DeepScientist: Advancing Frontier-Pushing Scientific Findings
  Progressively
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/.
68dcd65b4159d1f2418f9c68
Yixuan Weng
68dcd65b4159d1f2418f9c69
Minjun Zhu
68dcd65b4159d1f2418f9c6a
Qiujie Xie
68dcd65b4159d1f2418f9c6b
Qiyao Sun
68dcd65b4159d1f2418f9c6c
Zhen Lin
68dcd65b4159d1f2418f9c6d
Sifan Liu
68dcd65b4159d1f2418f9c6e
Yue Zhang
DeepScientist: Advancing Frontier-Pushing Scientific Findings
  Progressively
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/.
DeepScientist autonomously conducts scientific discovery through Bayesian Optimization, surpassing human state-of-the-art methods on multiple AI tasks.
Bayesian Optimization hierarchical evaluation Findings Memory exploration exploitation scientific discovery state-of-the-art AI tasks