equipped to do that, right? Obviously, language model are very powerful. They are good reasoning interfaces. They are increasingly multimodal, as well. But, the supporting stack, right, around that is very um I'd say limited, and that create three problem. Number one, is wrong context, right? So, video is not naturally a sequence of text token. If we force it into that sequence by sampling frames, by extracting a transcript, uh by dumping everything into a prompt, you lose the spatiotemporal relationships, right, that actually define the event. Second, uh wrong memory. So, if you think about text system memory here is often mean retrieval and generation, vector search, or probably like larger context window. Uh those are very useful, but video memory has a different requirement. It needs to link today's scene for something that happened in another file, another episode, another camera angle, another season, another year. So, it actually need durable continuity, And the last part here is wrong reasoning. As I said, you know, text-first system cannot reason over, you know, um, natively over motion, causality, all that. So, uh you know, they do not automatically build like a persistent structure uh on, you know, who appear, what happened, what changes, etc. And so, my argument is that video intelligence need a memory layer that decide what to preserve, how to connect it, and how to retrieve later.