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We study adaptive spatial weighting as a task-level design principle and instantiate it in two adapters. LAW learns per-pixel loss weights for mask-conditioned diffusion by modulating a ratio prior with a feature-dependent delta map, with normalization, clamping, and Dice regularization for stability. ORDER improves lightweight segmentation by adding selective bidirectional skip attention with stage-wise confidence gating. On held-out diffusion test sets, LAW lowers FID from 158.13$\\pm$0.15 to 108.43$\\pm$0.71 on Polyps, from 144.13$\\pm$0.31 to 89.51$\\pm$0.96 on KiTS19, and from 139.22$\\pm$0.38 to 112.58$\\pm$0.68 on BRISC, while improving held-out mask-recovery Dice from 0.681$\\pm$0.013 to 0.825$\\pm$0.003 on Polyps. 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