Latent World Recovery for Multimodal Learning with Missing Modalities
For bioscience applications where modalities are often partially available, LWR provides a practical method for robust prediction without requiring imputation or fixed modality sets.
LWR addresses multimodal learning with missing modalities by aligning modality-specific embeddings in a shared latent space and fusing only available modalities, avoiding imputation. It achieves robust performance on cancer phenotype classification and survival prediction using incomplete multi-omics data.
We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made. We propose Latent World Recovery (LWR), a framework built on two key ideas: (i) modality-specific embeddings from different modalities are aligned in a shared latent space, and (ii) a unified representation is constructed by fusing only the embeddings of the modalities that are actually available at both training and inference time. Rather than imputing missing modalities or requiring a fixed modality set, LWR treats each modality as a partial perception of an underlying latent state and performs availability-aware representation learning directly from the observed modalities. This combination of neighbor-based latent alignment and availability-aware modality fusion enables robust multimodal prediction under partial observation, while avoiding error propagation from explicit reconstruction of missing modalities. We evaluate the proposed framework on real-world incomplete multi-omics benchmarks and demonstrate that it provides an effective approach to downstream tasks such as cancer phenotype classification and survival prediction.