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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
Published in The Web Conference (WWW), 2024
A frequency-perspective study revisiting VAE for unsupervised time series anomaly detection.
Recommended citation: Zexin Wang, Changhua Pei, Minghua Ma, Xin Wang, Zhihan Li, Dan Pei, Saravan Rajmohan, Dongmei Zhang, Qingwei Lin, Haiming Zhang, Jianhui Li, and Gaogang Xie. Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective. WWW 2024.
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Large Language Models Can Provide Accurate and Interpretable Incident Triage
Published in ISSRE, 2024
A study on using large language models for accurate and interpretable incident triage.
Recommended citation: Zexin Wang, Jianhui Li, Minghua Ma, Ze Li, Yu Kang, Chaoyun Zhang, Chetan Bansal, Murali Chintalapati, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang, Changhua Pei, and Gaogang Xie. Large Language Models Can Provide Accurate and Interpretable Incident Triage. ISSRE 2024.
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ActiveDNS: Is There Room for DNS Optimization Beyond CDNs?
Published in LCN, 2024
A study on DNS optimization beyond CDNs.
Recommended citation: Yue Wang, Changhua Pei, Zexin Wang, Yingqiang Wang, Guo Chen, Yuchao Zhang, Yi Li, Jingjing Li, Jianhui Li, and Gaogang Xie. ActiveDNS: Is There Room for DNS Optimization Beyond CDNs? LCN 2024.
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Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster
Published in International Conference on Network and Parallel Computing (NPC), 2024
An efficient heterogeneous implementation of the LOBPCG eigensolver on a CPU-GPU cluster.
Recommended citation: Yang Liu, Yonghua Zhao, Zexin Wang, Rongfeng Huang, Dingye Zhang, and Xinyin Zhang. Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster. NPC 2024, pp. 65-76.
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Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis
Published in WWW Companion, 2025
An SOP-enhanced LLM-based multi-agent system for root cause analysis.
Recommended citation: Changhua Pei, Zexin Wang, Fengrui Liu, Zeyan Li, Yang Liu, Xiao He, Rong Kang, Tieying Zhang, Jianjun Chen, Jianhui Li, Gaogang Xie, and Dan Pei. Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis. WWW Companion 2025.
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KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning
Published in arXiv, 2026
A framework for unified reasoning over time series and semantic information.
Recommended citation: Haotian Si, Changhua Pei, Xiao He, Zeyan Li, Zhe Xie, Zexin Wang, Jiyao Hu, Zhaoyang Yu, Tieying Zhang, Dan Pei, Jianhui Li, and Gaogang Xie. KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning. arXiv, 2026.
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ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts
Published in The Web Conference (WWW), 2026
A visual time-series anomaly detection framework inspired by how human experts inspect time series.
Recommended citation: Zexin Wang, Changhua Pei, Yang Liu, Hengyue Jiang, Quan Zhou, Haotian Si, Hang Cui, Jianhui Li, Gaogang Xie, Jingjing Li, and Dan Pei. ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts. WWW 2026.
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Smart Eye: LLM-Guided Proposer-Verifier Framework for Industrial-Scale Log Anomaly Detection
Published in The Web Conference (WWW), 2026
An LLM-guided proposer-verifier framework for industrial-scale log anomaly detection.
Recommended citation: Changhua Pei, Hang Cui, Jingjing Li, Yuxuan Li, Zihan Liu, Xinyuan Liao, Cenjie Hu, Jiabao Wang, Zheyuan Li, Zexin Wang, Haotian Si, Ke Xiang, Gaogang Xie, and Dan Pei. Smart Eye: LLM-Guided Proposer-Verifier Framework for Industrial-Scale Log Anomaly Detection. WWW 2026.
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Agent System Operations: Categorization, Challenges, and Future Directions
Published in IEEE Transactions on Software Engineering (TSE), 2026
A survey and categorization of AgentOps for operating agent systems.
Recommended citation: Zexin Wang, Changhua Pei, Yuanhao Liu, Jingjing Li, Yintong Huo, Quan Zhou, Haotian Si, Hang Cui, Zihan Liu, Jianhui Li, Gaogang Xie, Fei Sun, Dan Pei, and David Lo. Agent System Operations: Categorization, Challenges, and Future Directions. IEEE Transactions on Software Engineering, 2026.
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UModel: An Agent-Ready Observability Data Modeling Method at Scale
Published in arXiv, 2026
An agent-ready observability data modeling method for large-scale operation scenarios.
Recommended citation: Changhua Pei, Zheyuan Li, Zexin Wang, Hang Cui, Xiaohui Nie, Qi Zhou, Fang Situ, Cheng Zhang, Xin Zhang, Xidao Wen, Gaogang Xie, Jingjing Li, and Dan Pei. UModel: An Agent-Ready Observability Data Modeling Method at Scale. arXiv, 2026.
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EviReport: From Reasoned Outlines to Evidence Tracked Long-Form Reports
Published in Findings of ACL, 2026
A framework for generating long-form reports from reasoned outlines with evidence tracking.
Recommended citation: Zihan Liu, Jianhui Li, Zexin Wang, Fei Sun, Jingjing Li, Zheyuan Li, Ke Xiang, Hang Cui, Houhua Gong, Changhua Pei, and Gaogang Xie. EviReport: From Reasoned Outlines to Evidence Tracked Long-Form Reports. Findings of ACL 2026.
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Rethinking Time Series Anomaly Detection from a Dynamic Perspective: Temporal-Frequency-Curvature Fusion
Published in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Research Track), 2026
A dynamic time-series anomaly detection framework that fuses temporal, frequency, and curvature information.
Recommended citation: Hang Cui, Zexin Wang, Changhua Pei, Juncheng Hu, Haotian Si, Quan Zhou, Cenjie Hu, Jingjing Li, Dan Pei, and Gaogang Xie. Rethinking Time Series Anomaly Detection from a Dynamic Perspective: Temporal-Frequency-Curvature Fusion. KDD 2026 Research Track.
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TSLoc: Self-Supervised Faulty Node Localization Framework in Large-scale Training Clusters
Published in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Applied Data Science Track), 2026
A self-supervised framework for locating faulty nodes in large-scale training clusters.
Recommended citation: Quan Zhou, Changhua Pei, Yuanwei Lu, Difeng Ma, Zexin Wang, Jianhui Li, Yibo Zhu, Daxin Jiang, Dan Pei, Jingjing Li, and Gaogang Xie. TSLoc: Self-Supervised Faulty Node Localization Framework in Large-scale Training Clusters. KDD 2026 Applied Data Science Track.
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TSRBench: Benchmarking Time-Series Retrieval
Published in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Datasets and Benchmarks Track), 2026
A benchmark for systematically evaluating time-series retrieval methods.
Recommended citation: Cenjie Hu, Hang Cui, Zexin Wang, Juncheng Bao, Jingwen Yang, Jingjing Li, Changhua Pei, Dan Pei, and Gaogang Xie. TSRBench: Benchmarking Time-Series Retrieval. KDD 2026 Datasets and Benchmarks Track.
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Don’t Predict, Prioritize: Rethinking GPU Reliability Assessment
Published in ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Applied Data Science Track), 2026
A priority-oriented approach to GPU reliability assessment for large-scale training clusters.
Recommended citation: Difeng Ma, Changhua Pei, Yuanwei Lu, Quan Zhou, Zexin Wang, Yibo Zhu, Daxin Jiang, Dan Pei, Jingjing Li, and Gaogang Xie. Don't Predict, Prioritize: Rethinking GPU Reliability Assessment. KDD 2026 Applied Data Science Track.
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LongRCA Bench: Diagnosing Responsible Roles and Root Causes in Long-Horizon Agent Failures
Published in arXiv, 2026
A benchmark and training-free method for attributing responsible roles and localizing root-cause steps in long-horizon agent failures.
Recommended citation: Yunfei Zhang, Boyu Feng, Changhua Pei, Zexin Wang, Zhihuang Peng, Xinlong Liu, Hengyue Jiang, Difeng Ma, Jiayi Zhang, Yongzhou Yao, Yanan Zhao, Fei Sun, Yintong Huo, Zhaoyang Liu, Jingjing Li, Gaogang Xie, and Dan Pei. LongRCA Bench: Diagnosing Responsible Roles and Root Causes in Long-Horizon Agent Failures. arXiv, 2026.
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Predict Boldly, Recover Cautiously: Fast On-Router Route Anomaly Prediction and Recovery
Published in ACM SIGCOMM, 2026
Fast on-router route anomaly prediction coupled with cautious recovery.
Recommended citation: Hang Cui, Cenjie Hu, Zexin Wang, Jingjing Li, Juncheng Hu, Dan Pei, Changhua Pei, and Gaogang Xie. Predict Boldly, Recover Cautiously: Fast On-Router Route Anomaly Prediction and Recovery. ACM SIGCOMM 2026, pp. 2253-2255.
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Smart Brain: Semantic Anomaly Detection for Operational Time Series in Large Scale Service Systems
Published in IEEE/ACM International Conference on Automated Software Engineering (Research Track), 2026
Semantic anomaly detection for operational time series in large-scale service systems.
Recommended citation: Hang Cui, Zexin Wang, Jingjing Li, Juncheng Hu, Haotian Si, Cenjie Hu, Quan Zhou, Yongchang Hu, Lei Han, Dan Pei, Changhua Pei, and Gaogang Xie. Smart Brain: Semantic Anomaly Detection for Operational Time Series in Large Scale Service Systems. ASE 2026 Research Track.
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BTSB: Business-Level Time Series Anomaly Detection Benchmark
Published in IEEE International Symposium on Software Reliability Engineering (Research Track), 2026
A benchmark for evaluating business-level time-series anomaly detection methods.
Recommended citation: Hang Cui, Cenjie Hu, Zexin Wang, Juncheng Hu, Haotian Si, Zihan Liu, Yuxuan Li, Jingjing Li, Dan Pei, Changhua Pei, and Gaogang Xie. BTSB: Business-Level Time Series Anomaly Detection Benchmark. ISSRE 2026 Research Track.
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From Benchmark Accuracy to Diagnostic Boundaries: A Time-Aware Replication of DEST
Published in IEEE International Symposium on Software Reliability Engineering (RENE Track), 2026
A time-aware replication study examining the diagnostic boundaries of DEST beyond aggregate benchmark accuracy.
Recommended citation: Hang Cui, Cenjie Hu, Changhua Pei, Juncheng Hu, Haotian Si, Zihan Liu, Ke Xiang, Yuxuan Li, Quan Zhou, Xiaohui Nie, Zexin Wang, Jingjing Li, Dan Pei, and Gaogang Xie. From Benchmark Accuracy to Diagnostic Boundaries: A Time-Aware Replication of DEST. ISSRE 2026 RENE Track.
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Title It Right: Faithful and Attractive Paper Title Generation via Pareto Dominance Alignment
Published in Conference on Empirical Methods in Natural Language Processing (EMNLP), 2026
A Pareto-dominance alignment approach for generating paper titles that are both faithful and attractive.
Recommended citation: Ke Xiang, Yingchao Piao, Zexin Wang, Yufei Hou, Jianhui Li, Jingjing Li, Zihan Liu, Hang Cui, and Changhua Pei. Title It Right: Faithful and Attractive Paper Title Generation via Pareto Dominance Alignment. EMNLP 2026.
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talks
SDG Lingxi Foundation Model for Digital Governance
发布时间：
Invited talk on the SDG Lingxi foundation model and its applications in sustainable development and digital governance.
AI Cluster Operations: Experience and Lessons
发布时间：
Invited keynote on practical experience, challenges, and lessons in operating AI clusters.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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