SAGE decomposes univariate time-series anomaly detection into four specialized LLM analyzers plus an evidence-grounded detector and supervisor, achieving the highest average performance on three benchmarks while using only normal data for in-context examples.
Large language models can be zero-shot anomaly detectors for time series?CoRR, abs/2405.14755
3 Pith papers cite this work. Polarity classification is still indexing.
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IstGPT combines LLMs for graph extraction with improved GNNs for anomaly detection in industrial cyber-physical systems and reports best F1 and eTaF1 scores across nine datasets versus 12 baselines.
A systematic review of LLM applications in process systems engineering finds genuine utility for natural-language tasks but persistent challenges for real-time execution, constraint satisfaction, and safety guarantees.
citing papers explorer
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Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
SAGE decomposes univariate time-series anomaly detection into four specialized LLM analyzers plus an evidence-grounded detector and supervisor, achieving the highest average performance on three benchmarks while using only normal data for in-context examples.
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IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems
IstGPT combines LLMs for graph extraction with improved GNNs for anomaly detection in industrial cyber-physical systems and reports best F1 and eTaF1 scores across nine datasets versus 12 baselines.
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Large Language Models in Process Systems Engineering: Opportunities, Architectures, and Industrial Deployment Challenges
A systematic review of LLM applications in process systems engineering finds genuine utility for natural-language tasks but persistent challenges for real-time execution, constraint satisfaction, and safety guarantees.