**摘要**
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
👤 作者: Xiaona Zhou, Muntasir Wahed, Tianjiao Yu, Constantin Brif, Ismini Lourentzou

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🔗 **[Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection](https://arxiv.org/abs/2605.30344v1)**

> Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection
🏷️ 来源: ArXiv cs.AI
⏱️ 2026-05-29 14:01