The field of computer vision and human-computer interaction is moving towards more sophisticated and nuanced understanding of human visual attention and behavior. Researchers are developing innovative methods to leverage gaze data, head movements, and other modalities to improve object detection, action recognition, and human-computer interaction. A key direction is the integration of gaze-aware technologies, which enable more accurate and realistic modeling of human visual attention. Another important area of research is action chunking, which involves modeling multi-step actions to enhance the capabilities of learning from demonstration. Recent advances in this area have focused on improving reactivity, decision consistency, and motion coherence. Noteworthy papers include: Eyes on Target, which proposes a novel gaze-aware object detection framework, and HAGI++, which introduces a multi-modal diffusion-based approach for gaze data imputation. Temporal Action Selection for Action Chunking is also a significant contribution, as it presents a novel algorithm for balanced optimization across reactivity, decision consistency, and motion coherence.