COMPASS: Complete Multimodal Fusion via Proxy Tokens and Shared Spaces for Ubiquitous Sensing

Missing modalities remain a major challenge for multimodal sensing, because most existing methods adapt the fusion process to the observed subset by dropping absent branches, using subset-specific fusion, or reconstructing missing features. As a result, the fusion head often receives an input structure different from the one seen during training, leading to incomplete fusion and degraded cross-modal interaction. We propose COMPASS, a missing-modality fusion framework built on the principle of fusion completeness: the fusion head always receives a fixed N-slot multimodal input, with one token per modality slot. For each missing modality, COMPASS synthesizes a target-specific proxy token from the observed modalities using pairwise source-to-target generators in a shared latent space, and aggregates them into a single replacement token. To make these proxies both representation-compatible and task-informative, we combine proxy alignment, shared-space regularization, and per-proxy discriminative supervision. Experiments on XRF55, MM-Fi, and OctoNet under diverse single- and multiple-missing settings show that COMPASS outperforms prior methods on the large majority of scenarios. Our results suggest that preserving a modality-complete fusion interface is a simple and effective design principle for robust multimodal sensing.