BEARING: A Benchmark for Egocentric–Allocentric Correspondence in Map-Guided Indoor Navigation

Map-guided navigation draws on the spatial intelligence of multimodal models, since a model must relate an allocentric map to its egocentric view and act on the result. Although maps tailored to a particular system improve its navigation, they are judged only by that navigation, so a general model's ability to relate map and view has received little systematic study. We introduce BEARING, a benchmark that tests this ability through scale, pose and identity correspondences and pairs each test with navigation from the same pose. Its 5 task categories share 60 routes in 9 Matterport3D buildings, from closed-loop navigation of a route to open-loop planning of its segments and static questions at their starts. We evaluate 9 closed-source, open-source and embodied models and find that all remain well below a human reference, with the widest gap in self-localisation. They lose directions rather than distances, in estimating their own heading, reading bearings on the map or converting them into their own frame, and only the strongest models read the map's compass and agent marker as intended. Relating the tasks to one another, we find that identity tests predict the progress of the segment that starts at their pose but not of other segments on the same route, so a navigation failure can be diagnosed with tests at the pose where it occurs.