Autoregressive Multimodal Anomaly Detection with Large Language Models for Microservice Systems
Read the original on arXiv AI →The paper introduces LLMMAD, an autoregressive multimodal anomaly detection framework that leverages Large Language Models for microservice systems. It addresses two key limitations of existing methods—ignoring modality-specific traits and the identity-shortcut problem—by embedding multimodal data into a unified sequence for LLM processing while preserving modality-specific information through dedicated reconstruction heads, and by using a prediction-driven autoregressive objective to avoid copying inputs. Experiments on three real-world datasets show LLMMAD outperforms state-of-the-art approaches, improving F1-scores by up to 3.1%.
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