TONIC: Token-Centric Semantic Communication for Task-Oriented Wireless Systems
Sige Liu, and Kezhi Wang

TL;DR
TONIC introduces a token-centric semantic communication framework that enhances task-oriented wireless systems by aligning transmission with downstream model needs, improving robustness and efficiency.
Contribution
It proposes a modular, interpretable architecture combining semantic-aware protection and confidence gating, with a utility-aware Bayes-risk interpretation for improved task performance.
Findings
Outperforms separation-based schemes and DeepJSCC baselines in image classification tasks.
Demonstrates robustness over AWGN, Rayleigh, and Rician channels.
Effectively integrates token relevance estimation with error protection and gating.
Abstract
Tokens are becoming the basic units through which foundation models represent and process information for understanding and inference. However, traditional wireless communication, centered on bit-level fidelity, faces a mismatch between what is transmitted reliably and what downstream models actually consume. This mismatch calls for a communication design that directly accounts for token-level task relevance and downstream model requirements, rather than treating all transmitted bits as equally important. In this paper, we propose TONIC, a token-centric semantic communication framework for task-oriented wireless systems. The transmitter converts each source sample into a sequence of tokens, estimates token-level task relevance, and allocates protection through utility-aware unequal error protection under a fixed channel-use budget. At the receiver, token-level confidence is used to gate…
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