This project explores architectural materiality through the lens of machine perception. By training a model to classify material compositions across different spatial conditions, the project reframes material understanding as a diagrammatic and spatial process rather than a fixed attribute. The resulting forms and organizational systems emerge from the model’s internal logic, revealing how materiality, contrast, and spatial depth are abstracted and reassembled by a non-human perceptual system.