用大模型自动设计手写识别模型，跨语言准确率超93%。
LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4
- 大模型自主生成、训练并优化神经网络架构
- 跨语言测试平均准确率93%以上，最高达98.1%
- 无需人工调参，适合多语言手写识别场景
我们提出一个完全自动化的闭环AutoML框架，利用GPT-5、GPT-4o和Claude Sonnet 4作为自主神经网络架构设计师，实现跨语言手写文字识别。每个大模型独立完成架构生成、训练、评估，并根据历史表现反馈迭代优化。在阿拉伯语、波斯语和英语手写数据集上进行270次独立实验，结果表明该框架无需人工设计架构、领域特定预处理或超参数调优，即可持续发现高精度且计算高效的模型。生成模型的平均测试准确率超过93%，最高达98.1%，推理延迟保持在41至44毫秒之间。实验验证了大语言模型可作为有效的AutoML代理，实现可扩展、脚本自适应且可复现的跨语言手写识别。
原文摘要 · Abstract (English)
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.
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