这篇研究论文介绍了 DeepScientist，一个旨在于实现目标导向、完全自主科学发现的 AI 科学家系统。该系统将科学探索建模为一个 贝叶斯优化问题，通过“假设、验证和分析”的迭代循环运行，并利用 累积发现记忆 来平衡对新假设的探索和对有前景方向的利用。实验结果表明，DeepScientist 在三个前沿 AI 任务上，通过 生成超过 5,000 个独特想法 和 验证约 1,100 个，最终以显著优势超越了人类设计的现有技术水平（SOTA）。这提供了第一个大规模证据，证明 AI 能够 持续推动科学前沿，并以超过人类研究人员的速度取得突破性发现。然而，分析指出该系统的主要瓶颈在于其 低成功率（1-3%），强调了高效验证和过滤在自动化科学中的关键作用。References: Weng Y, Zhu M, Xie Q, et al. DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively[J]. arXiv preprint arXiv:2509.26603, 2025.
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