REMAP: Evaluating Cross-Perspective Spatial Reasoning

Building coherent spatial representations requires relating what is seen from different viewpoints to a shared global layout. Frontier multimodal models are becoming increasingly capable at this problem in realistic environments, but it is unclear whether these gains reflect robust spatial reasoning or dependence on familiar correspondences between representations. We introduce REMAP, a benchmark for cross-perspective spatial reasoning in which models identify a target location on an allocentric map from multiple egocentric views. REMAP is based on settings developed for cognitive studies of human spatial reasoning. It combines FLOORPLAN, which is based on indoor layouts, and TRIANGLE, a controlled diagnostic that isolates robustness to changes in how local views correspond to the global map. REMAP exhibits high discriminative power. Across 24 vision-language models and human participants, capability ranges from below random to surpassing average human performance on FLOORPLAN. TRIANGLE enables deeper analysis, showing that simple variations in the geometric correspondence between views can lead to significant performance drops, whereas humans are largely robust.