mlsanityData to 14 Oct 2026

RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration

arXiv 2503.02800 ↗First posted 4 Mar 2025cs.LG cs.CEcited by 4

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Abstract (arXiv)

Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictive maintenance (PdM) in such settings demands methodologies that are adaptive, transferable, and capable of integrating domain-specific knowledge. In this paper, we present RAAD-LLM, a novel framework for adaptive anomaly detection, leveraging large language models (LLMs) integrated with Retrieval-Augmented Generation (RAG). This approach addresses the aforementioned PdM challenges. By effectively utilizing domain-specific knowledge, RAAD-LLM enhances the detection of anomalies in time series data without requiring fine-tuning on specific datasets. The framework's adaptability mechanism enables it to adjust its understanding of normal operating conditions dynamically, thus increasing detection accuracy. We validate this methodology through a real-world application for a plastics manufacturing plant and the Skoltech Anomaly Benchmark (SKAB). Results show significant improvements over our previous model with an accuracy increase from 70.7% to 88.6% on the real-world dataset. By allowing for the enriching of input series data with semantics, RAAD-LLM incorporates multimodal capabilities that facilitate more collaborative decision-making between the model and plant operators. Overall, our findings support RAAD-LLM's ability to revolutionize anomaly detection methodologies in PdM, potentially leading to a paradigm shift in how anomaly detection is implemented across various industries.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 24 of its 55 references that are in the map. First posted on arXiv 4 Mar 2025.

Builds on

24 of 55 references.

References · 24

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12 more
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  12. 2024AAD-LLM: Adaptive Anomaly Detection Using Large Language Modelscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025OMNISEC: LLM-Driven Provenance-based Intrusion Detection via Retrieval-Augmented Behavior Promptingcited by 3
  2. 2025Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Modelscited by 3
  3. 2025Human-Centered LLM-Agent System for Detecting Anomalous Digital Asset Transactionscited by 1
  4. 2026LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanationcited by 0

Similar, not cited

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  1. 2025PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environments
  2. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
  3. 2025IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection
  4. 2026M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection
  5. 2025Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?
  6. 2026ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution
  7. 2025Refining Time Series Anomaly Detectors using Large Language Models
  8. 2025CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series