Test-Time Co-Adaptation as Data Assimilation

Test-time adaptation under corruption mainly follows two directions: directly adapting models to corrupted inputs, or restoring inputs before prediction. These two routes are complementary, yet simply combining them does not necessarily outperform model adaptation alone, since corrupted inputs may lose discriminative evidence while restoration can introduce biased or misleading cues. Inspired by data assimilation, we treat the corrupted input as an observation and its diffusion-restored counterpart as a prior estimate, and propose *test-time co-adaptation* (**CoDA**) to address this issue. CoDA regulates cross-view interaction through cross-view gating for reliable updates, selective Jensen–Shannon coupling for consistent samples, and entropy-aware logit fusion for prediction, while leaving the underlying TTA objective unchanged. On ImageNet-C at severity 5, across five TTA methods and five backbone architectures, CoDA improves independent data–model adaptation by 2.8 percentage points on average and up to 4.4 points, reaching **54.7%** accuracy with ZeroSiam. The gains persist under label shift with batch size one and continual adaptation, and extend to natural rendition shifts on ImageNet-R and geometry-aware corruptions on ImageNet-3DCC, showing that CoDA is effective beyond standard 2D corruptions and generalizes to broader natural and structured distribution shifts.