שמור ב:
| מחבר ראשי: | |
|---|---|
| פורמט: | Recurso digital |
| שפה: | אנגלית |
| יצא לאור: |
Zenodo
2026
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| נושאים: | |
| גישה מקוונת: | https://doi.org/10.5281/zenodo.18794776 |
| תגים: |
הוספת תג
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
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תוכן הענינים:
- <p>The release of V-JEPA marks a turning point in contemporary machine learning: a shift from representational architectures that emit predictions to constitutive substrates that construct a world-model. This paper examines V-JEPA at the substrate layer, analyzing the structural commitments that emerge from its predictive objective, masking strategy, and training distribution. A detection grammar is applied to surface the privilege envelopes, event-driven separations, and implicit priors governing the system's internal organization. A failure geometry is mapped to show that V-JEPA's breakdowns—boundary collapse, irrecoverable occlusion, temporal misalignment, and coherence-over-truth prioritization—are structured consequences of the substrate's internal geometry rather than representational mispredictions. The paper demonstrates that representational governance frameworks cannot reach the operational layer of world-model substrates and that the field's inherited vocabulary is insufficient for describing the class of architectures now emerging. The analysis concludes by situating V-JEPA among a broader class of substrate-forming architectures and reframing the discourse toward structural organization, coherence geometry, and substrate-level interpretation.</p>