  Recently, multimodal prompting, which introduces learnable missing-aware
prompts for all missing modality cases, has exhibited impressive performance.
However, it encounters two critical issues: 1) The number of prompts grows
exponentially as the number of modalities increases; and 2) It lacks robustness
in scenarios with different missing modality settings between training and
inference. In this paper, we propose a simple yet effective prompt design to
address these challenges. Instead of using missing-aware prompts, we utilize
prompts as modality-specific tokens, enabling them to capture the unique
characteristics of each modality. Furthermore, our prompt design leverages
orthogonality between prompts as a key element to learn distinct information
across different modalities and promote diversity in the learned
representations. Extensive experiments demonstrate that our prompt design
enhances both performance and robustness while reducing the number of prompts.
