Mode Collapse Dynamics and Their Systemic Risk Implications in Generative Model Pipelines

Abstract

Mode collapse remains a critical failure mechanism in generative model pipelines because it reduces representational diversity while preserving the appearance of valid output quality. This study investigates mode collapse as a pipeline-level systemic risk rather than a single-model training defect. The analysis evaluates collapse trajectories, diversity degradation, stage-wise amplification, operational sensitivity, and mitigation outcomes across a structured generative pipeline. The findings show that collapse intensified progressively across repeated generation rounds, with the collapse score increasing from 0.14 to 0.71, entropy loss rising from 0.11 to 0.68, and pairwise similarity increasing from 0.36 to 0.82. At the same time, mode coverage declined from 0.89 to 0.43, indicating substantial contraction in distributional support. Stratum-level analysis revealed that lexical complexity and style condition were the most vulnerable dimensions, with coverage retention values of 0.39 and 0.42, respectively, while sentiment polarity remained comparatively stable at 0.74. Pipeline amplification analysis showed that cumulative systemic risk increased from 0.52 at the generation stage to 0.88 after retraining, with filtering and ranking contributing the strongest early downstream increments. Sensitivity testing further identified generated data ratio and filtering intensity as the most influential operational parameters, with sensitivity scores of 0.91 and 0.76. Mitigation analysis showed that isolated controls reduced risk only partially, while combined governance reduced systemic risk from 0.88 to 0.38, increased mode coverage from 0.43 to 0.76, and improved novelty retention from 0.39 to 0.72. These results demonstrate that mode collapse should be managed through temporal monitoring, stratum-level diversity auditing, filtering calibration, and retraining quarantine to prevent local diversity loss from becoming persistent systemic instability.