arXiv AI 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

From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Machine Learning
Aug 27

Token-Oriented Semantic Communication with Pretrained Vision Transformers

The paper introduces a token‑oriented semantic communication framework that transmits only task‑relevant image latents instead of full token embeddings, reducing communication cost and improving interoperability. It leverages a spatial alignment between vision transformer patch tokens and learned image compression latents, enabling token‑level relevance estimation and selective transmission. Experiments on ImageNet demonstrate a superior rate–accuracy trade‑off compared to existing semantic communication methods and hand‑crafted codecs.

By Jiwoong Im, Minwoo Kim, Jaeho Lee, Yo-Seb Jeon, Yongjune Kim
arXiv Machine Learning
Sep 11

EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression

The paper introduces EMMI, a framework that enables communication‑efficient inference of multimodal large language models (MLLMs) on edge devices. EMMI encodes each sensor modality separately, fuses the representations, and compresses them into a compact latent vector that is transmitted to a server for high‑capacity reasoning. Experiments on a multimodal benchmark show that EMMI can cut the communication payload by 32× while keeping accuracy comparable, achieving up to a 3.4× reduction in end‑to‑end inference latency under bandwidth‑constrained conditions.

By Motahare Mounesan, Irfan Khan