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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.
The study assesses mentalization in large language models using computational modeling and economic games, finding that models like GPT-5 exhibit adaptive reasoning that can exceed human performance.