Spatial intelligence unfolds through a perception-action loop: agents act to acquire observations, and reason about how observations vary as a function of action. Rather than passively processing what is seen, they actively uncover what is unseen - occluded structure, dynamics, containment, and functionality that cannot be resolved from passive sensing alone. We move beyond prior formulations of spatial intelligence that assume oracle observations by recasting the observer as an actor. We introduce ESI-BENCH, a comprehensive benchmark for embodied spatial intelligence spanning 10 task categories and 29 subcategories built on OmniGibson, grounded in Spelke's core knowledge systems. Agents must decide what abilities to deploy - perception, locomotion, and manipulation - and how to sequence them to actively accumulate task-relevant evidence. We conduct extensive experiments on state-of-the-art MLLMs and find that active exploration substantially outperforms passive counterparts, with agents spontaneously discovering emergent spatial strategies without explicit instructions, while random multi-view often adds noise rather than signal despite consuming far more images. Most failures stem not from weak perception but from action blindness: poor action choices lead to poor observations, which in turn drive cascading errors. While explicit 3D grounding stabilizes reasoning on depth-sensitive tasks, imperfect 3D representation proves more harmful than 2D baselines by distorting spatial relations. Human studies further reveal that unlike humans who seek falsifying viewpoints and revise beliefs under contradiction, models commit prematurely with high confidence regardless of evidence quality, exposing a metacognitive gap that neither better perception nor more embodied interaction alone can close.

翻译：摘要：空间智能通过感知-行动循环展开：智能体通过行动获取观察，并推理观察如何随行动变化。它并非被动处理所见之物，而是主动揭示不可见之物——被动感知无法解析的遮挡结构、动态、包含关系及功能属性。我们超越先前依赖先知观察的空间智能范式，将观察者重新定义为行动者。基于OmniGibson平台，以Spelke核心知识体系为理论根基，我们提出ESI-BENCH——跨越10个任务类别与29个子类别的具身空间智能综合基准。智能体必须自主决策需调用的能力（感知、移动、操作）及其序列编排，以主动积累任务相关证据。针对最先进多模态大语言模型的广泛实验表明：主动探索显著优于被动策略，智能体无需显式指令即可自发涌现空间策略，而随机多视角采样虽消耗更多图像，却常引入噪声而非有效信号。多数失败根源并非感知薄弱，而是"行动盲视"：错误行动选择导致劣质观察，进而引发级联误差。在深度敏感任务中，显式3D感知虽可稳定推理，但残缺的3D表征因扭曲空间关系比2D基线更具危害性。人类研究进一步揭示：不同于人类会寻求证伪视角并在矛盾时修正信念，模型无论证据质量如何均会过早固化高置信度判断，暴露出感知增强与具身交互无法单独弥补的元认知鸿沟。