Vision-Language Models Should Commit to a Cézannian Specification

Structural visual intelligence in vision-language models cannot be learned through task-level supervision under current architectures. Supervision on tasks whose correct answers require structural competence installs shortcut pathways. These pathways match the answers without building the competence, a pattern identified in the diagnostic literature. The shortcut pathways suppress the relational processes structural reasoning would require, and scaling the supervision strengthens them instead of inducing the intended capability. This paper argues for a complementary research program based on architectural principles under which structural properties emerge from the computation, guided by an explicit specification of those properties. Cézanne's studio investigations provide such a specification of multi-viewpoint relational synthesis, grounded in predictive processing theory. The paper advances three prescriptions. The field should stop treating structural visual capability as a target for task-level supervision, should invest in architectural commitments that make shortcut pathways impossible by construction, and should develop evaluations that distinguish structural representation from shortcut matching. The strongest alternative view is that structural competence will emerge from scaling under sufficiently rich objectives. The mechanistic evidence requires this view to specify how scaling would overcome the shortcut pathway suppression that supervision causes.