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The paper introduces SpatialASM, a framework that trains large vision-language models to actively plan task-relevant viewpoints and mentally imagine missing spatial evidence via novel-view synthesis scaffolding, improving spatial reasoning across four backbones and seven benchmarks without extra views at inference.