V-JEPA and the Constitutive Turn: Detection Grammar, Failure Geometry, and the Limits of Representational Governance

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.