# Knowledge-Guided Textual Reasoning for Explainable Video Anomaly Detection via LLMs

arXiv: https://arxiv.org/abs/2511.07429 · HTML: https://mlsanity.com/paper/2511.07429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)

## Builds on (most cited first, 2 of 2)

- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models](https://mlsanity.com/paper/2409.17146/) (2024)

## Built on by (most cited first, 1 of 1)

- [LightAIR: Lightweight Action Inversion and Riemannian Rectification for Text-based Person Anomaly Search](https://mlsanity.com/paper/2608.09152/) (2026)

## Abstract (arXiv)

We introduce Text-based Explainable Video Anomaly Detection (TbVAD), a language-driven framework for weakly supervised video anomaly detection that performs anomaly detection and explanation entirely within the textual domain. Unlike conventional WSVAD models that rely on explicit visual features, TbVAD represents video semantics through language, enabling interpretable and knowledge-grounded reasoning. The framework operates in three stages: (1) transforming video content into fine-grained captions using a vision-language model, (2) constructing structured knowledge by organizing the captions into four semantic slots (action, object, context, environment), and (3) generating slot-wise explanations that reveal which semantic factors contribute most to the anomaly decision. We evaluate TbVAD on two public benchmarks, UCF-Crime and XD-Violence, demonstrating that textual knowledge reasoning provides interpretable and reliable anomaly detection for real-world surveillance scenarios.