# Evolving Token Communication with Parametric Memory Network

arXiv: https://arxiv.org/abs/2605.01869 · HTML: https://mlsanity.com/paper/2605.01869/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 3 May 2026.

## Builds on (most cited first, 13 of 13)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2308.09361/) (2023)
- [Token Communications: A Large Model-Driven Framework for Cross-modal Context-aware Semantic Communications](https://mlsanity.com/paper/2502.12096/) (2025)
- [Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models](https://mlsanity.com/paper/2508.09874/) (2025)
- [Adaptive Semantic Token Communication for Transformer-based Edge Inference](https://mlsanity.com/paper/2505.17604/) (2025)
- [Semantic Communication with Memory](https://mlsanity.com/paper/2303.12335/) (2023)
- [Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation](https://mlsanity.com/paper/2303.08546/) (2023)
- [Evolving Semantic Communication with Generative Model](https://mlsanity.com/paper/2403.20237/) (2024)
- [Joint Semantic-Channel Coding and Modulation for Token Communications](https://mlsanity.com/paper/2511.15699/) (2025)
- [Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission](https://mlsanity.com/paper/2308.03713/) (2023)
- [Semantic Entropy Can Simultaneously Benefit Transmission Efficiency and Channel Security of Wireless Semantic Communications](https://mlsanity.com/paper/2402.02950/) (2024)
- [Knowledge Enhanced Semantic Communication Receiver](https://mlsanity.com/paper/2302.07727/) (2023)

## Abstract (arXiv)

Token communication has emerged as a promising framework for efficient wireless transmission by representing source data as compact semantic tokens. However, transmitting full semantic tokens still incurs considerable communication overhead. In this paper, we propose an evolving semantic token communication system with a parametric memory network over MIMO fading channels. Specifically, only an equal-length prefix of each semantic token is transmitted, which reduces transmission cost while preserving a consistent token structure for receiver-side recovery. At the receiver, a parametric memory network is introduced to reconstruct the missing suffix information from the received token prefixes, where semantic memory is stored implicitly in the network parameters. To realize this design, full semantic tokens are first organized into a codebook, and truncated tokens are paired with the codeword labels of their corresponding full tokens. Based on these token-label pairs, kNN-based teacher distributions are constructed to fine-tune a pretrained GPT-2-based recovery module, which learns to infer the codeword distribution of each incomplete token and recover the corresponding complete semantic token. In addition, an online evolution strategy is developed to periodically update the parametric memory network and the entire system using newly observed test samples, thereby improving adaptability under distribution shifts. Experimental results demonstrate that the proposed method consistently outperforms the existing evolving memory benchmark under different channel conditions and channel bandwidth ratios, with up to 1.09 dB PSNR improvement.