Paper2Code: Automating Code Generation from Scientific Papers in Machine
  Learning
Authors: name: Minju Seo, name: Jinheon Baek, name: Seongyun Lee, name: Sung Ju Hwang

Despite the rapid growth of machine learning research, corresponding code
implementations are often unavailable, making it slow and labor-intensive for
researchers to reproduce results and build upon prior work. In the meantime,
recent Large Language Models (LLMs) excel at understanding scientific documents
and generating high-quality code. Inspired by this, we introduce PaperCoder, a
multi-agent LLM framework that transforms machine learning papers into
functional code repositories. PaperCoder operates in three stages: planning,
where it constructs a high-level roadmap, designs the system architecture with
diagrams, identifies file dependencies, and generates configuration files;
analysis, which focuses on interpreting implementation-specific details; and
generation, where modular, dependency-aware code is produced. Moreover, each
phase is instantiated through a set of specialized agents designed to
collaborate effectively across the pipeline. We then evaluate PaperCoder on
generating code implementations from machine learning papers based on both
model-based and human evaluations, specifically from the original paper
authors, with author-released repositories as ground truth if available. Our
results demonstrate the effectiveness of PaperCoder in creating high-quality,
faithful implementations. Furthermore, it consistently shows strengths in the
recently released PaperBench benchmark, surpassing strong baselines by
substantial margins.