Finding the right video in a massive online library can be like searching for a needle in a haystack. You type in a few keywords, but the results are often a jumble of close-but-not-quite-right clips. What if search engines could ask you clarifying questions to pinpoint exactly what you’re looking for? Researchers are exploring this idea with MERLIN, a new AI system that refines video search results by engaging in a dynamic Q&A with the user. Imagine searching for a video about "a cat playing." Initial results might show various cat videos. MERLIN, however, goes a step further. It analyzes the top results and asks questions like, "Is the cat playing with a toy?" or "Is the cat indoors or outdoors?" Your answers guide MERLIN to refine its understanding of your search intent, moving the "video in mind" closer to the top of the results. This iterative process mimics how humans refine their searches—we rarely get it perfect on the first try. MERLIN's innovation lies in its ability to learn from this interactive feedback without requiring extensive retraining of its underlying AI models. This efficiency makes it a promising approach for improving search relevance in real-world applications. Current tests on datasets like MSR-VTT, MSVD, and ActivityNet show that MERLIN significantly boosts search accuracy, especially for hard-to-find videos buried deep within search results. While the system shows great potential, there are challenges to overcome. One is the difficulty in understanding temporal information in videos—a fast-paced action sequence versus a slow, scenic shot, for example. Also, current methods rely on still images extracted from the video, missing the full context of motion and time. Despite these limitations, MERLIN offers an exciting glimpse into the future of search technology, moving towards more intelligent and interactive search engines that understand exactly what you mean, even when you can't quite articulate it in a simple search query.