Imagine an AI assistant that can effortlessly answer complex questions by piecing together information from multiple sources, even when the information changes over time. This isn't science fiction, it's the promise of multi-hop question answering (QA). Traditional AI struggles with these tasks, often getting tripped up by outdated or irrelevant data. A new research paper proposes a solution: the "review-then-refine" framework. This innovative approach allows AI to dynamically adapt to temporal changes in information, essentially giving it a real-time understanding of evolving knowledge. It works by first breaking down a complex question into smaller, manageable sub-queries. Then, it dynamically rewrites these sub-queries based on the information gathered at each step, ensuring the AI stays focused on the most relevant data. The 'review' stage assesses whether the AI needs to search for external information or if it can rely on its existing knowledge. This minimizes the chances of the AI 'hallucinating' or fabricating answers. Finally, the 'refine' phase integrates all the gathered information, both internal and external, to produce a coherent and accurate answer. Tests on several challenging datasets, including those with constantly updating information, show that this new framework significantly outperforms existing methods. This breakthrough could revolutionize how we interact with AI, enabling more accurate and reliable question answering across various applications, from search engines to customer service bots. However, challenges remain, particularly in handling very long reasoning chains and ensuring accuracy in the face of conflicting or biased information. The future of multi-hop QA lies in tackling these challenges, paving the way for even more sophisticated and adaptable AI assistants that can truly understand and respond to the complexities of our world.