12 papers
MORES: Mobile Reasoning-as-a-Service via Distributed LLM Inference-Time Scaling
Guanchen Liu, Hongyang Du, Kaibin Huang
Inference-time scaling has emerged as an effective approach for enhancing the capabilities of Large Language Models (LLMs), addressing the growing demand for stronger reasoning wit…
Multi-objective Low-altitude IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning
Wenwen Xie, Geng Sun, Chuang Zhang +3
Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless netwo…
Multi-SPIN: Multi-Access Speculative Inference for Cooperative Token Generation at the Edge
Haotian Zheng, Zhanwei Wang, Mingyao Cui +3
Speculative inference (SPIN) was originally developed as an efficient architecture to accelerate Large Language Models (LLMs). In this work, we propose its distributed deployment t…
NetMCP: Network-Aware Model Context Protocol Platform for LLM Capability Extension
Enhan Li, Hongyang Du, Kaibin Huang
Large Language Models (LLMs) remain static in functionality after training, and extending their capabilities requires integration with external data, computation, and services. The…
Experience Scaling: Post-Deployment Evolution For Large Language Models
Xingkun Yin, Kaibin Huang, Dong In Kim +1
Scaling model size, training data, and compute power have driven advances in large language models (LLMs), but these approaches are reaching saturation as human-generated text is e…
Ubiquitous Intelligence Via Wireless Network-Driven LLMs Evolution
Xingkun Yin, Feiran You, Hongyang Du +1
We introduce ubiquitous intelligence as a paradigm where Large Language Models (LLMs) evolve within wireless network-driven ecosystems. Unlike static model deployments, this approa…