@inproceedings{al-qasem-2026-u,
    title = "{U}-{R}o{CX}: An x{LSTM} based Approach to {AI}-Generated {U}rdu Text Detection",
    author = "Al-Qasem, Rabee Adel",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.abjadnlp-1.53/",
    pages = "443--447",
    abstract = "Large Language Models (LLMs) have rapidly proliferated, presenting challenges in distinguishing human-written text from AI-generated content, especially in low-resource languages like Urdu. This paper introduces U-RoCX, a novel hybrid architecture for the AbjadGenEval Shared Task on AI-Generated Urdu Text Detection. U-RoCX combines the multilingual semantic capabilities of a frozen XLM-RoBERTa backbone with local feature extraction from Convolutional Neural Networks (CNNs) and the advanced sequential modeling of the recently proposed Extended LSTM (xLSTM). By utilizing xLSTM{'}s matrix memory and covariance update rules, the model addresses traditional Recurrent Neural Network bottlenecks. Experimental results demonstrate the robustness of U-RoCX, achieving a balanced accuracy and F1-score of 88{\%} on the test set."
}