| --- |
| license: other |
| license_name: qwen-research |
| license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE |
| language: |
| - en |
| arxiv: 2605.30344 |
| library_name: peft |
| base_model: Qwen/Qwen2.5-VL-3B-Instruct |
| datasets: |
| - PLAN-Lab/VisAnomBench |
| pipeline_tag: image-text-to-text |
| tags: |
| - anomaly-detection |
| - time-series |
| - vision-language |
| - explainability |
| - lora |
| - peft |
| --- |
| |
| # VisAnomReasoner-3B |
|
|
| **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-7B](https://huggingface.co/PLAN-Lab/VisAnomReasoner-7B) |
|
|
| --- |
|
|
| ## Overview |
|
|
| **VisAnomReasoner-3B** is a parameter-efficient vision-language model for time-series anomaly detection. It is a LoRA adapter for Qwen2.5-VL-3B-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 74.3M trainable parameters on top of Qwen2.5-VL-3B-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. |
|
|
| * **Tighter intervals:** VisAnomReasoner-3B exceeds the overlap score of every baseline by at least 6.26 percentage points. |
|
|
| --- |
|
|
| ## 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) |
|
|
| --- |
|
|
| ## License |
|
|
| Subject to the [Qwen Research License](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE) (non-commercial use only). Built with Qwen. |
|
|
| --- |
|
|
| ## 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} |
| } |
| ``` |
|
|
