TRUST: Trustworthy Reliability-guided Update Selection for Multimodal Test-time Adaptation

Multimodal test-time adaptation aims to improve model robustness under distribution shifts without source data or target labels, yet real-world deployment often involves dynamic modality missingness, where incomplete or unreliable modalities can degrade predictions and contaminate online adaptation signals. To address this problem, we propose TRUST, a source-free online adaptation framework that selects trustworthy unimodal and fused views for model updates rather than relying only on prediction confidence. TRUST constructs a dynamic anchor from confident and temporally stable views, estimates update reliability using confidence gain, anchor agreement, and stream-level stability, and applies an adaptive update budget for efficient single-backward adaptation. Experiments on egocentric audio-visual recognition and multimodal sentiment analysis show that TRUST consistently improves robustness across missing rates, dynamic missing patterns, and pretrained backbones, while reliability analysis confirms its ability to stabilize adaptation under severe modality missingness.