1 citations · 1 across the 5 of their papers we have counts for
7 papers
Decompose Dynamics Before Learning Dependencies in Spatiotemporal Systems
Ziqi Wang, Daojiang Hu, Cheng Bao +4
Relations in networked spatiotemporal systems are often learned from observations that entangle dynamics governed by different mechanisms, obscuring what evolves locally and how it…
When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling
Ziqi Wang, Yuhao Yang, Zhiwei Ling +2
Recent advances in agent and multi-agent systems have shown strong performance on tool use, reasoning, and collaborative tasks. However, existing benchmarks mostly evaluate task co…
ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality
Wenzhuo Qian, Hailiang Zhao, Ziqi Wang +4
Automated incident management is critical for microservice reliability. While recent unified frameworks leverage multimodal data for joint optimization, they unrealistically assume…
Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection
Zhiwei Ling, Hailiang Zhao, Chao Zhang +8
Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes while preserving data privacy, making it a cornerstone of intelligent serv…
Agentic Services Computing
Shuiguang Deng, Hailiang Zhao, Ziqi Wang +6
Services computing has evolved from Web services and microservices to cloud-native and serverless paradigms. These approaches established mature principles for describing, composin…
Reliable Microservice Tail Latency Prediction via Decoupled Dual-Stream Learning and Gradient Modulation
Wenzhuo Qian, Hailiang Zhao, Jiayi Chen +7
Microservice architectures enable scalable cloud-native applications; however, the distributed nature of these systems complicates the maintenance of strict Service Level Objective…
CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging
Zhiwei Ling, Yachen Chang, Hailiang Zhao +3
Deep neural networks (DNNs) have been widely criticized for their overconfidence when dealing with out-of-distribution (OOD) samples, highlighting the critical need for effective O…