EASE: Effect-Aware Representation Editing for Missing-Modality Prediction

Missing modalities typically result in irreversible information loss; consequently, recovering performance as much as possible under these conditions is a compelling research problem. Existing methods address this issue by reconstructing or retrieving missing information, transferring knowledge from modality-complete models, or adapting models to available inputs. These strategies encompass feature alignment, modality experts, adaptive fusion, and prompt learning. However, we aim to explore this from a novel perspective: what impact does a missing modality have on the model's decision-making, and can we restore model performance by recovering that impact? Across 12 settings involving various models and datasets, we find that low-dimensional projections of natural representation shifts between complete and incomplete inputs can substantially preserve the score effects of the unprojected shifts under replay on held-out samples, even when retaining only a small fraction of their hidden-state energy. We propose (Effect-Aware Subspace Editing), a lightweight editor that learns sample-specific corrections from available inputs within a fixed subspace derived from paired complete–incomplete training representations, while keeping the backbone frozen. In this way, we achieve state-of-the-art (SOTA) results on the IEMOCAP and CMU-MOSEI datasets.