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Nianjun Zhou

3 accepted papers

2026

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring.

ICLR 2026poster

We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation models without requiring additional fine-tuning. Our method yields an interpretable anomaly score directly interpretable as a false alarm rate (p-value)…

Cited by 0SourcecodeScholar
2026

AssetOpsBench-Live: Privacy-Aware Online Evaluation of Multi-Agent Performance in Industrial Operations

AAAI 2026technical

Industrial automation increasingly relies on multi-agent AI, yet evaluation remains difficult due to task complexity and data confidentiality. We present AssetOpsBench-Live, a demo of a competition-ready platform for real-time, privacy-preserving evaluation of multi-agent AI in industrial contexts.

Cited by 0SourcePDFScholar
2026

Deployed AI Agents for Industrial Asset Management: CodeReAct Framework for Event Analysis and Work Order Automation

AAAI 2026technical

Maintenance of mission-critical industrial assets is frequently hindered by fragmented data, inconsistent record-keeping, and limited access to analytical expertise, resulting in reactive rather than predictive practices. We present \textit{CodeReAct}, an AI-powered agentic framework deployed in lar

Cited by 0SourcePDFScholar