The groundbreaking development of generative artificial intelligence (AI) is rapidly boosting the ability to generate content such as images and videos, reshaping communication paradigms. This article introduces generative communications (GenCom), a novel paradigm for 6G networks in which large AI models (LAMs) drive semantic understanding, reasoning, and content generation, embedding these into the communication process.
arXiv:2609.10714v1 Announce Type: cross
Abstract: The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and ta...
By Yu Ma, Zhen Gao, Li Qiao, Xiaoyuan Zhang, Mahdi Boloursaz Mashhadi, Yin Xu, Wenjun Xu, Xiaodong Xu, Kaibin Huang, Jiangzhou Wang, Rahim Tafazolli, Sheng Chen, Tony Q. S. Quek, Ping Zhang
arXiv:2610.01826v1 Announce Type: cross
Abstract: Collaborative embodied artificial intelligence (CEAI) enables multiple physical agents to perceive, reason, and act cooperatively in dynamic environm...
By Peng Yi, Ying-Chang Liang
The article discusses how vision generative AI models, while rapidly advancing, have largely been developed with a focus on output quality, leading to hardware that adapts reactively to increasing model demands. It evaluates the parameter cost and energy efficiency of these models across various accelerator platforms and aligns four generative model families with seven real-world application domains. The authors propose a software‑hardware co‑design strategy that considers deployment constraints from the outset, ensuring that the appropriate model runs on suitable hardware for specific applications, thereby making generative AI deployment more sustainable and widely accessible.
By Eleni Tselepi, Cristian Sestito, Shady Agwa, Themis Prodromakis
arXiv:2608. 14694v1 Announce Type: new Abstract: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks.
By Naveed Khan, Besan Al Sbeihi, Maryam Alshehhi, Nasir Saeed
arXiv:2608. 14600v1 Announce Type: cross Abstract: We present a demonstration for generative multicasting with on-device, intent-aware semantic decomposition.
By Xinkai Liu, Mahdi Boloursaz Mashhadi, Yi Ma, Rahim Tafazolli
arXiv:2607. 27372v1 Announce Type: new Abstract: The deep learning revolution, kicked off by AlexNet, taught us that end-to-end training beats decomposing a problem into hand-designed stages.
By Alexi Gladstone, Heng Ji, Yilun Du
The paper introduces FMSGOC, a framework that leverages visual‑linguistic foundation models to improve semantic and goal‑oriented communication for 6G. By transmitting a sparse set of semantic anchors and using a pretrained diffusion model for masked completion, it reduces overfitting and achieves high rate efficiency, reaching 0.039 BPP while maintaining strong semantic fidelity and robustness on unseen data.
By Boliang Liu, Wint Yi Poe, Riccardo Trivisonno, Giuseppe Caire
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
By Cyril Shih-Huan Hsu, Wig Yuan-Cheng Cheng, Chrysa Papagianni
The paper introduces a GAN‑based semantic communication framework for image transmission in the Internet of Vehicles, aiming to overcome bandwidth and channel limitations. At the transmitter, a pyramid attention network extracts semantic label maps and a priority mechanism assigns weights to categories based on driving safety, guiding bit allocation and loss design. The receiver reconstructs images using a coarse‑to‑fine multi‑resolution generator, multi‑scale discriminator, temporal consistency, spatial pyramid pooling, and class‑aware convolutions, achieving high‑fidelity results with combined adversarial, feature‑matching, and perceptual losses. Experiments on Cityscapes demonstrate superior semantic segmentation accuracy and image quality compared to existing methods, with stable performance under AWGN and Rayleigh channels.
By Ruixing Ren, Shan Chen, Junhui Zhao, Xiaoke Sun
The paper introduces UniSandbox, a decoupled evaluation framework with controlled synthetic datasets, to study whether understanding informs generation in Unified Multimodal Models. Results show a notable understanding‑generation gap, especially in reasoning generation and knowledge transfer. Explicit Chain‑of‑Thought (CoT) in the understanding module bridges this gap, and self‑training can internalize CoT for implicit reasoning during generation; query‑based architectures also exhibit latent CoT‑like properties that aid knowledge transfer.
By Yuwei Niu, Weiyang Jin, Jiaqi Liao, Chaoran Feng, Peng Jin, Bin Lin, Zongjian Li, Bin Zhu, Weihao Yu, Li Yuan
arXiv:2603.25507v2 Announce Type: replace-cross
Abstract: Network Traffic Classification (NTC) increasingly relies on data-driven models, yet its practical deployment is often constrained by limited...
By Giampaolo Bovenzi, Domenico Ciuonzo, Jonatan Krolikowski, Antonio Montieri, Alfredo Nascita, Antonio Pescap\`e, Dario Rossi