Abstract
Automated neural network architecture design remains a significant challenge in computer vision. Task diversity and computational constraints require both effective architectures and efficient search methods. Large Language Models (LLMs) present a promising alternative to computationally intensive Neural Architecture Search (NAS), but their application to architecture generation in computer vision has not been systematically studied, particularly regarding prompt engineering and validation strategies. Building on the task-agnostic NNGPT/LEMUR framework, this work introduces and validates two key contributions for computer vision. First, we present Few-Shot Architecture Prompting (FSAP), the first systematic study of the number of supporting examples (n = 1, 2, 3, 4, 5, 6) for LLM-based architecture generation. We find that using n = 3 examples best balances architectural diversity and context focus for vision tasks. Second, we introduce Whitespace-Normalized Hash Validation, a lightweight deduplication method (less than 1 ms) that provides a 100x speedup over AST parsing and prevents redundant training of duplicate computer vision architectures. In large-scale experiments across seven computer vision benchmarks (MNIST, CIFAR-10, CIFAR-100, CelebA, ImageNette, SVHN, Places365), we generated 1,900 unique architectures. We also introduce a dataset-balanced evaluation methodology to address the challenge of comparing architectures across heterogeneous vision tasks. These contributions provide actionable guidelines for LLM-based architecture search in computer vision and establish rigorous evaluation practices, making automated design more accessible to researchers with limited computational resources.
Abstract (translated)
自动化神经网络架构设计在计算机视觉领域仍是一项重要挑战。任务多样性和计算约束要求既有效又高效的搜索方法。大型语言模型（LLMs）为计算密集型的神经架构搜索（NAS）提供了一种有希望的替代方案，但在计算机视觉架构生成的应用方面尚未系统地研究过，尤其是在提示工程和验证策略方面。基于任务无关的NNGPT/LEMUR框架，这项工作引入并验证了两个关键贡献，以促进计算机视觉领域的进步。 首先，我们提出少样本架构提示（Few-Shot Architecture Prompting, FSAP），这是第一个关于LLM基架构生成所需的辅助示例数量（n=1,2,3,4,5,6）的系统性研究。研究表明使用n=3个例子可以在保持视觉任务多样性的同时最好地聚焦上下文。 其次，我们引入了空白标准化哈希验证方法，这是一种轻量级去重技术（少于1毫秒），相比抽象语法树解析提供了100倍的速度提升，并且可以防止重复训练相同的计算机视觉架构。在七个大规模的计算机视觉基准测试集（MNIST、CIFAR-10、CIFAR-100、CelebA、ImageNette、SVHN和Places365）上，我们生成了超过1,900种独特架构。 此外，为了应对不同异构视觉任务之间比较架构的挑战，我们还提出了一种数据集平衡评估方法。这些贡献为基于LLM的计算机视觉领域架构搜索提供了切实可行的操作指南，并确立了严格的评价实践标准，使自动设计对于计算资源有限的研究人员来说更加可及。
URL
https://arxiv.org/abs/2512.24120
