Vision Transformers have always worked the same way

Vision Transformers have always worked the same way: look at an image once, produce an output, done. No memory, no second look.
That's starting to change. Recurrent Vision Transformers add an iterative loop to the architecture — the model processes the image, reflects on what it saw, and refines its understanding across multiple passes, carrying memory forward each time.
Instead of one-shot recognition, the model reasons the way we do when we look twice at something ambiguous.
This matters for anything where a single glance isn't enough: occluded faces, cluttered scenes, fine-grained details that only emerge after a second pass. It's a shift from "vision as a snapshot" to "vision as a process."
Coming from my own work on efficient Vision Transformers, this is the kind of architectural idea worth watching — not bigger models, just smarter processing.