mlsanityData to 14 Oct 2026

Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining

arXiv 2502.19534 ↗First posted 26 Feb 2025cs.LG cs.AI cs.CRcited by 1

On the map: Retrieval-augmented generation → Privacy and security of RAG

Abstract (arXiv)

We propose a novel mechanism for real-time (human-in-the-loop) feedback focused on false positive reduction to enhance anomaly detection models. It was designed for the lightweight deployment of a behavioral network anomaly detection model. This methodology is easily integrable to similar domains that require a premium on throughput while maintaining high precision. In this paper, we introduce Retrieval Augmented Anomaly Detection, a novel method taking inspiration from Retrieval Augmented Generation. Human annotated examples are sent to a vector store, which can modify model outputs on the very next processed batch for model inference. To demonstrate the generalization of this technique, we benchmarked several different model architectures and multiple data modalities, including images, text, and graph-based data.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 23 references that are in the map. First posted on arXiv 26 Feb 2025.

Builds on

13 of 23 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023RAGLog: Log Anomaly Detection using Retrieval Augmented Generation9 Nov

Other references · 12

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  3. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  4. 2016Bag of Tricks for Efficient Text Classificationcited by 876
  5. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  6. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 510
  7. 2014Deep metric learning using Triplet networkcited by 422
  8. 2019Label Propagation for Deep Semi-supervised Learningcited by 289
  9. 2020An Overview of Deep Semi-Supervised Learningcited by 121
  10. 2019Fast and Accurate Network Embeddings via Very Sparse Random Projectioncited by 29
  11. 2022Anomaly detection optimization using big data and deep learning to reduce false-positivecited by 4
  12. 2023Foundational Models for Malware Embeddings Using Spatio-Temporal Parallel Convolutional Networkscited by 1

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Seeing the Unseen: Towards Training-Free Inspection for Wind Turbine Blades Using Knowledge-Augmented Vision Language Modelscited by 0

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Close in wording, no citation either way

  1. 2026RAID: Retrieval-Augmented Anomaly Detection
  2. 2024Retrieval Augmented Deep Anomaly Detection for Tabular Data
  3. 2025RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration
  4. 2020Improve black-box sequential anomaly detector relevancy with limited user feedback
  5. 2025ADNet: A Large-Scale and Extensible Multi-Domain Benchmark for Anomaly Detection Across 380 Real-World Categories
  6. 2024Advancing Pre-trained Teacher: Towards Robust Feature Discrepancy for Anomaly Detection
  7. 2026MRAD: Zero-Shot Anomaly Detection with Memory-Driven Retrieval
  8. 2025LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning