大模型脑子里真有一张空间地图
大模型说“球在桌子左边”时，它可能不是查了话术，而是真的在内部画了个坐标。研究者在大模型的多层神经元里找到一个叫 S-Space 的线性子空间，物体对应的激活值会沿着水平、垂直、距离三个轴编码连续坐标——就像一张内部地图。更关键的是，这张地图不是被特定问题逼出来的，平时问它无关问题，坐标也一直在。模型做空间判断时直接读这张图；换视角推理时，它会把图做变换；回答时，它只挑需要的部分读。研究者还能用这张图当诊断工具：发现模型经常不是“看不懂空间”，而是“变换视角”这一步掉链子。它不是你明天能用上的东西，但它第一次让人看见：语言模型的空间感不是模仿话术，而是真有内部几何。
📄 原文摘要(英文)
Multimodal language models (MLLMs) exhibit promising spatial reasoning capabilities, yet the internal mechanisms supporting them remain poorly understood. We uncover S-Space, a linear representation subspace in which object-token representations encode continuous coordinates along horizontal, vertical, and distance axes. Although we identify these axes by differentiating spatial-answer scores with respect to object-token activations, S-Space is intrinsic rather than induced by specific data or prompts, remaining readable under non-spatial questions. We show that S-Space functions as an internal spatial workspace of MLLMs. Perception encodes object locations into it, forming spatial representations that can be directly read out to support accurate spatial judgments. Internal reasoning transforms it for changing viewpoints. Verbal responses selectively access it, evidenced by causal interventions that alter spatial judgments while largely preserving non-spatial responses. Case studies further show that S-Space assigns locations to unseen entities and the observer, and connects physical directions with abstract linguistic meanings, suggesting both the capabilities and limitations of large-scale language training. Finally, we use S-Space as a diagnostic tool for spatial reasoning failures. Direction-word readouts reveal systematic coupling between cardinal and image-relative reference frames. More importantly, a perception-computation decomposition exposes a key bottleneck: MLLMs can form capable spatial representations, yet fall short in the spatial transformations required to reason reliably across viewpoint changes.