title: Paper2Code: Automating Code Generation from Scientific Papers in Machine
    Learning
summary: 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.
paper_authors._id: 680aee7bcf67477f2c00ca53
paper_authors.name: Minju Seo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T08:34:41.304Z
paper_authors._id: 64f7bf0c7565a69eb693ad1f
paper_authors.avatarUrl: /avatars/aba6910aa39a3437a7f0df3f5cd49e6d.svg
paper_authors.fullname: minju
paper_authors.type: user
paper_authors.user: iaminju
paper_authors._id: 680aee7bcf67477f2c00ca54
paper_authors.name: Jinheon Baek
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T08:34:38.982Z
paper_authors._id: 63036b6c5c70c21d0ea79d48
paper_authors.avatarUrl: /avatars/a7eb03f5cbd4eaa09fe807bbed8bc0f7.svg
paper_authors.fullname: Jinheon Baek
paper_authors.type: user
paper_authors.user: jinheon
paper_authors._id: 680aee7bcf67477f2c00ca55
paper_authors.name: Seongyun Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-26T08:53:43.030Z
paper_authors._id: 6550c4f27bbfce1878f5f280
paper_authors.avatarUrl: /avatars/0ecedbcd8a55b2c4abd1da9e741a6652.svg
paper_authors.fullname: seongyun_lee
paper_authors.type: user
paper_authors.user: Seongyun
paper_authors._id: 680aee7bcf67477f2c00ca56
paper_authors.name: Sung Ju Hwang
paper_title: Paper2Code: Automating Code Generation from Scientific Papers in Machine
    Learning
paper_summary: 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.
paper_ai_summary: PaperCoder is a multi-agent LLM framework that converts machine learning papers into functional code repositories through planning, analysis, and generation stages.
paper_ai_keywords: Large Language Models LLM PaperCoder multi-agent framework system architecture configuration files modular dependency-aware PaperBench benchmark