4 citations · 17 across the 21 of their papers we have counts for
23 papers
LongRCA Bench: Root-Cause Localization in Long-Horizon Agent Trajectories
Yunfei Zhang, Boyu Feng, Changhua Pei +14
In long agent executions, an early error can persist through later actions and checks, while evidence needed to trace its origin is dispersed across the history. Short histories of…
Don't Predict, Prioritize: Rethinking GPU Reliability Assessment
Difeng Ma, Changhua Pei, Yuanwei Lu +7
The reliability of Graphics Processing Units (GPUs) is a criticalbottleneck for modern large-scale AI infrastructure, where a sin-gle node failure can disrupt synchronous training…
UModel: An Agent-Ready Observability Data Modeling Method at Scale
Changhua Pei, Zheyuan Li, Zexin Wang +10
When networked system failures occur, automatically performing Root Cause Analysis (RCA) using observability data is critical for ensuring networked system reliability. Recently, L…
Agent System Operations: Categorization, Challenges, and Future Directions
Zexin Wang, Changhua Pei, Yuanhao Liu +10
As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional sys…
KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning
Haotian Si, Changhua Pei, Xiao He +9
Driven by the increasingly complex and decision-oriented demands of time series analysis, we introduce the Semantic-Conditional Time Series Reasoning task, which extends convention…
DIVER: A Robust Text-to-SQL System with Dynamic Interactive Value Linking and Evidence Reasoning
Yafeng Nan, Haifeng Sun, Zirui Zhuang +5
In the era of large language models, Text-to-SQL, as a natural language interface for databases, is playing an increasingly important role. The sota Text-to-SQL models have achieve…
TimeSense:Making Large Language Models Proficient in Time-Series Analysis
Zhirui Zhang, Changhua Pei, Tianyi Gao +7
In the time-series domain, an increasing number of works combine text with temporal data to leverage the reasoning capabilities of large language models (LLMs) for various downstre…
ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts
Zexin Wang, Changhua Pei, Yang Liu +8
Web service administrators must ensure the stability of multiple systems by promptly detecting anomalies in Key Performance Indicators (KPIs). Achieving the goal of "train once, in…
TShape: Rescuing Machine Learning Models from Complex Shapelet Anomalies
Hang Cui, Jingjing Li, Haotian Si +4
Time series anomaly detection (TSAD) is critical for maintaining the reliability of modern IT infrastructures, where complex anomalies frequently arise in highly dynamic environmen…
A Survey on AgentOps: Categorization, Challenges, and Future Directions
Zexin Wang, Jingjing Li, Quan Zhou +7
As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional sys…
Mitigating Geospatial Knowledge Hallucination in Large Language Models: Benchmarking and Dynamic Factuality Aligning
Shengyuan Wang, Jie Feng, Tianhui Liu +2
Large language models (LLMs) possess extensive world knowledge, including geospatial knowledge, which has been successfully applied to various geospatial tasks such as mobility pre…
From Time Series Analysis to Question Answering: A Survey in the LLM Era
Wei Li, Zhe Xie, Yuxuan Liang +4
Recently, Large Language Models (LLMs) have introduced a novel paradigm in Time Series Analysis (TSA), leveraging strong language capabilities to support tasks such as forecasting…
CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations
Haotian Si, Changhua Pei, Jianhui Li +2
Recent advances in lightweight time series forecasting models suggest the inherent simplicity of time series forecasting tasks. In this paper, we present CMoS, a super-lightweight…
Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis
Changhua Pei, Zexin Wang, Fengrui Liu +9
In the realm of microservices architecture, the occurrence of frequent incidents necessitates the employment of Root Cause Analysis (RCA) for swift issue resolution. It is common t…
Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
Yaxuan Kong, Yiyuan Yang, Shiyu Wang +7
Understanding time series data is fundamental to many real-world applications. Recent work explores multimodal large language models (MLLMs) to enhance time series understanding wi…
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Quan Zhou, Changhua Pei, Fei Sun +6
Time series anomaly detection (TSAD) underpins real-time monitoring in cloud services and web systems, allowing rapid identification of anomalies to prevent costly failures. Most T…
Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models
Shenglin Zhang, Pengtian Zhu, Minghua Ma +8
Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies…
A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management
Yongqian Sun, Jiaju Wang, Zhengdan Li +9
AIOps algorithms play a crucial role in the maintenance of microservice systems. Many previous benchmarks' performance leaderboard provides valuable guidance for selecting appropri…
LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis
Tianyu Cui, Shiyu Ma, Ziang Chen +10
Log analysis is crucial for ensuring the orderly and stable operation of information systems, particularly in the field of Artificial Intelligence for IT Operations (AIOps). Large…
Failure Diagnosis in Microservice Systems: A Comprehensive Survey and Analysis
Shenglin Zhang, Sibo Xia, Wenzhao Fan +6
Widely adopted for their scalability and flexibility, modern microservice systems present unique failure diagnosis challenges due to their independent deployment and dynamic intera…
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
Zexin Wang, Changhua Pei, Minghua Ma +9
Time series Anomaly Detection (AD) plays a crucial role for web systems. Various web systems rely on time series data to monitor and identify anomalies in real time, as well as to…
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
Haotian Si, Jianhui Li, Changhua Pei +9
Time series anomaly detection (TSAD) has gained significant attention due to its real-world applications to improve the stability of modern software systems. However, there is no e…
OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models
Yuhe Liu, Changhua Pei, Longlong Xu +13
Information Technology (IT) Operations (Ops), particularly Artificial Intelligence for IT Operations (AIOps), is the guarantee for maintaining the orderly and stable operation of e…