Ma ZhenYao, Yue Liang, Jian-Qiao Zhu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28562 · open-access PDF
许多科学现象被最好地理解为潜在优化问题的结果,但自动化科学发现主要集中在恢复描述观察到的输入输出模式的方程式上。我们介绍了OptScientist,一个新的自动化框架,用于直接从数据中发现紧凑的象征优化法则。OptScientist包括(i)一个模型,通过先学习将数据作为优化问题表示,然后提取其象征形式,并(ii)一个LLM代理系统,将模型嵌入到一个发现过程中。
Machine translation (Meta M2M100); the English original follows.
Many scientific phenomena are best understood as outcomes of underlying optimization problems, yet automated scientific discovery has largely focused on recovering equations that describe observed input-output patterns. We introduce OptScientist, a novel automated framework for discovering compact symbolic optimization laws directly from data. OptScientist comprises (i) a model that infers symbolic optimization problems by first learning to represent data as an optimization problem and then extracting its symbolic form, and (ii) an LLM agentic system embedding the model in a discovery process
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