4 papers
OPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining
Zhongzheng Li, Tiancan Feng, Wenhao Li +5
Designing optimizers for modern deep learning remains a challenging scientific problem, requiring the joint consideration of optimization geometry, state dynamics, numerical stabil…
ExLLM: Experience-Enhanced LLM Optimization for Molecular Design and Beyond
Nian Ran, Yue Wang, Xiaoyuan Zhang +4
Molecular design involves an enormous and irregular search space, where traditional optimizers such as Bayesian optimization, genetic algorithms, and generative models struggle to…
MCCE: A Framework for Multi-LLM Collaborative Co-Evolution
Nian Ran, Zhongzheng Li, Yue Wang +5
Multi-objective discrete optimization problems, such as molecular design, pose significant challenges due to their vast and unstructured combinatorial spaces. Traditional evolution…
HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting
Nian Ran, Peng Xiao, Yue Wang +4
The application of large deep learning models in weather forecasting has led to significant advancements in the field, including higher-resolution forecasting and extended predicti…