# Token Communications: A Large Model-Driven Framework for Cross-modal Context-aware Semantic Communications

arXiv: https://arxiv.org/abs/2502.12096 · HTML: https://mlsanity.com/paper/2502.12096/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 18 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [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)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [An Image is Worth 32 Tokens for Reconstruction and Generation](https://mlsanity.com/paper/2406.07550/) (2024)
- [Language Modeling Is Compression](https://mlsanity.com/paper/2309.10668/) (2023)
- [Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff](https://mlsanity.com/paper/1901.07821/) (2019)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [Generative AI-driven Semantic Communication Networks: Architecture, Technologies and Applications](https://mlsanity.com/paper/2401.00124/) (2023)
- [Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2403.17256/) (2024)
- [Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large Language Models](https://mlsanity.com/paper/2412.12687/) (2024)
- [Generative Semantic Communication via Textual Prompts: Latency Performance Tradeoffs](https://mlsanity.com/paper/2409.09715/) (2024)
- [S-RAN: Semantic-Aware Radio Access Networks](https://mlsanity.com/paper/2407.11161/) (2024)

## Built on by (most cited first, 30 of 35)

- [Adaptive Semantic Token Communication for Transformer-based Edge Inference](https://mlsanity.com/paper/2505.17604/) (2025)
- [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)
- [Adaptive Token Merging for Efficient Transformer Semantic Communication at the Edge](https://mlsanity.com/paper/2509.09955/) (2025)
- [Joint Semantic-Channel Coding and Modulation for Token Communications](https://mlsanity.com/paper/2511.15699/) (2025)
- [Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search](https://mlsanity.com/paper/2506.19451/) (2025)
- [Video TokenCom: Textual Intent-Guided Multi-Rate Video Token Communications with UEP-Based Adaptive Source-Channel Coding](https://mlsanity.com/paper/2603.02470/) (2026)
- [Task-Oriented Multimodal Token Transmission in Resource-Constrained Multiuser Networks](https://mlsanity.com/paper/2505.07841/) (2025)
- [Revisiting MLLM Token Technology through the Lens of Classical Visual Coding](https://mlsanity.com/paper/2508.13460/) (2025)
- [TokenCom: Vision-Language Model for Multimodal and Multitask Token Communications](https://mlsanity.com/paper/2603.00482/) (2026)
- [Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission](https://mlsanity.com/paper/2507.00082/) (2025)
- [Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication](https://mlsanity.com/paper/2509.09168/) (2025)
- [Compression Tells Intelligence: Visual Coding, Visual Token Technology, and the Unification](https://mlsanity.com/paper/2601.20742/) (2026)
- [Wireless TokenCom: RL-Based Tokenizer Agreement for Multi-User Wireless Token Communications](https://mlsanity.com/paper/2602.12338/) (2026)
- [Token Encoding for Semantic Recovery](https://mlsanity.com/paper/2604.12931/) (2026)
- [Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models](https://mlsanity.com/paper/2604.26508/) (2026)
- [Context-Aware Wireless Token Communication via Joint Token Masking and Detection](https://mlsanity.com/paper/2605.02123/) (2026)
- [Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation](https://mlsanity.com/paper/2608.28086/) (2026)
- [Task-Adaptive Semantic Communications with Controllable Diffusion-based Data Regeneration](https://mlsanity.com/paper/2505.07980/) (2025)
- [TACO: Rethinking Semantic Communications with Task Adaptation and Context Embedding](https://mlsanity.com/paper/2505.10834/) (2025)
- [DiSC-Med: Diffusion-based Semantic Communications for Robust Medical Image Transmission](https://mlsanity.com/paper/2508.00172/) (2025)
- [Goal-Oriented Multi-Agent Semantic Networking: Unifying Intents, Semantics, and Intelligence](https://mlsanity.com/paper/2512.01035/) (2025)
- [MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation](https://mlsanity.com/paper/2602.18332/) (2026)
- [Evolving Token Communication with Parametric Memory Network](https://mlsanity.com/paper/2605.01869/) (2026)
- [Explainable Task-Oriented Token Communication for AI-Native 6G Networks](https://mlsanity.com/paper/2606.14808/) (2026)
- [Loss-Resilient Wireless Video Token Communication over Block Fading Channels](https://mlsanity.com/paper/2608.08698/) (2026)
- [ImpactHO: Importance-Aware KV Cache Transfer for Multi-User Edge LLM Handover](https://mlsanity.com/paper/2608.10545/) (2026)
- [Token-Oriented Semantic Communication with Pretrained Vision Transformers](https://mlsanity.com/paper/2608.25410/) (2026)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)
- [SpaceDiffusion: Over-the-Orbit Diffusion for Space Generate-and-Forward Communications](https://mlsanity.com/paper/2609.20899/) (2026)

## Abstract (arXiv)

In this paper, we introduce token communications (TokCom), a large model-driven framework to leverage cross-modal context information in generative semantic communications (GenSC). TokCom is a new paradigm, motivated by the recent success of generative foundation models and multimodal large language models (GFM/MLLMs), where the communication units are tokens, enabling efficient transformer-based token processing at the transmitter and receiver. In this paper, we introduce the potential opportunities and challenges of leveraging context in GenSC, explore how to integrate GFM/MLLMs-based token processing into semantic communication systems to leverage cross-modal context effectively at affordable complexity, present the key principles for efficient TokCom at various layers in future wireless networks. In a typical image semantic communication setup, we demonstrate a significant improvement of the bandwidth efficiency, achieved by TokCom by leveraging the context information among tokens. Finally, the potential research directions are identified to facilitate adoption of TokCom in future wireless networks.