Designing neural architectures for time-series forecasting and anomaly detection remains a resource-intensive task that often requires substantial domain expertise. Traditional Automated Machine Learning (AutoML) systems typically rely on static, predefined search spaces, limiting their ability to adapt to diverse data characteristics. We present GenAutoML, an agentic framework that leverages Large Language Models (LLMs) as neural architects to bridge natural-language requirements and executable PyTorch implementations. The framework incorporates a Sandboxed Reflection Loop for autonomous code refinement and a Signature-Aware Runtime that enforces architectural consistency and execution safety. To improve robustness under non-stationary conditions, we further introduce a Dynamic Reversible Instance Normalization (Dyn-RevIN) wrapper. Experiments on the ETTh1, ETTm1, and Weather benchmarks demonstrate that GenAutoML can dynamically generate task-specific neural architectures tailored to dataset characteristics. Among the generated models, WaveInterferenceNet achieves inference latency below 0.01 ms per sample while maintaining competitive predictive performance. By emphasizing computational efficiency, architectural adaptability, and stable optimization behavior, GenAutoML enables the creation of ultra-lightweight neural networks suitable for resource-constrained and latency-sensitive Edge AI deployments.

翻译：设计用于时间序列预测和异常检测的神经架构仍是一项资源密集型任务，通常需要大量领域专业知识。传统自动化机器学习系统通常依赖静态预定义的搜索空间，限制了其适应多样数据特征的能力。我们提出GenAutoML——一种将大型语言模型作为神经架构师的智能体框架，能够桥接自然语言需求与可执行的PyTorch实现。该框架包含用于自主代码精化的沙盒反射循环，以及强制架构一致性与执行安全性的签名感知运行时。为提升非平稳条件下的鲁棒性，我们进一步引入动态可逆实例归一化封装器。在ETTh1、ETTm1和Weather基准上的实验表明，GenAutoML能够根据数据集特征动态生成任务特定的神经架构。在生成的模型中，WaveInterferenceNet在保持竞争性预测性能的同时，实现了每样本低于0.01毫秒的推理延迟。通过强调计算效率、架构适应性和稳定优化行为，GenAutoML能够创建适用于资源受限和延迟敏感的边缘AI部署的超轻量神经网络。