The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Abstract: We introduce The AI Scientist-v2, an end-to-end agentic system capable of autonomously conducting scientific research, including hypothesis generation, experimentation, analysis, and manuscript writing. We demonstrated its capability by having one of its three AI-generated papers successfully navigate peer review at the ICLR workshop "I Can't Believe It's Not Better: Challenges in Applied Deep Learning". While this highlights AI's potential in scientific discovery, challenges remain: verifying AI outputs is time-intensive and the current system struggles with generating genuinely novel, high-impact hypotheses and justifying design decisions with deep domain expertise. This study, conducted with IRB approval and organizer cooperation, underscores the urgent need for community norms and transparency regarding AI-generated scientific content to ensure responsible development and maintain the integrity of peer review in the face of the potential influx of such papers.

Bio: Yutaro Yamada is a research scientist at Sakana AI. He previously earned a PhD in Statistics and Data Science from Yale University, supported by the Masason Fellowship. His research covers language processing, computer vision, and machine learning, with a current focus on AI agents.