The paper introduces Token Communication (TokCom) as a native interface for Collaborative Embodied Artificial Intelligence (CEAI), where tokens act as compact semantic carriers and inference units for generative foundation models. It outlines a TokCom-assisted CEAI framework featuring a task‑adaptive communication protocol that includes a compact codebook, syntax rules, and contextual examples to guide token distillation and reconstruction over wireless channels. A case study on collaborative object transport shows that TokCom significantly reduces source payload bit consumption while maintaining task efficiency and robustness to noisy channels.
By Peng Yi, Ying-Chang Liang
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:2608.28726v1 Announce Type: new
Abstract: The remarkable performance of multimodal large language models (MLLMs) comes at the cost of substantial computational overhead, posing significant chal...
By Xinyuan Gui, Shaowen Wang, Sheng Sun, Zijian Wang, Zishu Yu, Zheming Yang
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
arXiv:2606. 09131v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the same computation uniformly to image and language tokens.
By Siyuan Liu, Jinyang Wu
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
arXiv:2505. 10946v3 Announce Type: replace-cross Abstract: Token communications (TokenCom) is an emerging generative semantic communication paradigm, where tokens serve as compact representation units across modalities.
By Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao, Robert Schober, Deniz G\"und\"uz
arXiv:2606. 12688v1 Announce Type: cross Abstract: We are entering a new era of composite model architectures that integrate diverse components such as vision encoders, language backbones, diffusion and flow heads, audio codecs, action generators, and world-model predictors.
By Atindra Jha, Naomi Sagan, Keisuke Kamahori, Irmak Sivgin, Rohan Sanda, Steven Gao, Mark Horowitz, Luke Zettlemoyer, Olivia Hsu, Jure Leskovec, Baris Kasikci, Stephanie Wang
arXiv:2610.16730v1 Announce Type: new
Abstract: The rapid advancement of foundation models and edge intelligence is transforming wireless networks from data-oriented communication systems into artifi...
By Yuquan Xiao, Qinghe Du, Chaoyang Zhang, Wenchi Cheng, Jingqing Wang, Tao Chen, Jiangzhou Wang
arXiv:2505. 18227v4 Announce Type: replace-cross Abstract: In Transformer architectures, tokens\textemdash discrete units derived from raw data\textemdash are formed by segmenting inputs into fixed-length chunks.
By Zhenglun Kong, Yize Li, Fanhu Zeng, Lei Xin, Shvat Messica, Xue Lin, Pu Zhao, Manolis Kellis, Hao Tang, Marinka Zitnik
MoETok introduces a sparse expert routing mechanism for 1D visual tokenizers, enabling distinct Content and Detail queries to specialize in high‑level semantics and fine‑grained visual details, respectively. By jointly optimizing reconstruction and semantic objectives, the router learns role‑specialized processing paths that improve reconstruction, semantic representation, and autoregressive generation across various model scales and token budgets. On ImageNet, MoETok achieves state‑of‑the‑art class‑conditional generation within the 1D tokenizer family and, when scaled to a unified autoregressive model, matches or surpasses diffusion‑based models on benchmarks such as DPG‑Bench, GenEval2, and WISE.
By Jiahao Guo, Bao Tang, Haoxiang Cao, Jingfeng Yao, Bo Li, Wenyu Liu, Kun Gai, Changqian Yu, Xinggang Wang
Ada-TokenCom is a rate‑adaptive token communication framework that uses large autoregressive models to compress and transmit tokens efficiently. It combines next‑token prediction with arithmetic coding, sending only the most informative tokens and letting the receiver generate the rest. A Lyapunov‑based algorithm dynamically adjusts compression and modulation to match changing network conditions, and simulations show it outperforms existing digital and deep joint source‑channel coding baselines.
By Zijun Zhang, Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao, Mehdi Bennis, Kaibin Huang