Introduction  Humans naturally excel at spatial reasoning by integrating multimodal sensory inputs such as vision and sound to understand complex environments and spatial relationships. Recent breakthroughs in artificial intelligence, particularly with large multimodal reasoning models, have begun to approximate this ability to perceive, interpret, and reason across diverse spatial tasks.  However, systematic reviews of such models remain sparse, and publicly available benchmarks to evaluate them comprehensively are just emerging. The recent survey by Zheng et al. (https://arxiv.org/abs/2510.25760) fills this gap by offering a thorough overview of multimodal spatial reasoning challenges, surveying architectures, methodologies, post-training strategies, explainability approaches, and evaluating a wide spectrum of…

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