The recent advancements in the field of Question Answering (QA) have primarily focused on enhancing the efficiency and accuracy of models by refining the context and knowledge integration processes. A notable trend is the development of context filtering techniques that leverage reward modeling to distill essential information from retrieved contexts, thereby improving token efficiency and performance in low-resource settings. Additionally, there is a strong emphasis on dynamic knowledge graphs for multi-hop QA, which aim to resolve knowledge conflicts and enhance retrieval accuracy through fine-grained strategies. These methods not only improve the reliability of answers but also adapt to environments with dynamic information. Another significant contribution is the introduction of frameworks that incorporate temporal adaptability, allowing models to better handle time-related information in multi-hop scenarios. These frameworks dynamically rewrite sub-queries and implement adaptive retrieval mechanisms to minimize unnecessary data processing and reduce hallucinations. Overall, the field is moving towards more sophisticated and adaptive models that can efficiently manage and synthesize complex information for improved QA performance.