| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| pretty_name: VisAnomBench |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - image-text-to-text |
| tags: |
| - time-series |
| - anomaly-detection |
| - vision-language |
| - explanations |
| --- |
| |
| # VisAnomBench |
|
|
| **Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection** |
|
|
| **Xiaona Zhou, Muntasir Wahed, Tianjiao Yu, Constantin Brif, Ismini Lourentzou** |
|
|
| ### 🎉 Accepted to NeurIPS 2026 |
|
|
| [📄 Paper](https://arxiv.org/abs/2605.30344) · [🌐 Project Page](https://plan-lab.github.io/projects/VisAnom/) · [💻 Code](https://github.com/PLAN-Lab/VisAnom) |
|
|
| [🤗 VisAnomReasoner-3B](https://huggingface.co/PLAN-Lab/VisAnomReasoner-3B) · [🤗 VisAnomReasoner-7B](https://huggingface.co/PLAN-Lab/VisAnomReasoner-7B) |
|
|
| --- |
|
|
| ## Overview |
|
|
| VisAnomBench is an explanation-augmented benchmark for vision-language time-series anomaly reasoning. Each sample pairs a rendered time-series plot with a prompt and ground-truth anomaly intervals. Training samples also include a step-by-step explanation grounded in the plot. The benchmark is used to train and evaluate [VisAnomReasoner-3B](https://huggingface.co/PLAN-Lab/VisAnomReasoner-3B) and [VisAnomReasoner-7B](https://huggingface.co/PLAN-Lab/VisAnomReasoner-7B). |
|
|
| <p align="center"> |
| <img src="VisAnomReasoner_teaser.png" width="100%"> |
| </p> |
|
|
|
|
| Left: Given a time-series plot, VisAnomReasoner, trained on VisAnomBench, locates anomalies with details grounded in the plot. Right: VisAnomReasoner-7B outperforms the strongest baselines on VisAnomBench and TSB-AD-U. |
|
|
| --- |
|
|
| ## Dataset Structure |
|
|
| | Split | Samples | UCR-EGI | GutenTAG | UCR-TSAD | KPI | |
| |---|---:|---:|---:|---:|---:| |
| | train | 2,576 | 1,682 | 563 | 200 | 131 | |
| | test | 740 | 415 | 247 | 49 | 29 | |
|
|
| ### Data Fields |
|
|
| | Field | Description | |
| |---|---| |
| | `image` | Path to the plot image, relative to the root of the [VisAnom code repository](https://github.com/PLAN-Lab/VisAnom), for example `VisAnomBench/VisAnomBench_plot_test/test_0.png`. | |
| | `query` | Prompt given to the model: context about the series, task instructions, and the required output format. | |
| | `resp` | Target response: image description, rationale, step-by-step reasoning, and a final answer of the form `<anomaly>True</anomaly><index>(start, end)</index>` followed by the reasoning in `<think>...</think>`. Empty in the test split. | |
| | `anomalies` | Ground-truth anomaly intervals as inclusive `(start, end)` pairs, for example `(52000, 52620)`. KPI intervals use timestamps, for example `(2016-11-09 03:35:00, 2016-11-09 03:45:00)`. | |
|
|
| ## Download |
|
|
| To use the data with the VisAnom code, run this from the root of the code repository: |
|
|
| ```bash |
| hf download PLAN-Lab/VisAnomBench \ |
| --repo-type dataset \ |
| --include "VisAnomBench_train.json" \ |
| --include "VisAnomBench_test.json" \ |
| --include "VisAnomBench_plot_train/*" \ |
| --include "VisAnomBench_plot_test/*" \ |
| --include "VisAnomBench_train_ts/*" \ |
| --include "VisAnomBench_test_ts/*" \ |
| --local-dir VisAnomBench |
| ``` |
|
|
| ## How the Explanations Were Created |
|
|
| For each time-series segment, four general-purpose VLMs (Grok-4-Fast, LLaMA-4-Maverick, Gemma-3-27B-IT, and Qwen2.5-VL-32B) generated candidate outputs, each with an anomaly decision, predicted intervals, and a reasoning trace. Candidates were ranked by a composite reward that combines anomaly localization accuracy with explanation quality (visual groundedness, axis awareness, and clarity) scored by Qwen2.5-VL-72B, and the highest-scoring candidate was kept as the target response. See the paper for details. |
|
|
| ## Source Datasets |
|
|
| | Source | Reference | License | |
| |---|---|---| |
| | [KPI](https://github.com/NetManAIOps/KPI-Anomaly-Detection) | Xu et al., "Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications," WWW 2018 | MIT | |
| | [GutenTAG](https://github.com/TimeEval/GutenTAG) | Wenig et al., "TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms," PVLDB 2022 | MIT | |
| | [UCR-EGI](https://doi.org/10.5441/002/edbt.2020.09) | Gao, Lin, and Brif, "Ensemble Grammar Induction for Detecting Anomalies in Time Series," EDBT 2020 | See source | |
| | [UCR-TSAD](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/) | Wu and Keogh, "Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress," TKDE 2021 | See source | |
|
|
| The source time series remain subject to the terms of their original sources. Please also cite the source datasets when using VisAnomBench. |
|
|
| ## License |
|
|
| VisAnomBench is released under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/). |
|
|
| ## Citation |
|
|
| ```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} |
| } |
| ``` |
|
|
