The paper introduces Memory-Augmented Source Coding (MASC), a scheme that embeds contextual patterns into a source model to improve robustness in low‑SNR communications. MASC uses a shared Parameterized Contextual Memory (PCM) for multi‑order n‑gram patterns and a Mixture‑of‑Memory‑Experts Router (MMER) to selectively activate memory experts based on hidden states, thereby refining probability estimates and shortening code length. Experiments on Rayleigh fading and AWGN channels show that MASC reduces decoding sensitivity to residual channel errors compared to traditional SSCC with autoregressive decoding and LLM‑based Arithmetic Coding.
By Ziqiong Wang, Rongpeng Li, Zhifeng Zhao, Honggang Zhang
Learned image compression (LIC) has achieved impressive rate-distortion performance. However, existing methods remain highly vulnerable to packet loss, a common challenge in satellite and emergency communications.
arXiv:2606. 06273v1 Announce Type: cross Abstract: Lossless pixel-level image transmission is a fundamental regime beyond semantic communications, because exact recovery requires both accurate symbol probability modeling and reliable delivery over noisy channels.
By Tianqi Ren, Rongpeng Li, Xianfu Chen, Yingyu Li, Zhifeng Zhao
arXiv:2606. 12858v1 Announce Type: cross Abstract: Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distortion theory.
By Tong Wu, Zhiyong Chen, Guo Lu, Li Song, Feng Yang, Meixia Tao, Wenjun Zhang
arXiv:2606. 09135v1 Announce Type: cross Abstract: We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model weights, sampling code, or output distributions.
By Felix M\"achtle, Jonas Sander, Sebastian Berndt, Ben Weimar, Nils Loose, Thomas Eisenbarth
arXiv:2607. 08643v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly constrained by memory capacity, weight bandwidth, and checkpoint storage during deployment.
By Yuantian Shao, Peisong Wang, Zhilei Liu, Chuangyi Li, Yuanteng Chen, Pengcheng Xie, Yiwu Yao, Zhihui Wei, Jian Cheng
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. 20538v1 Announce Type: cross Abstract: Long-context Transformer inference increasingly relies on KV-cache compression or quantization.
By Yitao Jiang, Yaoqing Yang, Luyang Zhao, Muhao Chen, Devin Balkcom
arXiv:2608.28687v1 Announce Type: new
Abstract: Generative models have significantly improved the performance ceiling of image lossy compression at low bitrates by exploiting learned priors. However,...
By Jiarun Chen, Kejun Wu, Li Li, Chengtao Cai, Zhengguo Li, Chia-Wen Lin
arXiv:2410. 13056v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substantial memory requirements imposed by their large parameter sizes.
By Zihan Chen, Bike Xie, Jundong Li, Cong Shen
arXiv:2604. 22005v2 Announce Type: replace-cross Abstract: Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems.
By Junjie Zhao, Guangming Liang, Xiaonan Liu, Dongzhu Liu
arXiv:2608.23144v1 Announce Type: cross
Abstract: Low-bit weight quantization saves storage but leaves errors that degrade language-model quality. We introduce Activation-Weighted Seeded Residual Cod...
By Zehao Liu, Chuangchuang Fang, Yang Ren