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Wichita State University

Academic institutionnorthamerica · us
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Research library28linked papers
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Selected work

Representative Papers

AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection

Aug 12, 2026

This work addresses the challenge of interpreting anomaly detection results in digital twin systems, where the complexity and volume of sensor data often hinder operator comprehension. To this end, it proposes the first explainable anomaly analysis framework that integrates embodied agent architecture with digital twins, enabling natural language querying and generating interpretable diagnostics through structured agent collaboration, knowledge-anchored reasoning, and context-aware retrieval. The study innovatively introduces synthetic anomaly injection and query generation mechanisms to establish a rigorous benchmark for evaluation and demonstrates the feasibility of deploying lightweight, open-source large language models in real-world cyber-physical systems. Experimental results on a meteorological sensor dataset show that the proposed approach significantly outperforms baseline methods in diagnostic quality, retrieval accuracy, and the effectiveness of mitigation recommendations.

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Latest Papers

AgenticTwin: An Agentic LLM Framework Integrated with Digital Twin for Anomaly Detection

Aug 12, 2026

This work addresses the challenge of interpreting anomaly detection results in digital twin systems, where the complexity and volume of sensor data often hinder operator comprehension. To this end, it proposes the first explainable anomaly analysis framework that integrates embodied agent architecture with digital twins, enabling natural language querying and generating interpretable diagnostics through structured agent collaboration, knowledge-anchored reasoning, and context-aware retrieval. The study innovatively introduces synthetic anomaly injection and query generation mechanisms to establish a rigorous benchmark for evaluation and demonstrates the feasibility of deploying lightweight, open-source large language models in real-world cyber-physical systems. Experimental results on a meteorological sensor dataset show that the proposed approach significantly outperforms baseline methods in diagnostic quality, retrieval accuracy, and the effectiveness of mitigation recommendations.

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