DeepScientist: Advancing Frontier-Pushing Scientific Findings
  Progressively
Authors: name: Yixuan Weng, name: Minjun Zhu, name: Qiujie Xie, name: Qiyao Sun, name: Zhen Lin, name: Sifan Liu, name: Yue Zhang
Organization: Text Intelligence Lab of Westlake University

While previous AI Scientist systems can generate novel findings, they often
lack the focus to produce scientifically valuable contributions that address
pressing human-defined challenges. We introduce DeepScientist, a system
designed to overcome this by conducting goal-oriented, fully autonomous
scientific discovery over month-long timelines. It formalizes discovery as a
Bayesian Optimization problem, operationalized through a hierarchical
evaluation process consisting of "hypothesize, verify, and analyze". Leveraging
a cumulative Findings Memory, this loop intelligently balances the exploration
of novel hypotheses with exploitation, selectively promoting the most promising
findings to higher-fidelity levels of validation. Consuming over 20,000 GPU
hours, the system generated about 5,000 unique scientific ideas and
experimentally validated approximately 1100 of them, ultimately surpassing
human-designed state-of-the-art (SOTA) methods on three frontier AI tasks by
183.7\%, 1.9\%, and 7.9\%. This work provides the first large-scale evidence of
an AI achieving discoveries that progressively surpass human SOTA on scientific
tasks, producing valuable findings that genuinely push the frontier of
scientific discovery. To facilitate further research into this process, we will
open-source all experimental logs and system code at
https://github.com/ResearAI/DeepScientist/.