Spatial Reasoning Failures & Diagnostics

Failure Morphologies

COMING SOON* A Diagnostic Taxonomy for Spatial Reasoning Failures in Generative Image Models

Generative image models often produce visually fluent scenes that nonetheless fail in spatial reasoning. This study presents a diagnostic taxonomy and metric suite for identifying seven recurrent morphologies of collapse. Each morphology pairs a visible symptom with a measurable signature when geometric reasoning plateaus or drifts. The resulting field grid and tagging protocol convert aesthetic irregularities into reproducible diagnostics, revealing how image models substitute correlation for causality or abandon volumetric logic under constraint. By integrating metric behavior with morphological observation, this framework bridges computational evaluation and visual analysis, offering a method to read model cognition