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 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 da
The rapid advancement of VLMs is pushing researchers to apply them to more complex and specialized tasks like time-series anomaly detection, which has traditionally been challenging for AI. This effort is driven by the need for more interpretable AI decisions in critical applications.
Improving anomaly detection in sequential data with interpretable AI has significant implications for industrial monitoring, cybersecurity, and predictive maintenance across various sectors. The creation of a dedicated benchmark (VisAnomBench) signifies a focused effort to overcome current limitations.
The development of smaller, more efficient, and 'trusted' VLMs that can interpret and ground decisions in time-series data opens new avenues for AI application beyond traditional large-scale models. It also highlights a growing demand for explainable AI in practical settings.
- · Industrial automation sector
- · Cybersecurity industry
- · AI researchers in VLMs
- · Companies with complex sensor data
- · Legacy anomaly detection systems
- · AI models lacking interpretability
- · Sectors reliant on manual anomaly identification
More accurate and interpretable anomaly detection systems become deployed in critical infrastructure.
Reduced operational downtime and enhanced security due to proactive identification of unusual patterns.
The benchmark (VisAnomBench) becomes a standard for evaluating VLM performance in similar specialized domains, accelerating further research and product development.
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Read at arXiv cs.AI