GenAutoML: An Agentic Framework for Dynamic Architecture Generation and Optimization in Time-Series Analysis
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 s...
| Authors | Oleeviya Babu Poikarayil, Cédric Schockaert, Abdulrahman Nahhas et al. |
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| Primary category | cs.LG |
| Published | 4 June 2026 |
| Last updated at source | 11 June 2026 |
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