Abstract In the context of multi-scenario recommendation, multi-scenario click-through-rate (MS-CTR) prediction plays a crucial role in effectively personalizing recommendations on commercial platforms. However, when confronted with multi-scenario data, models often face challenges such as overfitting, inadequate feature representation, and unstable optimization, which hinder the performance and reliability of CTR prediction. To tackle these challenges, this study introduces the enhanced scene-aware transformer (ESAT) framework for MS-CTR prediction. This framework is divided into five modules. First, the structural position-aware scene encoding module converts scene attributes into fixed-dimensional embedding vectors and the scene adaptive transformation module uses a nonlinear transformation to dynamically adjust scene features. The cross-scene regularization module uses multi-sample dropout technology to enhance generalization ability and prevent overfitting. The scene-aware discriminative learning module applies contrastive learning to optimize the similarity between samples. Finally, the hierarchical stability control module introduces ClippyGrad optimizer and $L_\infty $ regularization to accurately control gradient updates, avoid excessive steps, and improve training stability. Experiments conducted on large-scale multi-scenario datasets confirm the effectiveness of the proposed module. The results indicate that the ESAT model substantially elevates performance in MS-CTR prediction tasks, especially in terms of generalization to new scenes, precision in feature representation, and stability in parameter updates.

Industrial Anomaly Detection (IAD) is critical for ensuring product quality by identifying defects. Traditional methods such as feature embedding and reconstruction-based approaches require large datasets and struggle with scalability. Existing vision-language models (VLMs) and Multimodal Large Language Models (MLLMs) address some limitations but rely on mask annotations, leading to high implementation costs and false positives. Additionally, industrial datasets like MVTec-AD and VisA suffer from severe class imbalance, with defect samples constituting only 23.8 % and 11.1 % of total data respectively. To address these challenges, we propose a reward function that dynamically prioritizes rare defect patterns during training to handle class imbalance. We also introduce a mask-free reasoning framework using Chain of Thought (CoT) and Group Relative Policy Optimization (GRPO) mechanisms, enabling anomaly detection directly from raw images without annotated masks. This approach generates interpretable step-by-step explanations for defect localization. Our method achieves state-of-the-art performance, outperforming prior approaches by 36% in accuracy on MVTec-AD and 16% on VisA. By eliminating mask dependency and reducing costs while providing explainable outputs, this work advances industrial anomaly detection and supports scalable quality control in manufacturing.