Large Language Models have shown growing ability to generate fluent and coherent texts that are highly similar to the writing style of humans. Current detectors for Machine-Generated Text (MGT) perform well when they are trained and tested in the same domain but generalize poorly to unseen domains, due to domain shift between data from different sources. In this work, we propose MGT-Prism , an MGT detection method from the perspective of the frequency domain for better domain generalization. Our key insight stems from analyzing text representations in the frequency domain, where we observe consistent spectral patterns across diverse domains, while significant discrepancies in magnitude emerge between MGT and human-written texts (HWTs). The observation initiates the design of a low frequency domain filtering module for filtering out the document-level features that are sensitive to domain shift, and a dynamic spectrum alignment strategy to extract the task-specific and domain-invariant features for improving the detector's performance in domain generalization. Extensive experiments demonstrate that MGT-Prism outperforms state‑of‑the‑art baselines by an average of 0.90% in accuracy and 0.92% in F1 score on 11 test datasets across three domain‑generalization scenarios.

Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This paper studies the problem of the lightweight image semantic communication system that is deployed on Internet of Things (IoT) devices. In the considered system model, devices must use semantic communication techniques to support user behavior recognition in ultimate video service with high data transmission efficiency. However, it is computationally expensive for IoT devices to deploy semantic codecs due to the complex calculation processes of deep learning (DL) based codec training and inference. To make it affordable for IoT devices to deploy semantic communication systems, we propose an attention-based UNet enabled lightweight image semantic communication (LSSC) system, which achieves low computational complexity and small model size. In particular, we first let the LSSC system train the codec at the edge server to reduce the training computation load on IoT devices. Then, we introduce the convolutional block attention module (CBAM) to extract the image semantic features and decrease the number of downsampling layers thus reducing the floating-point operations (FLOPs). Finally, we experimentally adjust the structure of the codec and find out the optimal number of downsampling layers. Simulation results show that the proposed LSSC system can reduce the semantic codec FLOPs by 14%, and reduce the model size by 55%, with a sacrifice of 3% accuracy, compared to the baseline. Moreover, the proposed scheme can achieve a higher transmission accuracy than the traditional communication scheme in the low channel signal-to-noise (SNR) region.