The proposed "spacedepth" model represents a fundamental architectural departure from conventional artificial intelligence (AI) methodologies. Instead of relying on time-based learning mechanisms—such as epochs, iterations, or sequential processing—spacedepth mandates that cognition be treated as an inherent property of dimensional saturation and relational density within the system's latent space. This framework posits that intellectual competence is a volumetric function, dependent on how "full" and structurally coherent the system’s internal fields of meaning have become, rather than the duration of its training.

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