Imagine an AI that can understand a three-minute video, instantly, without any prior training. This is the groundbreaking idea behind VideoINSTA, a new framework tackling the challenge of long-form video understanding. Traditional AI models often struggle with extended videos, getting bogged down in redundant information. VideoINSTA tackles this issue by focusing on the most relevant information within the video. Think of it like a super-efficient detective. Instead of meticulously reviewing every second of footage, VideoINSTA pinpoints the most crucial moments, like a change of scene or a significant action. Using a method called "event-based temporal reasoning", it automatically segments the video into key events, essentially creating a summary of the video's timeline. It then analyzes the spatial relationships of objects in these crucial scenes, supplementing what's happening with *where* it's happening. Finally, VideoINSTA uses an innovative "self-reflection" process, almost like an internal checklist. It continually evaluates its understanding, double-checking for gaps in information and bolstering its confidence before delivering an answer. The results? VideoINSTA significantly outperforms existing state-of-the-art models in long-form video question answering, successfully tackling complex tasks like intent recognition in videos. This advance not only improves the accuracy of AI video understanding but also dramatically reduces the time and resources needed to train these models. VideoINSTA provides a blueprint for a more effective and efficient approach to long video analysis. It sets the stage for the next generation of AI—one that can comprehend and process even hours of video content, unlocking a wealth of information for countless practical applications.