Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection 事件

Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection arXiv:2605.30344v1 Announce Type: new Abstract: 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 rationa