The field of 3D point cloud analysis is moving towards more efficient and effective methods for semantic segmentation, quantization, and cross-modal alignment. Recent developments have focused on leveraging language guidance, multimodal prompting, and dynamic attention policies to improve model performance and adaptability. Notably, innovative approaches have been proposed to address the challenges of few-shot and zero-shot learning, fine-grained 3D-text alignment, and generative recommendation. These advancements have the potential to significantly impact various applications, including embodied intelligence, recommender systems, and 3D understanding. Noteworthy papers include: EPSegFZ, which presents a novel pre-training-free network for few- and zero-shot 3D point cloud semantic segmentation. 3DAlign-DAER, which introduces a unified framework for fine-grained 3D-text alignment via dynamic attention policy and efficient retrieval strategy. Text2Loc++, which proposes a novel neural network for generalizing 3D point cloud localization from natural language descriptions.