If Inputs Conflict, When Does Learning Help?

A clear image and a clear audio clip can tell different stories. If one of them belongs to another example, confidence alone cannot tell a multimodal model which story to follow. Learning a correction is an appealing response, but when does it help? We answer this question with a controlled replacement protocol and one default-preserving method, ConflictCalibrated Evidential Fusion (CEF). CEF keeps the average of source logits as a transparent default, proposes alternative predictions, and accepts one only when a cross-fitted selector predicts a rescue rather than a harm. The method is a measuring instrument as much as a competitor: it lets us score, example by example, when overriding a strong default pays. Three findings trace its benefits and limits. When three of five PolyMNIST views remain target-consistent, coordinate-wise median reaches 97.3% on the official test split, outperforming frozen CEF at 93.6%. With two conflicting sources there is no majority to follow: on AV–MNIST, CEF reaches 54.4% donor accuracy on validation against 48.1% for the best simple rule, and on a reserved test split it raises the default from 48.1% to 55.8% while clean accuracy stays at 96.8%. Finally, a correct answer can be available yet out of reach: with the candidate pool fixed, a selector trained on two replacements reaches only 6.4% on three despite a 99.8% candidate oracle, and retraining on three raises it to 84.3% at a cost of 1.3 points in clean accuracy. The value of learned correction therefore depends on the remaining redundancy, the availability of a correct candidate, and whether the selector generalizes to the corruption regime.