# Context-Aware Iterative Token Detection and Masked Transmission for Wireless Token Communication

arXiv: https://arxiv.org/abs/2601.17770 · HTML: https://mlsanity.com/paper/2601.17770/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 25 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Token-Domain Multiple Access: Exploiting Semantic Orthogonality for Collision Mitigation](https://mlsanity.com/paper/2502.06118/) (2025)
- [Communication-Efficient Hybrid Language Model via Uncertainty-Aware Opportunistic and Compressed Transmission](https://mlsanity.com/paper/2505.11788/) (2025)
- [Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search](https://mlsanity.com/paper/2506.19451/) (2025)
- [ESC-MVQ: End-to-End Semantic Communication With Multi-Codebook Vector Quantization](https://mlsanity.com/paper/2504.11709/) (2025)
- [MIMO Detection under Hardware Impairments: Data Augmentation With Boosting](https://mlsanity.com/paper/2412.06049/) (2024)

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

- [Context-Aware Wireless Token Communication via Joint Token Masking and Detection](https://mlsanity.com/paper/2605.02123/) (2026)

## Abstract (arXiv)

The success of large-scale language models has established tokens as compact and meaningful units for natural-language representation, which motivates token communication over wireless channels, where tokens are considered fundamental units for wireless transmission. We propose a context-aware token communication framework that uses a pretrained masked language model (MLM) as a shared contextual probability model between the transmitter (Tx) and receiver (Rx). At Rx, we develop an iterative token detection method that jointly exploits MLM-guided contextual priors and channel observations based on a Bayesian perspective. At Tx, we additionally introduce a context-aware masking strategy which skips highly predictable token transmission to reduce transmission rate. Simulation results demonstrate that the proposed framework substantially improves reconstructed sentence quality and supports effective rate adaptation under various channel conditions.