The field of egocentric video understanding is rapidly advancing, with a focus on developing systems that can effectively analyze and interpret first-person video streams. Recent developments have emphasized the importance of multimodal approaches, incorporating both visual and textual cues to improve performance in tasks such as action anticipation and video question answering. Researchers are also exploring the use of reinforcement learning and policy optimization to fine-tune models and enhance their ability to reason about complex scenes. Notably, the integration of hierarchical semantic information and the use of early fusion-based video localization models have shown promising results. Some noteworthy papers in this area include: Multi-RAG, a multimodal retrieval-augmented generation system that achieves superior performance on the MMBench-Video dataset. Reinforcing Video Reasoning with Focused Thinking introduces TW-GRPO, a novel framework that enhances visual reasoning with focused thinking and dense reward granularity, achieving state-of-the-art performance on several video reasoning benchmarks. Multi-level and Multi-modal Action Anticipation presents a novel approach that combines visual and textual cues, while explicitly modeling hierarchical semantic information for more accurate predictions, achieving state-of-the-art results on widely used datasets.