arXiv:2606. 07684v1 Announce Type: cross Abstract: Disaggregated serving alleviates memory bottlenecks in Large Language Model (LLM) inference but creates a severe communication bottleneck: transmitting high-dimensional Key-Value (KV) caches often dominates time-to-first-token (TTFT).
By Qianli Ma, Zhiqing Tang, Hanshuai Cui, Zhi Yao, Weijia Jia
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: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
Token-Budget Distillation (TBD) is a parameter‑efficient fine‑tuning framework that adapts video vision‑language models to a fixed token budget. It freezes the pretrained backbone, updates only LoRA adapters, and incorporates FlashVID visual token compression. TBD uses a dual‑path teacher‑student design with full‑token supervision and compressed student optimization, enabling the student to recover full‑token semantics while remaining efficient under aggressive token reduction.
By Xiaoyang Guo, Guoping Luo, Jusheng Zhang, Keze Wang, Wenhao 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
arXiv:2607. 25527v1 Announce Type: cross Abstract: Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between the visual features needed for these two capabilities.
By Weiming Zhuang, Jiabo Huang, Jingtao Li, Zhizhong Li, Chen Chen, Sina Sajadmanesh, Lingjuan Lyu