Resource-Efficient Iterative LLM-Based NAS with Feedback Memory

ArXi:2603.12091v1 Announce Type: cross Neural Architecture Search (NAS) automates network design, but conventional methods demand substantial computational resources. We propose a closed-loop pipeline leveraging large language models (LLMs) to iteratively generate, evaluate, and refine convolutional neural network architectures for image classification on a single consumer-grade GPU without LLM fine-tuning.