REVIEW 7 cited by
Token Communications: A Large Model-Driven Framework for Cross-modal Context-aware Semantic Communications
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 7 Pith papers
-
Adaptive Semantic Token Communication for Transformer-based Edge Inference
A single adaptive deep joint source-channel coding model with budget-conditioned token selection and Lyapunov-based resource allocation achieves better accuracy-compression trade-offs than static DJSCC and digital bas...
-
DiSC-Med: Diffusion-based Semantic Communications for Robust Medical Image Transmission
A diffusion-based semantic communication framework for CT images that sends segmentation and edge maps as conditions and regenerates images at the receiver, with a channel-aware denoising module for noise robustness.
-
Text-Guided Token Communication for Wireless Image Transmission
A text-guided token transmission system using pre-trained image and text models outperforms a deep JSCC baseline on perceptual and semantic metrics, but relies on an assumption that text is available at the receiver.
-
Low-Complexity Semantic Packet Aggregation for Token Communication via Lookahead Search
SemPA-Look groups tokens into packets using a leave-one-out residual semantic score and a fixed-width lookahead search, matching near-optimal ATS at linear text-encoding complexity.
-
Adaptive Token Merging for Efficient Transformer Semantic Communication at the Edge
An adaptive, training-free token-merging scheme with Bayesian-optimized per-layer thresholds reduces transformer compute and communication cost substantially while roughly preserving accuracy on ImageNet and VQA tasks.
-
Federated Learning-Enabled Hybrid Language Models for Communication-Efficient Token Transmission
FedHLM uses federated learning to learn token-level uncertainty thresholds that decide when to offload tokens from a small edge LM to a large cloud LM, claiming a 95 percent reduction in LLM transmissions.
-
Wireless Agentic AI with Retrieval-Augmented Multimodal Semantic Perception
A retrieval-augmented semantic communication framework with a DRL scheduler improves task completion efficiency and reduces bandwidth use in a simulated multi-agent autonomous driving setting.
Discussion (0). Sign in to comment.