Automated Design Optimization via Strategic Search with Large Language Models

• URL: http://arxiv.org/abs/2511.22651v1
• Date: Thu, 27 Nov 2025 17:42:05 GMT
• Title: Automated Design Optimization via Strategic Search with Large Language Models
• Authors: Anthony Carreon, Vansh Sharma, Venkat Raman,
• Abstract summary: AUTO is a framework that treats design optimization as a gradient-free search problem guided by strategic LLM reasoning.<n>It completes optimizations in approximately 8 hours at an estimated cost of up to $159 per run, compared to an estimated cost of up to $480 with median-wage software developers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Traditional optimization methods excel in well-defined search spaces but struggle with design problems where transformations and design parameters are difficult to define. Large language models (LLMs) offer a promising alternative by dynamically interpreting design spaces and leveraging encoded domain knowledge. To this end, we introduce AUTO, an LLM agent framework that treats design optimization as a gradient-free search problem guided by strategic LLM reasoning. The framework employs two collaborative agents: a Strategist that selects between exploration and exploitation strategies, and an Implementor that executes detailed designs. Applied to GPU code optimization -- a domain critical to fields from machine learning to scientific computing -- AUTO generates solutions competitive with expert implementations for chemical kinetics integration and dense matrix multiplication. The framework achieves 50-70% search efficiency relative to Bayesian optimization methodologies. It completes optimizations in approximately 8 hours at an estimated cost of up to \$159 per run, compared to an estimated cost of up to \$480 with median-wage software developers. These findings open the door to automating design optimization in ill-defined search spaces with limited prior information.

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