title: AIDE: AI-Driven Exploration in the Space of Code
summary: Machine learning, the foundation of modern artificial intelligence, has
  driven innovations that have fundamentally transformed the world. Yet, behind
  advancements lies a complex and often tedious process requiring labor and
  compute intensive iteration and experimentation. Engineers and scientists
  developing machine learning models spend much of their time on trial-and-error
  tasks instead of conceptualizing innovative solutions or research hypotheses.
  To address this challenge, we introduce AI-Driven Exploration (AIDE), a machine
  learning engineering agent powered by large language models (LLMs). AIDE frames
  machine learning engineering as a code optimization problem, and formulates
  trial-and-error as a tree search in the space of potential solutions. By
  strategically reusing and refining promising solutions, AIDE effectively trades
  computational resources for enhanced performance, achieving state-of-the-art
  results on multiple machine learning engineering benchmarks, including our
  Kaggle evaluations, OpenAI MLE-Bench and METRs RE-Bench.
paper_authors._id: 67b6e0829b29983879ad2312
paper_authors.name: Zhengyao Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:38:24.557Z
paper_authors._id: 630384837b50dd9d0a3328dc
paper_authors.avatarUrl: /avatars/17097a93ef403592bc07c0ff6712faf3.svg
paper_authors.fullname: Zhengyao Jiang
paper_authors.type: user
paper_authors.user: ZhengyaoJiang
paper_authors._id: 67b6e0829b29983879ad2313
paper_authors.name: Dominik Schmidt
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:38:52.544Z
paper_authors._id: 63042a72eedc089484c89aee
paper_authors.avatarUrl: /avatars/cbbe3000e6fa783c395b23c6b67da5ab.svg
paper_authors.fullname: Dominik Schmidt
paper_authors.type: user
paper_authors.user: dominikschmidt
paper_authors._id: 67b6e0829b29983879ad2314
paper_authors.name: Dhruv Srikanth
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-20T17:38:58.578Z
paper_authors._id: 62f3203506b6e6f54b4720f5
paper_authors.avatarUrl: /avatars/64a395d3391e1025dbdd945c33ceee94.svg
paper_authors.fullname: Dhruv Srikanth
paper_authors.type: user
paper_authors.user: dSrikanth
paper_authors._id: 67b6e0829b29983879ad2315
paper_authors.name: Dixing Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-20T15:52:52.450Z
paper_authors._id: 65f7927e7bc58032aa5bda58
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65f7927e7bc58032aa5bda58/JxUSj-J7YBwtgj6rqQtjn.jpeg
paper_authors.fullname: Dex Dixing Xu
paper_authors.type: user
paper_authors.user: dexhunter
paper_authors._id: 67b6e0829b29983879ad2316
paper_authors.name: Ian Kaplan
paper_authors._id: 67b6e0829b29983879ad2317
paper_authors.name: Deniss Jacenko
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-25T08:21:46.526Z
paper_authors._id: 63442e49b7485028097f005d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63442e49b7485028097f005d/bu8BhKCzp08GvrgMQ6c-T.jpeg
paper_authors.fullname: Deniss Jacenko
paper_authors.type: user
paper_authors.user: jacenko
paper_authors._id: 67b6e0829b29983879ad2318
paper_authors.name: Yuxiang Wu
paper_title: AIDE: AI-Driven Exploration in the Space of Code
paper_summary: Machine learning, the foundation of modern artificial intelligence, has
  driven innovations that have fundamentally transformed the world. Yet, behind
  advancements lies a complex and often tedious process requiring labor and
  compute intensive iteration and experimentation. Engineers and scientists
  developing machine learning models spend much of their time on trial-and-error
  tasks instead of conceptualizing innovative solutions or research hypotheses.
  To address this challenge, we introduce AI-Driven Exploration (AIDE), a machine
  learning engineering agent powered by large language models (LLMs). AIDE frames
  machine learning engineering as a code optimization problem, and formulates
  trial-and-error as a tree search in the space of potential solutions. By
  strategically reusing and refining promising solutions, AIDE effectively trades
  computational resources for enhanced performance, achieving state-of-the-art
  results on multiple machine learning engineering benchmarks, including our
  Kaggle evaluations, OpenAI MLE-Bench and METRs RE-Bench.
paper_ai_summary: AI-Driven Exploration, utilizing large language models, optimizes machine learning engineering tasks through code optimization and strategic solution reuse, achieving top results on various benchmarks.
paper_ai_keywords: large language models machine learning engineering agent code optimization tree search machine learning engineering benchmarks Kaggle evaluations OpenAI MLE-Bench METRs RE-Bench