This research introduces a novel framework where Large Language Models directly modify and optimize neural network code, revolutionizing Neural Architecture Search.

• Eliminates the need for extensive domain expertise and trial-and-error in model design
• LLMs intelligently modify source code to improve object detection performance
• Creates an autonomous optimization loop that continually enhances model architectures
• Demonstrates superior results compared to traditional evolutionary algorithms with fixed rules

For engineering teams, this approach significantly reduces development time while improving model performance, potentially transforming how AI systems are designed and optimized.

Original Paper: LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection