Visual foundation models excel at recognizing visible scenes but struggle with spatial persistence, predictive updates, and action guidance—capabilities essential for embodied AI. Strong readout accuracy doesn't transfer to maintaining spatial state across time or motion.
This paper investigates whether visual foundation models learn spatial representations that support persistent spatial awareness—tracking objects beyond the current view, predicting future scenes, and guiding actions.