Latent World Recovery for multimodal learning with missing modalities

arXiv·low signal

This paper (arXiv 2606.12362, Wang, Ren, Butler) studies multimodal learning when some modalities are missing — common in bioscience and other heterogeneous-data settings — by recovering a shared latent world representation. The approach aims to keep multimodal models robust when inputs are incomplete at inference time. Useful background for builders of multimodal agents that must degrade gracefully when a sensor or data stream drops out.