4 papers
WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
Zhongzheng Li, Qingsong Ran, Shikun Feng +5
Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suffer from poor sample efficienc…
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…
MCCE: A Framework for Multi-LLM Collaborative Search in Discrete Spaces with Similarity-Filtered Preference Learning
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…
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…