Spatial Amsan: A Benchmark for Perception-Grounded Spatial Reasoning and Action Evaluation in Egocentric Manipulation

We introduce Spatial Amsan, a benchmark for evaluatingstate-based spatial reasoning and action evaluation in egocentric manip-ulation videos. The term amsan (Korean for mental arithmetic) reflectsthe core challenge: constructing and updating an internal spatial state toevaluate whether each manipulation advances toward a goal. The bench-mark comprises a tangram task (7 colored blocks) and a wooden puz-zle task (16 blocks), totaling 282 scenarios with 1,801 evaluation turns.Models are evaluated through a multi-turn protocol that diagnosticallyisolates four capabilities: visual perception, temporal tracking, spatialcontact reasoning, and goal-directed action evaluation. Our zero-shotevaluation of nine VLMs spanning frontier API models and open-weightlocal models reveals that contact reasoning is a universal bottleneck (12–16% on the wooden puzzle despite 53–96% on block identification) andthat action evaluation degrades from above 79% to below 16% over 8turns while perception remains stable. These results highlight a fun-damental gap between perceiving scene changes and reasoning abouttheir correctness over time. We release all data, annotations, and codeat https://github.com/Blanchard-lab/SpatialAmsan.