MCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval
The framework addresses modality shortcut learning in multimodal large language models by enforcing structural relationships between composed and unimodal representations. It introduces two objectives—preference loss and composition regularization—to ensure models leverage complementary signals from multiple modalities instead of relying on a single dominant signal, leading to improved robustness under distribution shifts.