Value-Twisted Sequential Monte Carlo for Task-Aware Missing-Modality Connectome Completion

Completing a missing modality in the field of medical imaging data could create a Prior–Utility Dilemma: samples that faithfully follow a conditional generative prior may not help prediction, while samples aggressively optimized for a classifier may no longer represent that prior. Our goal is to make diffusion-generated connectomes more useful downstream while keeping their sampling distribution explicit and auditable. We introduce Value-Twisted Sequential Monte Carlo (VT-SMC), a task-aware inference procedure for missing-connectome completion. To resolve this dilemma, VT-SMC formulates task-aware connectome completion as inference under an explicit utility-tilted generative distribution. It combines a conditional diffusion prior with frozen uncertainty-aware task feedback, then uses corrected particle inference to favor diagnostically useful connectomes without losing distributional accountability. This unified formulation balances anatomical plausibility, downstream utility, and subject-specific uncertainty, while admitting a consistent SMC estimator and a gradient-free variant. Sufficient experiments show that the method manages, rather than eliminates the fidelity–utility trade-off.