2025/02/22 by Patrick Tser Jern Kon, Jiachen Liu, Kon, Patrick Tser Jern +16 · 14 citations
Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2502.16069
openalex publication_date 2025/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 3.4× improvement in correctly answering experimental questions. Curie is open-sourced at https://github.com/Just-Curieous/Curie.