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This paper introduces AgenticTwin, a novel framework that integrates large language model (LLM) reasoning with a digital twin-based anomaly detection system to enhance the interpretation of anomalies in cyber-physical environments. By grounding LLM-generated explanations in outputs from a digital twin-driven anomaly classifier, the framework allows operators to interactively query the system in natural language. Experimental results show that this integration significantly improves diagnosis quality and mitigation strategies across various anomaly scenarios, showcasing the potential of LLMs in complex data environments.
Grounding LLM explanations in digital twin outputs boosts anomaly diagnosis quality and operator engagement in cyber-physical systems.
Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems. Even with skilled operators, interpreting anomalies detected within digital twin pipelines is challenging, as the sheer complexity and volume of raw sensor data make thorough analysis difficult. Recent advances in large language models (LLMs) offer promising capabilities for reasoning and explanation, yet their integration into digital twin-driven anomaly analysis remains underexplored. In this work, we propose AgenticTwin, an agentic framework that integrates LLM-driven reasoning with a digital twin-based anomaly detection pipeline. The framework grounds LLM-generated explanations in outputs from a digital twin-driven anomaly classifier and enables human operators to ask relevant natural-language questions about the system. Beyond the framework itself, we introduce a benchmark-oriented evaluation pipeline constructed over synthetic anomalies injected into a real-world weather sensor dataset, enabling controlled generation of operator queries over anomaly events. We further evaluate the feasibility of deploying lightweight, open-source LLMs for practical cyber-physical environments. Experimental results demonstrate that structured agent collaboration and knowledge-grounded reasoning improve diagnosis quality, contextual retrieval, and mitigation quality across diverse possible anomaly scenarios.
