arXiv AI
From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity
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arXiv AI
Oct 2
Token Communication-Assisted Collaborative Embodied Artificial Intelligence: Concepts, Framework, and Opportunities
The paper introduces Token Communication (TokCom) as a native interface for Collaborative Embodied Artificial Intelligence (CEAI), where tokens act as compact semantic carriers and inference units for generative foundation models. It outlines a TokCom-assisted CEAI framework featuring a task‑adaptive communication protocol that includes a compact codebook, syntax rules, and contextual examples to guide token distillation and reconstruction over wireless channels. A case study on collaborative object transport shows that TokCom significantly reduces source payload bit consumption while maintaining task efficiency and robustness to noisy channels.
arXiv Machine Learning
Aug 27
Token-Oriented Semantic Communication with Pretrained Vision Transformers
The paper introduces a token‑oriented semantic communication framework that transmits only task‑relevant image latents instead of full token embeddings, reducing communication cost and improving interoperability. It leverages a spatial alignment between vision transformer patch tokens and learned image compression latents, enabling token‑level relevance estimation and selective transmission. Experiments on ImageNet demonstrate a superior rate–accuracy trade‑off compared to existing semantic communication methods and hand‑crafted codecs.
arXiv AI
Sep 1
Pro-Router: Token-Aware Progressive Model Routing with Adaptive Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
arXiv:2608.28726v1 Announce Type: new Abstract: The remarkable performance of multimodal large language models (MLLMs) comes at the cost of substantial computational overhead, posing significant chal...
arXiv AI
Aug 12
Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
arXiv AI
Jun 9
Late-Layer Fusion is Enough: Dual-Path Vision Token Routing for Multimodal Large Language Models under Visual Saturation
arXiv:2606. 09131v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) commonly inherit the deep, symmetric Transformer backbone designed for unimodal text modeling, and apply the same computation uniformly to image and language tokens.
arXiv Machine Learning
Sep 11
EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression
The paper introduces EMMI, a framework that enables communication‑efficient inference of multimodal large language models (MLLMs) on edge devices. EMMI encodes each sensor modality separately, fuses the representations, and compresses them into a compact latent vector that is transmitted to a server for high‑capacity reasoning. Experiments on a multimodal benchmark show that EMMI can cut the communication payload by 32× while keeping accuracy comparable, achieving up to a 3.4× reduction in end‑to‑end inference latency under bandwidth‑constrained conditions.