# TONIC: Token-Centric Semantic Communication for Task-Oriented Wireless Systems

arXiv: https://arxiv.org/abs/2605.21553 · HTML: https://mlsanity.com/paper/2605.21553/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 34 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [MaskGIT: Masked Generative Image Transformer](https://mlsanity.com/paper/2202.04200/) (2022)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [Task-Oriented Multi-User Semantic Communications for VQA Task](https://mlsanity.com/paper/2108.07357/) (2021)
- [SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2308.09361/) (2023)
- [Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2009.12480/) (2020)
- [DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2206.08100/) (2022)
- [Deep Joint Source Channel Coding for WirelessImage Transmission with OFDM](https://mlsanity.com/paper/2101.03909/) (2021)
- [D2-JSCC: Digital Deep Joint Source-channel Coding for Semantic Communications](https://mlsanity.com/paper/2403.07338/) (2024)
- [Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints](https://mlsanity.com/paper/2407.18468/) (2024)
- [Task-oriented Explainable Semantic Communications](https://mlsanity.com/paper/2302.13560/) (2023)
- [Token Communication in the Era of Large Models: An Information Bottleneck-Based Approach](https://mlsanity.com/paper/2507.01728/) (2025)
- [Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocation](https://mlsanity.com/paper/2411.04575/) (2024)
- [Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks](https://mlsanity.com/paper/2403.10613/) (2024)
- [ToDMA: Large Model-Driven Massive Token Communications for Semantic Multiple Access](https://mlsanity.com/paper/2505.10946/) (2025)
- [Joint Semantic-Channel Coding and Modulation for Token Communications](https://mlsanity.com/paper/2511.15699/) (2025)
- [Language-oriented Semantic Communication for Image Transmission with Fine-Tuned Diffusion Model](https://mlsanity.com/paper/2409.17104/) (2024)
- [Generative Semantic Communication for Joint Image Transmission and Segmentation](https://mlsanity.com/paper/2411.18005/) (2024)
- [Forget BIT, It is All about TOKEN: Towards Semantic Information Theory for LLMs](https://mlsanity.com/paper/2511.01202/) (2025)
- [Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission](https://mlsanity.com/paper/2507.00082/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond Token Accuracy: Prioritizing What Matters for Visual Reconstruction](https://mlsanity.com/paper/2610.03822/) (2026)

## Abstract (arXiv)

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 unreliable decisions, turning harmful substitutions into recoverable erasures before a Transformer-based completion model restores the masked tokens for final task inference. Our framework combines transmitter-side semantic-aware protection with receiver-side confidence-aware gating in a modular and interpretable architecture, rather than relying solely on fully black-box end-to-end learning. We further establish a utility-aware Bayes-risk interpretation for the receiver-side gating rule and study its interaction with unequal protection and completion. Experimental results on image classification show that TONIC consistently outperforms separation-based schemes, the pixel-domain DeepJSCC baseline, and token-domain baselines under matched communication budgets over AWGN, Rayleigh, and Rician channels.