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This research demonstrates the viability of using advanced LLMs like GPT-5 as autonomous agents for Neural Architecture Search in cross-lingual OCR tasks. By eliminating manual hyperparameter tuning, the framework achieves high accuracy across Arabic, Persian, and English datasets while maintaining low inference latency. The study highlights a significant shift toward fully automated machine learning pipelines where language models directly design and refine computational structures without human intervention.
LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4
arXiv:2607.15509v3 Announce Type: replace-cross Abstract: We present a fully automated closed-loop AutoML framework that uses GPT-5, GPT-4o, and Claude Sonnet 4 as autonomous neural architecture designers for cross-lingual handwritten optical character recognition. Each large language model independently generates, trains, evaluates, and iteratively refines neural network architectures using performance feedback from previous trials. The framework is evaluated on Arabic, Persian, and English handwriting datasets through 270 independent experiments. It consistently discovers accurate and computationally efficient models without manual architecture design, domain-specific preprocessing, or hyperparameter tuning. The generated models achieve mean test accuracies above 93 percent, a best accuracy of 98.1 percent, and inference latency between 41 and 44 milliseconds. The results demonstrate that large language models can function as effective AutoML agents for neural architecture search, enabling scalable, script-adaptive, and reproducible handwriting recognition across languages.
Abstract:We present a fully automated closed-loop AutoML framework that uses GPT-5, GPT-4o, and Claude Sonnet 4 as autonomous neural architecture designers for cross-lingual handwritten optical character recognition. Each large language model independently generates, trains, evaluates, and iteratively refines neural network architectures using performance feedback from previous trials. The framework is evaluated on Arabic, Persian, and English handwriting datasets through 270 independent experiments. It consistently discovers accurate and computationally efficient models without manual architecture design, domain-specific preprocessing, or hyperparameter tuning. The generated models achieve mean test accuracies above 93 percent, a best accuracy of 98.1 percent, and inference latency between 41 and 44 milliseconds. The results demonstrate that large language models can function as effective AutoML agents for neural architecture search, enabling scalable, script-adaptive, and reproducible handwriting recognition across languages.
7 pages, 10 figures, and 2 tables. Published in the 2025 15th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2607.15509 [cs.CV]
(or arXiv:2607.15509v3 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.15509 Focus to learn more arXiv-issued DOI via DataCite
2025 15th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE, 2025, Article No. 11273810
https://doi.org/10.1109/ICCKE68588.2025.11273810 Focus to learn more DOI(s) linking to related resources
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