arXiv AI
Queue-Aware Token Communications: Bridging AI Representation and Wireless Service
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 15
From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity
arXiv:2609.10714v1 Announce Type: cross Abstract: The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and ta...
arXiv AI
Oct 15
Stable Token-level Scheduling for Serverless LLM Inference
arXiv:2610.17689v1 Announce Type: new Abstract: Serverless inference is emerging as an efficient deployment model for Large Language Models (LLMs) due to its ability to elastically scale resources, i...
arXiv AI
Sep 16
End-to-End Latency-Minimizing and Load-Balanced Request Scheduling for Edge LLM Inference in Agentic AI Services
arXiv:2609.17193v1 Announce Type: new Abstract: Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference, motivating the deployment of LLMs across distributed...
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 Machine Learning
Jul 24
Wireless TokenCom: RL-Based Tokenizer Agreement for Multi-User Wireless Token Communications
arXiv:2602. 12338v2 Announce Type: replace Abstract: Token Communications (TokenCom) has recently emerged as an effective new paradigm, where tokens are the unified units of multimodal communications and computations, enabling efficient digital semantic- and goal-oriented communications in future wireless networks.
arXiv Computation and Language
Sep 23
TopoCompress: Topology Aware Token Compression Algorithm for Distributed Edge MoE Inference
The paper introduces TopoCompress, a token compression framework designed for distributed edge Mixture-of-Experts (MoE) inference. It jointly optimizes token compression, expert deployment, GPU-CPU residency, and routing to reduce cross-server communication and resource usage. The method uses a two-timescale alternating optimization, with an online loop compressing low-importance tokens and an offline loop updating expert placement based on accumulated traffic.