The field of vision-language models and anomaly detection is rapidly advancing, with a focus on improving zero-shot learning capabilities and adapting to diverse datasets and tasks. Researchers are exploring new methods to enhance the performance of vision-language models, such as ensemble learning, cooperative pseudo-labeling, and prompt optimization. Additionally, there is a growing interest in applying these models to industrial anomaly detection, where consistent anomalies and robust performance are crucial. Noteworthy papers in this area include: Cluster-Aware Prompt Ensemble Learning for Few-Shot Vision-Language Model Adaptation, which proposes a novel framework for preserving the cluster nature of context prompts. On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection, which introduces a graph-based algorithm for identifying and filtering consistent anomalies. Enhancing Zero-Shot Anomaly Detection: CLIP-SAM Collaboration with Cascaded Prompts, which proposes a two-stage framework for zero-shot anomaly segmentation tasks. ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?, which presents a novel benchmark for omnidirectional image understanding and introduces a training-free method for enhancing MLLMs' comprehension ability. EPIPTrack: Rethinking Prompt Modeling with Explicit and Implicit Prompts for Multi-Object Tracking, which proposes a unified multimodal vision-language tracking framework. Language as a Label: Zero-Shot Multimodal Classification of Everyday Postures under Data Scarcity, which investigates the influence of prompt design on recognizing visually similar categories.