Structuring Open-Ended NAS: Semi-Automated Design Knowledge Structuring with LLMs for Efficient Neural Architecture Search

2026-05-18

AI Summary

This work addresses the limitations of existing neural architecture search (NAS) methods, which are either confined to narrow predefined search spaces or suffer from inefficiency and bias when leveraging large language models (LLMs) for open-ended exploration. To overcome these challenges, the authors propose a semi-automated NAS framework that constructs a prior-informed, open search space by structurally modeling architectural knowledge extracted from scientific literature. The framework integrates the FairNAD algorithm with multiple fairness-aware mutation mechanisms—including fair sampling, Pareto-aware selection, and LLM-driven iterative refinement—to enable efficient, diverse, and high-quality architecture discovery. Empirical evaluations demonstrate consistent improvements over state-of-the-art methods, achieving accuracy gains of 0.84%, 2.17%, and 2.35% on CIFAR-10, CIFAR-100, and ImageNet16-120, respectively.

Abstract

Current neural architecture search (NAS) methods are often limited by their predefined, restrictive search spaces. While recent large language model (LLM)-assisted NAS methods enable open-ended search spaces, they often suffer from inefficient exploration due to biased or low-quality design ideas. To address these issues, we propose to semi-automatically structure model design knowledge to guide the search process. Our approach first defines a high-level structural template of architectural attributes. An LLM then populates this template by analyzing papers, creating a rich and diverse search space that embodies this structured design knowledge. To efficiently explore this vast space, we introduce FairNAD, using a multi-type mutation that enables broad exploration through mutation with fair idea sampling, Pareto-aware mutation, LLM-driven iterative mutation, and a fine-grained feedback loop. We demonstrate the effectiveness of FairNAD in discovering high-performing architectures that yield 0.84, 2.17, and 2.35 points improvement on CIFAR-10, CIFAR-100, and ImageNet16-120, respectively, compared to current state-of-the-art methods.

Problem

• Neural Architecture Search
• Open-ended Search Space
• Design Knowledge Structuring
• Search Efficiency
• Large Language Models

Innovation

• Neural Architecture Search
• Large Language Models
• Knowledge Structuring
• FairNAD
• Open-ended Search Space