LATIDIA · Investigación

Evaluación del aprendizaje por indagación en agentes basados en LLM: hipótesis, razonamiento y exploración

arXiv:2610.21604v1 Tipo de anuncio: nuevo Resumen: La investigación científica implica ciclos iterativos de exploración, generación de hipótesis y razonamiento sobre las observaciones para descubrir relaciones en sistemas complejos. Langu grande

La noticia

arXiv:2610.21604v1 Announce Type: new Abstract: Scientific inquiry involves iterative cycles of exploration, hypothesis generation, and reasoning over observations to uncover relationships in complex systems. Large language models (LLMs) have demonstrated strong capabilities in several components of this process, including inductive reasoning, hypothesis generation, and data analysis. However, prior work typically evaluates these abilities in isolation, leaving open how models integrate them across a full inquiry cycle. In this work, we introduce a framework for evaluating LLMs' inquiry capabilities in a unified, interactive setting. We extend the Countdown task to a mathematical environment where an agent probes the system by manipulating variables, collecting observations, and inferring relationships between them. This setup