The field of hand-object interaction and dexterous grasping is rapidly advancing, with a focus on developing more realistic and physically plausible models. Researchers are exploring new methods for estimating hand-object poses, generating stable grasps, and enabling zero-shot task-oriented dexterous grasping. Notable developments include the integration of visual and physical cues for pose estimation, the use of force-aware contact modeling for stable grasp generation, and the application of multimodal large language models for zero-shot grasp synthesis. These advancements have the potential to significantly improve the performance and versatility of robotic hands and human-computer interaction systems. Noteworthy papers include: VPHO, which proposes a novel framework for joint visual-physical cue learning and aggregation for hand-object pose estimation, achieving state-of-the-art results in pose accuracy and physical plausibility. ZeroDexGrasp, which enables zero-shot task-oriented dexterous grasp synthesis using prompt-based multi-stage semantic reasoning and contact-guided grasp optimization, demonstrating high-quality grasping on diverse unseen object categories and complex task requirements.