arXiv:2606. 30595v1 Announce Type: cross Abstract: Semantic communication (SemCom) aims to preserve semantic meaning and task-oriented information beyond conventional message recovery over wireless channels.
By Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus
arXiv:2605. 26595v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison.
By Zedian Shao, Charles Fleming, Teodora Baluta
arXiv:2606. 31973v1 Announce Type: cross Abstract: Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw data, enabling efficient and robust communication under resource and channel constraints.
By Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus
arXiv:2605. 01133v3 Announce Type: replace-cross Abstract: Large language model (LLM)-powered multi-agent systems (MAS) enable agents to communicate and share information, achieving strong performance on complex tasks.
By Lingxi Zhang, Guangtao Zheng, Hanjie Chen
arXiv:2511.14301v4 Announce Type: replace-cross
Abstract: Transformer-based models are highly susceptible to backdoor attacks via supervised fine-tuning (SFT). To red-team existing data-poisoning def...
By Eric Xue, Ruiyi Zhang, Pengtao Xie
The paper introduces DiffSem, a diffusion-based approach for task‑oriented semantic communications that splits the diffusion process between transmitter‑side self‑noising and receiver‑side reverse denoising. It addresses privacy concerns by reducing model‑inversion attacks while preserving task accuracy, as demonstrated on MNIST, CIFAR‑10, and CelebA datasets. The method achieves higher task performance without enlarging transmitted feature size or increasing semantic leakage.
By Xuesong Wang, Mo Li, Xingyan Shi, Zhaoqian Liu, Shenghao Yang