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20232026
most citedRevisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective

4 citations · 17 across the 21 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

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…

cs.DC2026

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…

cs.SE2026

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…

cs.MA2026

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…

cs.AI2026

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…

cs.DB2026

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…