SPARK: Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search

A recent publication on arXiv (2605.04057) presents SPARK, a technique designed to enhance neural architecture search (NAS) guided by large language models (LLMs). The researchers highlight a significant issue known as functional entanglement, where localized code changes by LLMs lead to unexpected performance variations elsewhere. SPARK tackles this challenge by deliberately choosing which functional aspect to alter and tailoring modifications based on that aspect, minimizing unintended consequences and facilitating more precise architectural changes. This method leverages pertinent architectural and coding insights from LLMs while reducing the expenses associated with costly evaluations.