Token Encoding for Semantic Recovery

2026-04-14

AI Summary

This work addresses the severe semantic distortion in wireless semantic communication caused by token loss. To this end, the authors propose TokCode—a plug-and-play token coding framework that incurs no additional transmission overhead. TokCode leverages a sentence-semantics-guided foundation model adaptation (SFMA) algorithm to optimize the encoder, enhancing robustness in semantic recovery without requiring end-to-end retraining. Integrated with a prompt-based generative image transmission mechanism, the proposed approach significantly reduces semantic distortion under harsh channel conditions with 40%–60% random token loss, achieving performance close to the theoretical upper bound.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Computer Vision: Diffusion Models for Vision
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Token-based semantic communication is promising for future wireless networks, as it can compact semantic tokens under very limited channel capacity. However, harsh wireless channels often cause missing tokens, leading to severe distortion that prevents reliable semantic recovery at the receiver. In this article, we propose a token encoding framework for robust semantic recovery (TokCode), which incurs no additional transmission overhead and supports plug-and-play deployment. For efficient token encoder optimization, we develop a sentence-semantic-guided foundation model adaptation algorithm (SFMA) that avoids costly end-to-end training. Based on simulation results on prompt-based generative image transmission, TokCode mitigates semantic distortion and can approach the performance upper-bound, even under harsh channels where 40% to 60% of tokens are randomly lost.

Problem

• semantic communication
• token loss
• semantic distortion
• wireless channels
• semantic recovery

Innovation

• token encoding
• semantic communication
• robust recovery
• foundation model adaptation
• wireless transmission