Probing Spatial State Representations in Visual Foundation Models
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper asks whether visual foundation models provide representations that support a persistent spatial state. Using frozen-feature probes on ten representations under streaming egocentric observations of static indoor environments, it evaluates four capability levels: spatial structure, object persistence, predictive spatial update, and actionability. Capability profiles vary across tasks, layers, and prefix lengths. Strong readout of the visible scene does not ensure accurate object persistence or future-feature retrieval. Predicted actions respond to goal changes, but supplying the correct goal does not reliably improve progress.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper asks whether visual foundation models provide representations that support a persistent spatial state. Using frozen-feature probes on ten representations under streaming egocentric observations of static indoor environments, it evaluates four capability levels: spatial structure, object persistence, predictive spatial update, and actionability. Capability profiles vary across tasks, layers, and prefix lengths. Strong readout of the visible scene does not ensure accurate object persistence or future-feature retrieval. Predicted actions respond to goal changes, but supplying the correct goal does not reliably improve progress.
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- 04What could mislead?
The evaluation covers static indoor environments and ten representations; results may not generalize to dynamic scenes or other models. Source claims not independently checked.
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Key findings from the stored brief
- Strong readout of the visible scene does not ensure accurate object persistence or future-feature retrieval.
- Native future prediction retains recoverable spatial structure, but a spatial reader trained on real features does not transfer unchanged to predicted features.
- Predicted actions respond to goal changes, yet supplying the correct goal does not reliably improve progress.
The evaluation covers static indoor environments and ten representations; results may not generalize to dynamic scenes or other models. Source claims not independently checked.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
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Why this may matter
The findings suggest spatial representations should be evaluated not only by what can be decoded, but by whether information persists beyond observation, is updated predictively, and supports goal-directed action.
Limits and open questions
The evaluation covers static indoor environments and ten representations; results may not generalize to dynamic scenes or other models. Source claims not independently checked.
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