CoMoR: A Conflict-Aware Modality Routing Framework for Multimodal Learning

Multimodal learning combines complementary evidence from heterogeneous sources, but high confidence in each modality's prediction does not guarantee agreement across modalities. Therefore, a general challenge is to determine how modality-specific judgments should influence joint prediction. We propose CoMoR, a Conflict-Aware Modality Routing framework. The framework uses these judgments, their disagreement, and uncertainty to guide multimodal integration. CoMoR does not combine diagnostic predictions into a final answer. It uses them to adjust the audio-visual inputs, which a frozen large language model then processes together with the original text to predict sentiment. We evaluate CoMoR on four widely used English and Chinese sentiment benchmarks. Ablations on CMU-MOSI and CH-SIMS support the use of diagnostic information and sample-specific routing. Among the methods compared, CoMoR obtains the lowest mean absolute error on all four datasets and leads on most reported metrics, with competitive results on the remaining ones.