title: Curie: Toward Rigorous and Automated Scientific Experimentation with AI
    Agents
summary: Scientific experimentation, a cornerstone of human progress, demands rigor in
  reliability, methodical control, and interpretability to yield meaningful
  results. Despite the growing capabilities of large language models (LLMs) in
  automating different aspects of the scientific process, automating rigorous
  experimentation remains a significant challenge. To address this gap, we
  propose Curie, an AI agent framework designed to embed rigor into the
  experimentation process through three key components: an intra-agent rigor
  module to enhance reliability, an inter-agent rigor module to maintain
  methodical control, and an experiment knowledge module to enhance
  interpretability. To evaluate Curie, we design a novel experimental benchmark
  composed of 46 questions across four computer science domains, derived from
  influential research papers, and widely adopted open-source projects. Compared
  to the strongest baseline tested, we achieve a 3.4times improvement in
  correctly answering experimental questions.Curie is open-sourced at
  https://github.com/Just-Curieous/Curie.
paper_authors._id: 67bf51f8653c05485b571e71
paper_authors.name: Patrick Tser Jern Kon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-02T20:18:25.807Z
paper_authors._id: 64b7111e17681d64b19cf95e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b7111e17681d64b19cf95e/VHPfCUl1nBS3OMMVi96CR.jpeg
paper_authors.fullname: Patrick Kon
paper_authors.type: user
paper_authors.user: patkon
paper_authors._id: 67bf51f8653c05485b571e72
paper_authors.name: Jiachen Liu
paper_authors._id: 67bf51f8653c05485b571e73
paper_authors.name: Qiuyi Ding
paper_authors._id: 67bf51f8653c05485b571e74
paper_authors.name: Yiming Qiu
paper_authors._id: 67bf51f8653c05485b571e75
paper_authors.name: Zhenning Yang
paper_authors._id: 67bf51f8653c05485b571e76
paper_authors.name: Yibo Huang
paper_authors._id: 67bf51f8653c05485b571e77
paper_authors.name: Jayanth Srinivasa
paper_authors._id: 67bf51f8653c05485b571e78
paper_authors.name: Myungjin Lee
paper_authors._id: 67bf51f8653c05485b571e79
paper_authors.name: Mosharaf Chowdhury
paper_authors._id: 67bf51f8653c05485b571e7a
paper_authors.name: Ang Chen
paper_title: Curie: Toward Rigorous and Automated Scientific Experimentation with AI
    Agents
paper_summary: Scientific experimentation, a cornerstone of human progress, demands rigor in
  reliability, methodical control, and interpretability to yield meaningful
  results. Despite the growing capabilities of large language models (LLMs) in
  automating different aspects of the scientific process, automating rigorous
  experimentation remains a significant challenge. To address this gap, we
  propose Curie, an AI agent framework designed to embed rigor into the
  experimentation process through three key components: an intra-agent rigor
  module to enhance reliability, an inter-agent rigor module to maintain
  methodical control, and an experiment knowledge module to enhance
  interpretability. To evaluate Curie, we design a novel experimental benchmark
  composed of 46 questions across four computer science domains, derived from
  influential research papers, and widely adopted open-source projects. Compared
  to the strongest baseline tested, we achieve a 3.4times improvement in
  correctly answering experimental questions.Curie is open-sourced at
  https://github.com/Just-Curieous/Curie.
paper_ai_summary: Curie is an AI agent framework that improves rigor in scientific experimentation through reliability, control, and interpretability, demonstrating a significant improvement over baselines in answering experimental questions.
paper_ai_keywords: large language models (LLMs) AI agent framework intra-agent rigor module inter-agent rigor module experiment knowledge module experimental benchmark computer science domains open-source projects