Ma ZhenYao, Yue Liang, Jian-Qiao Zhu · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28562 · open-access PDF
Muchos fenómenos científicos se entienden mejor como resultados de problemas de optimización subyacentes, sin embargo, el descubrimiento científico automatizado se ha centrado en gran parte en recuperar ecuaciones que describen patrones de entrada-salida observados. Introducimos OptScientist, un nuevo marco automatizado para descubrir leyes de optimización simbólica compacta directamente de los datos. OptScientist comprende (i) un modelo que deduce problemas de optimización simbólica aprendiendo primero a representar los datos como un problema de optimización y luego extrayendo su forma simbólica, y (ii) un sistema de agentes LLM que incorpora el modelo en un proceso de descubrimiento
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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