8 papers
Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks
Yang Liu, Kejia Zhang, Bingjie Yan +11
Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-specific tasks; small models (SMs), in c…
From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)
Binghan Wu, Shoufeng Wang, Yunxin Liu +3
Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive a…
Leveraging AI Agents for Autonomous Networks: A Reference Architecture and Empirical Studies
Binghan Wu, Shoufeng Wang, Yunxin Liu +3
The evolution toward Level 4 (L4) Autonomous Networks (AN) represents a strategic inflection point in telecommunications, where networks must transcend reactive automation to achie…
Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift
Chengtao Jian, Kai Yang, Tianhao Gao +5
Direct Preference Learning has emerged as a dominant offline paradigm for preference optimization. Most of these methods are based on the Bradley-Terry (BT) model for pairwise pref…
FedMMKT:Co-Enhancing a Server Text-to-Image Model and Client Task Models in Multi-Modal Federated Learning
Ningxin He, Yang Liu, Wei Sun +4
Text-to-Image (T2I) models have demonstrated their versatility in a wide range of applications. However, adaptation of T2I models to specialized tasks is often limited by the avail…
UrbanMind: Towards Urban General Intelligence via Tool-Enhanced Retrieval-Augmented Generation and Multilevel Optimization
Kai Yang, Zelin Zhu, Chengtao Jian +4
Urban general intelligence (UGI) refers to the capacity of AI systems to autonomously perceive, reason, and act within dynamic and complex urban environments. In this paper, we int…