小而可信：面向时间序列异常检测的高效视觉-语言推理

摘要

近年来，视觉-语言模型（VLM）虽在诸多任务中取得显著进展，但先前研究表明，将大型语言模型或多模态模型应用于时序数据异常模式检测时，其性能表现不尽如人意。公开的异常检测基准通常仅提供区间标注，缺乏自然语言解释，这使得对VLM进行微调以生成有依据、可解释的决策变得困难。为弥补这一不足，我们构建了VisAnomBench——一个基于公开时序数据集精心整理的基准测试集，并通过基于细粒度、任务特定奖励从多个大型VLM中筛选的高质量异常解释进行增强。在该基准上微调后，我们开发了VisAnomReasoner，一种用于时序异常检测的参数高效型VLM。在VisAnomBench上的实验结果表明，VisAnomReasoner能够实现更准确的异常定位，并在所有基线方法中持续领先，精确率和F1分数分别提升至少21.23和23.87个百分点。在TSB-AD-U基准上的额外实验进一步验证了其跨基准泛化能力，VisAnomReasoner使精确率和F1分数分别提升9.57和13.39个百分点。

English

Recent advances in Vision-Language Models (VLMs) have achieved impressive performance across many tasks, yet prior studies report unsatisfactory performance when applying large language or multimodal models to finding abnormal patterns in sequential data. Public anomaly detection benchmarks typically provide interval annotations but not natural-language rationales, making it difficult to fine-tune VLMs to produce grounded, interpretable decisions. To address this gap, we construct VisAnomBench, a curated benchmark built from public time-series datasets and augmented with high-quality anomaly explanations selected from multiple large VLMs using fine-grained, task-specific rewards. Through fine-tuning on this benchmark, we develop VisAnomReasoner, a parameter-efficient VLM for time-series anomaly detection. Experimental results on VisAnomBench show that VisAnomReasoner achieves more accurate anomaly localization and consistently outperforms all baselines, with improvements of at least 21.23 and 23.87 percentage points in precision and F1, respectively. Additional experiments on the TSB-AD-U benchmark demonstrate strong cross-benchmark generalization, with VisAnomReasoner improving precision and F1 by 9.57 and 13.39 percentage points, respectively.