The paper, titled "Can Language Models Discover Scaling Laws?" and published on January 22, 2026, represents a collaborative effort by researchers from Peking University, Stanford University, Wizard Quant, and Tsinghua University. he authors address the inefficiency of manual scaling law discovery by introducing SLDAgent, an evolution-based system designed to automate the search for predictive symbolic formulas. They prove that this agent consistently discovers laws with superior extrapolation accuracy compared to human experts and existing baselines across the newly curated SLDBench, a testbed aggregating over 5,000 training experiments. Additionally, the study demonstrates the practical superiority of these autonomously discovered laws in critical tasks such as analytical hyperparameter optimization and pre-trained model selection. Source: https://arxiv.org/pdf/2507.21184