BridgeWAM: Does Shared Attention Align Physical World Representations with Actions?

World Action Models (WAMs) commonly couple video and action experts through dense, layer-wise Mixture-of-Transformers (MoT) attention, exposing action prediction to irrelevant visual features while enforcing matched expert depths. This coupling hinders selective visual-feature use and introduces redundant computation. We introduce Bridge-of-Experts (BoE), instantiated as BridgeWAM, which aggregates visual, language, and proprioceptive priors through 32 Latent Bridge Queries (LBQs) into a shared, limited latent space. Joint video–action supervision lets action gradients guide the Video Expert toward representations better suited for action prediction, while the Action Expert learns to selectively retrieve task-relevant information through cross-attention. Decoupling expert depths and reusing LBQs during action denoising yield a $5.06B$ model with a 30-layer Video Expert and a two-layer Action Expert, achieving $98.25$% success on LIBERO and $72.24$% pooled success on LIBERO-Plus, demonstrating effective task execution and strong generalization with an ultralight action head. Ablations support joint representation adaptation and unified LBQ conditioning. Relative to Fast-WAM, BridgeWAM reduces single-sample inference FLOPs by $28.72$% in simulation profiling and real-robot latency to $77.1$ms ($2.47\times$ speedup). Qualitative experimental results further indicate preferential attention to target objects and task destinations across perturbations. Our project page is available at https://bridgewam.github.io/Bridge-Wam-show/ .