Large Language Model-Guided Optimization

This field studies how language models can use learned patterns and natural-language understanding to help search for better algorithms, programs, designs, prompts, and agent workflows. Common approaches combine model-generated proposals with evolutionary search, Bayesian optimization, reinforcement learning, or classical solvers, then use execution, simulator, or benchmark feedback to select and repair candidates. Applications range from combinatorial and scientific optimization to GPU kernels, compiler code, engineering design, and tuning language-model systems. A central open tension is that language models can suggest useful structures but may be unreliable, costly, or prone to overfitting, so robust evaluation, constraint checks, and comparisons with numerical methods remain essential.