CP-RRA: Clean-Preserving Residual Reliability Adaptation for Robust Multimodal Crisis Classification Under Degraded and Missing Evidence

Reliable and robust multimodal crisis classification is challenged by degraded, corrupted, or missing image–text evidence, because reliability-blind fusion can propagate an unreliable modality and amplify local evidence failure into joint prediction error. To address this problem, we propose Clean-Preserving Residual Reliability Adaptation (CP-RRA), a preserve–diagnose–intervene framework for robust multimodal crisis classification. First, CP-RRA preserves the intact evidence decision pathway as a stable anchor; second, it estimates modality reliability and intervention strength to construct a reliability-aware target representation; finally, it applies bounded residual adaptation between the target and the preserved anchor, with the intervention strength controlling the residual update. Evaluation on a five-class CrisisMMD v2.0 subset covers image and text corruptions, training-unseen perturbations, and complete modality loss. Under the original three-seed protocol, the pre-specified locked CP-RRA configuration (λrel = 1.0) achieves 76.17% worst Weighted-F1; under complete image or text loss, it recovers more than 44 percentage points relative to the preserved pathway. Post-lock Train/Dev ablation shows that removing explicit reliability supervision (λrel = 0) increases worst Weighted-F1 to 79.70%, while substantially weakening reliability diagnostics, revealing a prediction–diagnostic trade-off. Reliability diagnostics further show that the learned scores identify the usable evidence channel. These results indicate that CP-RRA improves continuity and resilience under asymmetric evidence failure while providing auditable signals for crisis information screening, prioritization, and analyst verification.