly from machine learning scientific papers. It employs a three-stage pipeline—planning, analysis, and code generation—each managed by specialized agents. This method has shown superior performance on Paper2Code and PaperBench benchmarks, producing faithful and high-quality implementations, supporting both OpenAI API and open-source models via vLLM.