This December 2025 paper introduces SGI-Bench, a comprehensive framework designed to evaluate the capabilities of autonomous scientific agents across diverse research workflows. The benchmark spans multiple disciplines, including chemistry, materials science, and astronomy, by challenging models with tasks like experimental design, numerical modeling, and data interpretation. Through a series of structured modules, it explores how artificial intelligence can manage dry experiments involving simulations and wet experiments focused on physical laboratory processes. Technical examples demonstrate the rigorous use of mathematical derivations and multi-modal analysis to solve complex problems, such as calculating gravitational wave parameters or predicting molecular properties. Ultimately, the text highlights a shift toward agentic science, where AI assistants assist in accelerating discovery through systematic reasoning and automated tool use. Source: December 2025 Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows Shanghai Artificial Intelligence Laboratory Wanghan Xu, Yuhao Zhou, Yifan Zhou, Qinglong Cao, Shuo Li, Jia Bu, et al. https://arxiv.org/pdf/2512.16969