LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4

• URL: http://arxiv.org/abs/2607.15509v1
• Date: Thu, 16 Jul 2026 23:43:23 GMT
• Title: LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4
• Abstract summary: We present a fully automated AutoML framework that uses GPT-5, GPT-4o, and Claude Sonnet 4 as autonomous neural architecture designers.<n>The framework is evaluated on Arabic, Persian, and English handwriting datasets through 270 independent experiments.<n>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.
• Score: 0.5735035463793009
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models [69.2503510410147]
  We present a unified autonomous driving framework built upon a pretrained VLM.<n>We demonstrate that pretrained VLM attention exhibits strong transferability beyond pure language modeling.<n>Experiments on end-to-end autonomous driving benchmarks demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-20T07:50:00Z)
• Development of ML model for triboelectric nanogenerator based sign language detection system [0.0]
  Sign language recognition is vital for bridging communication gaps between deaf and hearing communities.<n>This work presents a comparison of machine learning (ML) and deep learning models for a custom triboelectric nanogenerator (TENG)-based sensor glove.<n>The proposed MFCC CNN-LSTM architecture processes frequency-domain features from each sensor through independent convolutional branches before fusion.
  arXiv  Detail & Related papers  (2026-03-26T08:35:02Z)
• From Memorization to Creativity: LLM as a Designer of Novel Neural-Architectures [48.83701310501069]
  Large language models (LLMs) excel in program synthesis, yet their ability to autonomously navigate neural architecture design-balancing reliability, performance, and structural novelty--remains underexplored.<n>We address this by placing a code-oriented LLM within a closed-loop synthesis framework, analyzing its evolution over 22 supervised fine-tuning cycles.
  arXiv  Detail & Related papers  (2026-01-06T13:20:28Z)
• LLM as a Neural Architect: Controlled Generation of Image Captioning Models Under Strict API Contracts [48.83701310501069]
  We present NN-Caption, an LLM-guided neural architecture search pipeline.<n>It generates runnable image-captioning models by composing CNN encoders from LEMUR's classification backbones.<n>This work presents a pipeline that integrates prompt-based code generation with automatic evaluation.
  arXiv  Detail & Related papers  (2025-12-07T10:47:28Z)
• NNGPT: Rethinking AutoML with Large Language Models [36.90850535125572]
  NNGPT is an open-source framework that turns a large language model (LLM) into a self-improving AutoML engine for neural network development.<n>It integrates within one unified workflow five synergistic LLM-based pipelines: zero-shot architecture synthesis, hyper parameter optimization, code-aware accuracy/early-stop prediction, and reinforcement learning.<n>The system has already generated over 5K validated models, proving NNGPT as an autonomous AutoML engine.
  arXiv  Detail & Related papers  (2025-11-25T14:10:44Z)
• The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution [2.334824705384299]
  We present the first large-scale study exploring whether JavaScript code generated by Large Language Models can reveal which model produced it.<n>We show that individual LLMs leave unique stylistic signatures, even among models belonging to the same family or parameter size.<n>We introduce LLM-NodeJS, a dataset of 50,000 Node.js back-end programs from 20 large language models.
  arXiv  Detail & Related papers  (2025-10-12T07:51:03Z)
• Accelerated Test-Time Scaling with Model-Free Speculative Sampling [58.69141724095398]
  We introduce STAND (STochastic Adaptive N-gram Drafting), a novel model-free speculative decoding approach.<n>We show that STAND reduces inference latency by 60-65% compared to standard autoregressive decoding.<n>As a model-free approach, STAND can be applied to any existing language model without additional training.
  arXiv  Detail & Related papers  (2025-06-05T07:31:18Z)
• Improving Massively Multilingual ASR With Auxiliary CTC Objectives [40.10307386370194]
  We introduce our work on improving performance on FLEURS, a 102-language open ASR benchmark. We investigate techniques inspired from recent Connectionist Temporal Classification ( CTC) studies to help the model handle the large number of languages. Our state-of-the-art systems using self-supervised models with the Conformer architecture improve over the results of prior work on FLEURS by a relative 28.4% CER.
  arXiv  Detail & Related papers  (2023-02-24T18:59:51Z)
• Lexically Aware Semi-Supervised Learning for OCR Post-Correction [90.54336622024299]
  Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Previous work has demonstrated the utility of neural post-correction methods on recognition of less-well-resourced languages. We present a semi-supervised learning method that makes it possible to utilize raw images to improve performance.
  arXiv  Detail & Related papers  (2021-11-04T04:39:02Z)
• Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition [66.47000813920619]
  We propose a non-autoregressive end-to-end speech recognition system called LASO. Because of the non-autoregressive property, LASO predicts a textual token in the sequence without the dependence on other tokens. We conduct experiments on publicly available Chinese dataset AISHELL-1.
  arXiv  Detail & Related papers  (2020-05-11T04:45:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.