标题： 从有限视角的空间心理建模 显示英文标题

标题： Spatial Mental Modeling from Limited Views

摘要： 视觉语言模型（VLMs）能否像人类一样，仅从几个视角就想象出完整的场景？ 人类会形成空间心理模型，即对未看到的空间的内部表示，以推理布局、视角和运动。 我们新的MindCube基准测试包含3,268张图像中的21,154个问题，揭示了这一关键差距，现有VLMs的表现接近随机。 使用MindCube，我们系统地评估VLMs通过表示位置（认知制图）、方向（视角代入）和动态（“假设”移动的心理模拟）来构建稳健空间心理模型的效果。 然后，我们探索了三种方法来帮助VLMs近似空间心理模型，包括未见过的中间视角、自然语言推理链和认知地图。 显著的提升来自于一种协同方法，“先制图后推理”，该方法联合训练模型首先生成一个认知地图，然后在其上进行推理。 通过训练模型在这些内部地图上进行推理，我们将准确率从37.8%提高到了60.8%（+23.0%）。 添加强化学习进一步提升了性能至70.7%（+32.9%）。 我们的关键见解是，这种空间心理模型的支撑，主动构建并利用带有灵活推理过程的内部结构化空间表示，显著提高了对不可观测空间的理解。 显示英文摘要

摘要： Can Vision Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models, internal representations of unseen space, to reason about layout, perspective, and motion. Our new MindCube benchmark with 21,154 questions across 3,268 images exposes this critical gap, where existing VLMs exhibit near-random performance. Using MindCube, we systematically evaluate how well VLMs build robust spatial mental models through representing positions (cognitive mapping), orientations (perspective-taking), and dynamics (mental simulation for "what-if" movements). We then explore three approaches to help VLMs approximate spatial mental models, including unseen intermediate views, natural language reasoning chains, and cognitive maps. The significant improvement comes from a synergistic approach, "map-then-reason", that jointly trains the model to first generate a cognitive map and then reason upon it. By training models to reason over these internal maps, we boosted accuracy from 37.8% to 60.8% (+23.0%). Adding reinforcement learning pushed performance even further to 70.7% (+32.9%). Our key insight is that such scaffolding of spatial mental models, actively constructing and utilizing internal structured spatial representations with flexible reasoning processes, significantly improves understanding of unobservable space.