Um since this is um you know a track on on graph, right? So the the best mental model that I can come up with is to represent you know, video collection as a context graph. So a context graph is a durable, queryable representation that connects video moment, entities, appearances, relationship, time span, metadata, and corpus level context, right? So if you take a look here on on the screen all the way in the bottom, you got time bounded moment. These are like the the scene, the shot, right? Uh these are evidence unit. One one level up are the appearances, where and when each entity show up. And then you got the actual entity itself. So think about the people on the video, the brand, the places, the concept. Next you have relationship, uh co-occurrences to the same brand, sequences between different places, the causality, and timeline. And finally at the top you have corpus level context. What are the main themes, the patterns, the gap, the coverage that this video collection cover, right? Uh this matter because different question travels different part of the graph. If you ask a simple search question, then that might go directly into the moment. But like an entity workflow might start with a person and then it expand into appearances, right? And if you ask question like a storyline, like narrative storytelling of certain you know you know person, then it may follow relationship across time, right? Uh so the key idea here is that memory in the context of video understanding is a navigable structure over the entire video volume. From that concept, I come up with these five principles as a building you know a memory layer for video intelligence. Number one is to ingest once and reason many times. So um you don't want to like do every single query from scratch, right? You want to pay the cost up front, do one interpretation from the video content up up front, pay the cost, and then you move expensive understanding into injection. So this is same mental model uh database, right? You you're not repeatedly have to parse your entire source of data um for you know every application request. Um second principle is to store primitive, not just answer. So, uh, you know, moments, entities, appearances, as we talked about that, those are the the primitives, right? That allows you to, uh, do downstream workflow, like search, editing, um, you know, analytics, all of that. Third is to ground every claim. They like basically, if you ask a question, you need to cite back into where that scene happening in the video. So, uh, evidence, like, you know, should be grounded to a specific timestamp within the video, right? Uh, fourth is to let intent shape memory. Um, this is important because the same footage mean different thing in different workflow. We work across sports, uh, application, brand safety, compliance review, clear analytics. All of them require different primitives from the same video. So, the memory layer should be configurable, right? Developers should, uh, should be able to tell the system what matters. And lastly, uh, keep the layer composable. Um, so, basically, being API first, you know, um, it should provide, uh, the layers that allows those application, uh, on top of that to to serve it, uh, structured grounded metadata that can be plugged into any sort of application.