# TWIST: Closed-Loop token Synchronization for Application-Aware Wireless Digital Twins

arXiv: https://arxiv.org/abs/2605.27205 · HTML: https://mlsanity.com/paper/2605.27205/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 32 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 28 of 28)

- [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)
- [Deep Joint Source-Channel Coding for Semantic Communications](https://mlsanity.com/paper/2211.08747/) (2022)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2206.08100/) (2022)
- [Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond](https://mlsanity.com/paper/2301.11283/) (2023)
- [6G Digital Twin Networks: From Theory to Practice](https://mlsanity.com/paper/2212.02032/) (2022)
- [Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler](https://mlsanity.com/paper/2311.17451/) (2023)
- [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)
- [Digital Twin Based Beam Prediction: Can we Train in the Digital World and Deploy in Reality?](https://mlsanity.com/paper/2301.07682/) (2023)
- [Token Communication in the Era of Large Models: An Information Bottleneck-Based Approach](https://mlsanity.com/paper/2507.01728/) (2025)
- [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)
- [Digital Twin Channel for 6G: Concepts, Architectures and Potential Applications](https://mlsanity.com/paper/2403.12467/) (2024)
- [Digital Twin-Empowered Communications: A New Frontier of Wireless Networks](https://mlsanity.com/paper/2307.00973/) (2023)
- [Digital Twins and Testbeds for Supporting AI Research with Autonomous Vehicle Networks](https://mlsanity.com/paper/2404.00954/) (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)

## Abstract (arXiv)

Wireless digital twins require repeated synchronization between a time-evolving physical scene and its digital counterpart under limited and time-varying communication resources. For perception-centric twins, pixel-domain transmission or uniformly protected bitstreams can be mismatched to the semantic state consumed by twin-side applications. This paper proposes TWIST, a closed-loop token synchronization framework for application-aware wireless digital twins. TWIST represents each physical observation as a token and synchronizes this state over a wireless link, rather than optimizing visual reconstruction. Token positions are grouped by task relevance and protected through mode-conditioned unequal error protection under low-, medium-, and high-synchronization modes. At the twin side, decoding confidence converts unreliable hard token decisions into erasures, which are restored by a completion model before updating the semantic twin state. The recovered state supports traffic-state inference and generates compact feedback statistics, including channel quality, receiver uncertainty, semantic drift, and application priority, for subsequent mode adaptation. Experiments on a dynamic road-scene digital-twin scenario show that TWIST improves traffic-state inference and semantic twin-state synchronization compared with fixed-mode and channel-only adaptation strategies, while reducing the average synchronization cost relative to always-high transmission.