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 路 馃寪 Project Page 路 馃捇 Code
馃 VisAnomBench 路 馃 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.
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
- Project Page: plan-lab.github.io/projects/VisAnom
- Code: github.com/PLAN-Lab/VisAnom
- Dataset: PLAN-Lab/VisAnomBench
License
Subject to the Qwen Research License (non-commercial use only). Built with Qwen.
Citation
If you find VisAnomReasoner useful in your research, please consider citing our work:
@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}
}