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.