Cognitive impairment progressively limits independent living, yet scalable methods for identifying individuals at future risk remain limited. In the 2026 George B. Moody PhysioNet Challenge, our team (Matcha) developed a hierarchical framework for predicting cognitive impairment from overnight polysomnography (PSG). Modality-specific convolutional stems learned a 192-dimensional representation for every 30-s epoch from human and automated sleep annotations together with cross-view self-supervision. Local and full-night Transformers then aggregated the epoch sequence into a patient-level prediction, while a low-capacity record-wise residual represented whole-night sleep summaries and outcome-observation timing. In the three-site leave-one-site-out (LOSO) validation, it achieved a macro age-conditioned AUROC of 0.804. On the official phase hidden validation set, the best Small- and Large-track entries achieved 0.847 and 0.844, respectively, ranking 1st out of 513 entries.