微型但可信：高效視覺-語言推理應用於時序異常檢測

摘要

近年来，视觉语言模型（VLM）在众多任务上取得了显著进展，但先前研究表明，将大型语言或多模态模型应用于时序数据中的异常模式发现时，其性能表现仍不尽人意。公开的异常检测基准通常提供区间标注，却缺乏自然语言解释，这使得微调VLM以生成基于证据的可解释决策变得困难。为弥补这一空白，我们构建了VisAnomBench——一个基于公开时间序列数据集精心整理的基准，并通过使用细粒度、任务特定的奖励机制从多个大型VLM中筛选高质量异常解释进行增强。在此基准上微调后，我们开发了VisAnomReasoner，一种面向时间序列异常检测的参数高效型VLM。在VisAnomBench上的实验结果表明，VisAnomReasoner能够实现更精确的异常定位，并在所有基线方法中持续领先，在精确率和F1分数上分别至少提升21.23和23.87个百分点。此外，在TSB-AD-U基准上的额外实验展示了其强大的跨基准泛化能力，VisAnomReasoner在精确率和F1分数上分别提升了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.