The field of anomaly detection and industrial maintenance is moving towards more integrated and automated solutions. Recent developments have focused on leveraging multimodal large language models (MLLMs) and knowledge-infused frameworks to enhance inspection performance and provide actionable maintenance recommendations. These approaches aim to address the limitations of traditional anomaly detection methods and bridge the gap between condition monitoring and maintenance planning. Notable advancements include the development of plug-and-play frameworks, multi-stage deliberative reasoning processes, and fine-grained reward mechanisms. These innovations have shown significant performance improvements in adapting general vision-language models to specialized anomaly detection tasks. Noteworthy papers include: ADSeeker, which proposes a knowledge-infused framework for anomaly detection and reasoning, achieving state-of-the-art zero-shot performance on several benchmark datasets. AD-FM, which introduces a multi-stage deliberative reasoning process and fine-grained reward mechanism, demonstrating substantial performance improvements in adapting general vision-language models to specialized anomaly detection. PARAM, which presents an integrated LLM-based intelligent system for prescriptive maintenance, providing actionable maintenance recommendations and advancing the application of LLMs in industrial maintenance. AutoIAD, which introduces a multi-agent collaboration framework for automated industrial anomaly detection, significantly outperforming existing general-purpose agentic collaboration frameworks and traditional AutoML frameworks.