Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search

This paper introduces Structured Progressive Knowledge Activation (SPARK) to address the challenge of integrating architectural knowledge in LLM-driven Neural Architecture Search (NAS). SPARK mitigates "functional entanglement" by enabling factor-conditioned editing, leading to more targeted and reliable architecture modifications.