Imagine asking an AI, "Did the person laugh after taking a picture?" and not only getting an answer but also seeing the precise moment in the video that supports it. This is the exciting frontier of Video Question Answering (VideoQA), where researchers are pushing AI to go beyond simply recognizing objects and actions to actually understand and reason about events within videos. Traditionally, VideoQA models have been "black boxes," spitting out answers without revealing their logic. This makes it difficult to trust their reasoning, especially with complex questions involving multiple steps and temporal relationships. A new research paper introduces a clever framework called "Align and Aggregate" (VA3) aimed at cracking open this black box. The core idea is to break down complex questions into smaller, manageable parts (sub-questions) and then combine the answers intelligently. Think of it as a detective piecing together clues to solve a case. First, a "video aligner" acts like a spotlight, pinpointing the most relevant video segments for each sub-question. Then, an "answer aggregator" steps in, combining the answers to these sub-questions based on the relationships between them, ensuring a consistent and logical flow of reasoning. This process not only improves the accuracy of answers but also makes the AI's thinking transparent. For example, the AI might first locate the person taking a picture, then identify the moment of laughter, and finally determine if the laughter followed the picture-taking. All of this is shown visually, allowing us to understand exactly how the AI arrived at its conclusion. This research also proposes new, more robust metrics for evaluating the "compositional consistency" of VideoQA models. This essentially measures how well the AI can reason through multi-step questions. Early experiments are promising, showing substantial gains in both accuracy and consistency on challenging benchmark datasets. The "Align and Aggregate" framework is a significant step toward making AI video understanding more transparent and reliable. This opens doors to exciting applications in areas like video search, interactive entertainment, and even scientific analysis. Imagine searching for specific moments in vast video libraries or having personalized video summaries generated on the fly. As research progresses, we can expect even more intelligent and insightful interactions with video content, unlocking a deeper understanding of the world around us.