A study published in Engineering delves into the integration of generative artificial intelligence (GAI) with semantic communication (SemCom), a key technology for sixth-generation (6G) wireless networks, and proposes a novel large language model (LLM)-native generative SemCom system that shifts the communication paradigm from information recovery to information regeneration. SemCom, which transmits semantic meaning rather than raw bitstreams to boost communication efficiency, has long faced limitations in generalization, robustness and reasoning capabilities when based on traditional deep learning. GAI, with its strengths in learning complex data distributions and generating high-quality content, emerges as a viable solution to address these challenges, according to the research team composed of scholars from institutions including The Chinese University of Hong Kong, Shenzhen and Pengcheng Laboratory.

The paper systematically analyzes three SemCom systems empowered by classical GAI models: variational autoencoders (VAEs), generative adversarial networks (GANs) and diffusion models (DMs). For each model, the research elaborates on its fundamental concepts, corresponding SemCom architectures and latest research progress, noting their respective applications in semantic coding, joint source-channel coding (JSCC), channel modeling and equalization across text, image and audio modalities. Building on this foundation, the study introduces an LLM-driven generative SemCom system, which equips both transmitter and receiver with LLM-based AI agents acting as the core for information understanding and content generation, respectively, alongside channel adaptation modules for reliable signal transmission. The LLM-based AI agents integrate components like perception encoders, data-driven LLM adaptation and memory systems, enabling the transmitter to extract compact semantic embeddings from multimodal input data and the receiver to directly generate task-oriented content from the received semantic information.

A point-to-point video retrieval case study validates the system’s effectiveness, showing it achieves a 99.98% reduction in communication overhead and a 53% improvement in average retrieval accuracy compared to traditional communication systems, while also demonstrating superior robustness against channel noise. The research further identifies four promising application scenarios for generative SemCom: industrial Internet of Things (IIoT), vehicle-to-everything (V2X), the metaverse and the low-altitude economy, where the technology can streamline data transmission, enhance real-time processing and improve privacy protection. Additionally, the paper outlines three key open issues for future research, including the deployment of LLMs on resource-constrained edge devices, the dynamic evolution of AI agents at transceivers and privacy and security concerns during semantic transmission, proposing targeted solutions such as model compression, continual learning and advanced encryption technologies for each challenge.

The study provides a comprehensive guideline for the application of GAI in SemCom, laying a groundwork for the efficient deployment of generative SemCom in future 6G wireless networks and offering insights for the integration of advanced AI technologies with next-generation communication systems.

The paper “Generative Semantic Communication: Architectures, Technologies, and Applications,” is authored by Jinke Ren, Yaping Sun, Hongyang Du, Weiwen Yuan, Chongjie Wang, Xianda Wang, Yingbin Zhou, Ziwei Zhu, Fangxin Wang, Shuguang Cui. Full text of the open access paper: https://doi.org/10.1016/j.eng.2025.07.022. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.