DeepScientist：逐步推进具有前沿突破性的科学发现
DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively
AI总结：
针对现有AI科学家系统缺乏针对性、难产出高价值科学成果的问题，提出DeepScientist系统，通过贝叶斯优化和分层评估实现自主科学发现，在三项前沿AI任务上超越人类SOTA，并将开源相关资源。
摘要：
尽管此前的AI Scientist系统能够生成新颖的发现，但它们往往缺乏针对性，无法产出能应对人类迫切面临的挑战、具备科学价值的成果。我们推出DeepScientist，该系统旨在通过开展为期数月的目标导向型完全自主科学发现来解决这一问题。它将发现过程形式化为一个贝叶斯优化问题，通过包含“提出假设、验证、分析”的分层评估流程落地实现。依托累积式发现记忆（Findings Memory），这一循环能够智能平衡新颖假设的探索与已有成果的利用，有选择性地将最具前景的发现推进到保真度更高的验证层级。该系统消耗超过20000 GPU小时，生成了约5000个独特的科学构想，并对其中约1100个进行了实验验证，最终在三项前沿AI任务上超越了人类设计的当前最优（SOTA）方法，提升幅度分别为183.7%、1.9%和7.9%。这项研究首次提供了大规模证据，证明AI能够在科学任务上取得逐步超越人类SOTA的发现，产出真正推动科学发现前沿的有价值成果。为推动对这一过程的进一步研究，我们将在https://github.com/ResearAI/DeepScientist/ 开源所有实验日志和系统代码。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
