With the rapid and transformative advances of large language models (LLMs) in natural language processing, the capabilities of these models in knowledge integration and reasoning have opened new technological pathways for intelligent industrial applications. This review systematically surveys key adaptation techniques, representative application scenarios, and future development trends of LLMs in industrial scenarios. It also provides an integrated overview of their application paradigms and technical characteristics across core industrial processes. Key adaptation techniques for industrial scenarios are first analyzed, including prompt engineering, retrieval-augmented generation (RAG), and parameter-efficient fine-tuning, together with a summary of commonly used evaluation metrics and LLM-based assessment approaches. Representative practices of LLMs are then systematically reviewed across the product lifecycle, covering product design, process planning, production and manufacturing, as well as operation and maintenance. The effectiveness of LLMs in addressing practical industrial problems, facilitating technological innovation, and improving application performance is examined. Finally, major challenges currently encountered in industrial applications of LLMs are identified, including the scarcity of high-quality datasets, limited multimodal fusion capability, insufficient domain specificity, reliability concerns, constrained interpretability, and the lack of standardized evaluation frameworks. Corresponding future research directions are outlined, such as the development of data augmentation and secure sharing mechanisms, the exploration of novel model architectures, and the establishment of intelligent evaluation systems. Overall, this review provides a comprehensive reference for systematic investigations of LLM applications across the entire industrial process and offers theoretical foundations and methodological guidance for both academic research and engineering practice.

Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects. In this work, we present UniAD that accomplishes anomaly detection for multiple classes with a unified framework. Under such a challenging setting, popular reconstruction networks may fall into an "identical shortcut", where both normal and anomalous samples can be well recovered, and hence fail to spot outliers. To tackle this obstacle, we make three improvements. First, we revisit the formulations of fully-connected layer, convolutional layer, as well as attention layer, and confirm the important role of query embedding (i.e., within attention layer) in preventing the network from learning the shortcut. We therefore come up with a layer-wise query decoder to help model the multi-class distribution. Second, we employ a neighbor masked attention module to further avoid the information leak from the input feature to the reconstructed output feature. Third, we propose a feature jittering strategy that urges the model to recover the correct message even with noisy inputs. We evaluate our algorithm on MVTec-AD and CIFAR-10 datasets, where we surpass the state-of-the-art alternatives by a sufficiently large margin. For example, when learning a unified model for 15 categories in MVTec-AD, we surpass the second competitor on the tasks of both anomaly detection (from 88.1% to 96.5%) and anomaly localization (from 89.5% to 96.8%). Code is available at https://github.com/zhiyuanyou/UniAD.

3D shape feature learning plays a pivotal role in both industry and academia. PointCNN is one of excellent neural networks for 3D object databases classification. Instead of selecting representative points arbitrarily in PointCNN, clustering-enhanced PointCNN proposed in this paper can make representative points more logical and efficient for point cloud classification learning. The proposed clustering-based selection approach is able to distinguish more features and catch more details from 3D shapes. Both K-Means and Gaussian-Mixture-Model (GMM) clustering methods are applied during the point selection period. Both methods have been tested on several public data sets, which substantiates the superior classification accuracy with comparable training time.