MindBlock: Probing Spatial Assembly and Structure in Unified Multimodal Models

摘要

While Unified Multimodal Models (UMMs) show remarkablereasoning capabilities, their spatial intelligence remains limited to pas-sive 2D Question-Answering (QA). In this paper, we argue that true spa-tial intelligence demands active construction: not only recognizing a 3Dstructure in pixel space, but also building and modifying it. We introduceMindBlock, a benchmark that challenges models’ active generative con-struction in pixel space across two primary axes: Spatial Assembly,which evaluates step-by-step compositional and causal reasoning, andSpatial Structure, which probes spatial equivariance through localsub-component rotations and global viewpoint transformations. To movebeyond pixel-level metrics, we propose 3DGS-Eval, a novel validationprotocol using 3D Gaussian Splatting to reconstruct implicit scenes frommodel-generated multi-view images. This allows us to quantify structuralconsistency, verifying for the first time whether a model’s generative out-put admits a coherent internal 3D world model. Furthermore, we con-duct a deep-dive diagnostic analysis into the representational groundingof spatial logic, disentangling whether structural consistency relies ontextual Chain-of-Thought (CoT) as a symbolic scaffold, or emerges as anative spatial intuition within the generative latent space. Our findingsreveal a significant “perception-execution” gap: while current models cor-rectly identify the intended spatial state, they fail at active construction.They struggle to maintain spatial equivariance without explicit symbolicscaffolding. MindBlock provides a rigorous foundation for the next gen-eration of embodied, physically-grounded multimodal AI.