Multi-view spatial reasoning in large vision-language models involves inferring view-specific spatial relations and integrating them into a consistent scene-centered representation. However, existing approaches often learn view-dependent relations tied to individual camera frames, which limits their ability to form viewpoint-invariant spatial representations. Motivated by cognitive models of spatial perspective-taking, we propose SCoRE (Spatial Consistency Reasoning), a method that formulates multi-view spatial reasoning as an egocentric-to-allocentric transformation problem. SCoRE first trains the model to infer egocentric object relations from single-view observations. It then applies reinforcement learning to encourage the model to estimate viewpoint orientation, transform egocentric relations into a shared scene-centered reference frame, and predict allocentric object relations. To enforce viewpoint-invariant reasoning, we optimize the model with a multi-view consistency reward that aligns predictions from different views in the common allocentric space. Experiments show that SCoRE improves spatial reasoning performance by +3.69%p on multi-view benchmarks and +4.72%p on general spatial reasoning benchmarks over prior state-of-the-art methods, demonstrating the effectiveness of explicit reference-frame transformation for robust multi-view spatial understanding.

Consistent reasoning about 3D spatial relations across changing viewpoints is fundamental for Embodied AI agents operating in dynamic environments. While Large Vision-Language Models (LVLMs) have advanced multimodal perception, their ability to maintain spatial consistency across diverse perspectives remains underexplored. Existing benchmarks primarily assess spatial capabilities from a static, single-view, and egocentric perspective, failing to capture the dynamic nature of real-world spatial cognition.To address this gap, we introduce SCOPE (Spatial COnsistency across PErspectives and Viewpoints), a comprehensive benchmark designed to rigorously diagnose spatial reasoning capabilities. Grounded in human cognitive theories of dual spatial representations, SCOPE discretizes the 360∘ field into multiview scenarios to systematically evaluate both allocentric and egocentric reasoning capabilities. Our dataset comprises 20.1K spatial VQA pairs derived from high-quality 3D environments. Through an extensive evaluation of 26 state-of-the-art LVLMs, we identify two fundamental limitations that prevent consistent spatial understanding across viewpoints.We hope SCOPE facilitates the diagnosis of spatial reasoning, serving as a stepping stone toward reliable embodied action.