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From the 1 of 7 linked papers with an AI index.

collaborators

7 papers

cs.DC2026

Don't Predict, Prioritize: Rethinking GPU Reliability Assessment

Difeng Ma, Changhua Pei, Yuanwei Lu +7

The paper proposes HeaRank, a learning-to-rank framework that ranks GPU nodes by their relative failure risk instead of predicting exact failure times, showing improved detection o…

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.LG2025

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…

cs.AI2025

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…