Probing Spatial State Representations in Visual Foundation Models

An observer sees only part of the surrounding world, yet must keep track of what lies beyond the current view. Spatial perception therefore concerns more than recognizing individual images: it calls for a persistent spatial state that can be updated under self-motion and used to guide action. Do visual foundation models provide representations that support such a state? We investigate this question through four levels of spatial capability: spatial structure, object persistence, predictive spatial update, and actionability, using frozen feature probes across ten representations under streaming egocentric observations in static indoor environments. The resulting capability profiles vary across tasks, network layers, and history lengths: strong visible scene readout does not ensure accurate object persistence or future feature retrieval. Matched controls further distinguish readout accuracy from reliance on visual history and motion. Native future prediction retains recoverable spatial structure but changes its interface to a fixed spatial readout. Finally, goal-dependent candidate selection does not establish reliable continuous action utility: in the continuous output setting, predicted actions respond to goal changes, yet using the correct goal does not reliably improve progress. Together, these findings motivate evaluating spatial representations not only by what can be decoded, but by whether that information can be maintained beyond observation, predictively updated, and used for goal-directed action.