Visual generators need to learn *when* to search for external knowledge, not just *how* to use it—and this knowledge boundary is discoverable through co-training, not fixed in advance.
This paper identifies a critical gap in visual generators: they confidently create incorrect images for requests about new entities, trending topics, and post-training events. The authors show that naive search-augmentation fails because generators have an evolving 'knowledge boundary'—a threshold between what they learned and what needs external context.