# InertiaMPDD

InertiaMPDD extends depression screening beyond a single recording-level label to a measurable departure from a healthy reference: a latent predictor of the next state is trained on frozen multimodal representations, and three descriptors are read from its prediction residuals, namely reference deviation, residual decay rate and a Jacobian spectral radius, used either for standalone screening or as an individual correction to a population decision. Across five cohorts from four datasets, the fused pathway reaches 98.41 F1 on D-Vlog and 84.98 F1 on LMVD, while on AVEC2014 the reference descriptors discriminate weakly and fusion reaches 66.67 F1.

InertiaMPDD extends depression screening beyond a single recording-level label to a measurable departure from a healthy reference: a latent predictor of the next state is trained on frozen multimodal representations, and three descriptors are read from its prediction residuals, namely reference deviation, residual decay rate and a Jacobian spectral radius, used either for standalone screening or as an individual correction to a population decision. Across five cohorts from four datasets, the fused pathway reaches 98.41 F1 on D-Vlog and 84.98 F1 on LMVD, while on AVEC2014 the reference descriptors discriminate weakly and fusion reaches 66.67 F1.