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license: apache-2.0
language:
- en
arxiv: 2605.30344
library_name: peft
base_model: Qwen/Qwen2.5-VL-7B-Instruct
datasets:
- PLAN-Lab/VisAnomBench
pipeline_tag: image-text-to-text
tags:
- anomaly-detection
- time-series
- vision-language
- explainability
- lora
- peft
---
# VisAnomReasoner-7B
**Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection**
**Xiaona Zhou, Muntasir Wahed, Tianjiao Yu, Constantin Brif, Ismini Lourentzou**
### 馃帀 Accepted to NeurIPS 2026
[馃搫 Paper](https://arxiv.org/abs/2605.30344) 路 [馃寪 Project Page](https://plan-lab.github.io/projects/VisAnom/) 路 [馃捇 Code](https://github.com/PLAN-Lab/VisAnom)
[馃 VisAnomBench](https://huggingface.co/datasets/PLAN-Lab/VisAnomBench) 路 [馃 VisAnomReasoner-3B](https://huggingface.co/PLAN-Lab/VisAnomReasoner-3B)
---
## Overview
**VisAnomReasoner-7B** is a parameter-efficient vision-language model for time-series anomaly detection. It is a LoRA adapter for Qwen2.5-VL-7B-Instruct, fine-tuned on **VisAnomBench** to jointly localize anomalies and explain them from time-series plots.
> **Given a time-series plot, VisAnomReasoner locates the anomalous intervals and explains the visual evidence behind each decision.**
<p align="center">
<img src="VisAnomReasoner_teaser.png" width="100%">
</p>
Left: Given a time-series plot, VisAnomReasoner locates anomalies with details grounded in the plot. Right: VisAnomReasoner-7B outperforms the strongest baselines on VisAnomBench and TSB-AD-U.
---
## Key Idea
Public time-series anomaly detection benchmarks typically provide interval annotations but not natural-language rationales, which makes it difficult to fine-tune VLMs to produce grounded, interpretable decisions.
**VisAnomBench** fills this gap by augmenting four public benchmarks with temporally grounded explanations, selected from multiple large VLMs using fine-grained, task-specific rewards. Fine-tuning on this explanation-augmented supervision teaches a compact VLM to couple interval-level localization with step-by-step reasoning grounded in what is visible in the plot.
---
## Highlights
* **Explanation-augmented supervision:** Trained on VisAnomBench, which pairs anomaly intervals with step-by-step explanations for time series from KPI, GutenTAG, UCR-EGI, and UCR-TSAD.
* **Joint localization and explanation:** Outputs anomaly intervals in a parseable format together with a numbered reasoning trace.
* **Parameter-efficient:** A rank-32 LoRA adapter with 95.2M trainable parameters (1.13% of the backbone) on top of Qwen2.5-VL-7B-Instruct.
* **Strong localization:** On VisAnomBench, both VisAnomReasoner variants achieve the highest precision, recall, F1, and overlap against 15 baselines, including general-purpose VLMs, LLM/VLM anomaly detectors, time-series foundation models, and classical detectors. VisAnomReasoner-7B improves overlap by 11 percentage points over its base model, Qwen2.5-VL-7B.
* **Cross-benchmark generalization:** On TSB-AD-U, which contains time series with unseen characteristics and anomaly definitions, VisAnomReasoner-7B improves standard precision and F1 by 9.57 and 13.39 percentage points over the strongest baseline, Qwen2.5-VL-7B.
---
## Resources
* **Paper:** [Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection](https://arxiv.org/abs/2605.30344)
* **Project Page:** [plan-lab.github.io/projects/VisAnom](https://plan-lab.github.io/projects/VisAnom/)
* **Code:** [github.com/PLAN-Lab/VisAnom](https://github.com/PLAN-Lab/VisAnom)
* **Dataset:** [PLAN-Lab/VisAnomBench](https://huggingface.co/datasets/PLAN-Lab/VisAnomBench)
---
## Citation
If you find VisAnomReasoner useful in your research, please consider citing our work:
```bibtex
@inproceedings{zhou2026tiny,
title={Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection},
author={Zhou, Xiaona and Wahed, Muntasir and Yu, Tianjiao and Brif, Constantin and Lourentzou, Ismini},
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
year={2026}
}
```
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