Curie: Toward Rigorous and Automated Scientific Experimentation with AI
  Agents
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.
67bf51f8653c05485b571e71
Patrick Tser Jern Kon
claimed_verified
2025-03-02T20:18:25.807Z
64b7111e17681d64b19cf95e
https://cdn-avatars.huggingface.co/v1/production/uploads/64b7111e17681d64b19cf95e/VHPfCUl1nBS3OMMVi96CR.jpeg
Patrick Kon
user
patkon
67bf51f8653c05485b571e72
Jiachen Liu
67bf51f8653c05485b571e73
Qiuyi Ding
67bf51f8653c05485b571e74
Yiming Qiu
67bf51f8653c05485b571e75
Zhenning Yang
67bf51f8653c05485b571e76
Yibo Huang
67bf51f8653c05485b571e77
Jayanth Srinivasa
67bf51f8653c05485b571e78
Myungjin Lee
67bf51f8653c05485b571e79
Mosharaf Chowdhury
67bf51f8653c05485b571e7a
Ang Chen
Curie: Toward Rigorous and Automated Scientific Experimentation with AI
  Agents
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.
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.
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