AI学会「脑补」看不见的视角
现在的AI看图说话，遇到「从背面看这个物体长什么样」这类需要空间想象的问题就抓瞎。研究者发现，让AI在回答前先输出一组「想象感知标记」——相当于让模型先脑补出从其他角度看到的画面，再作答——效果比让它用文字一步步推理好得多。在三个新设计的空间推理任务上，这种训练方式让准确率提升3.4%，而传统的文字链式推理反而会拖后腿。它不是你明天能用上的功能，但提示了一个方向：让AI像人一样先「在脑子里画图」再思考，可能比逼它用语言硬算更靠谱。
📄 原文摘要(英文)
Vision language models (VLMs) excel at many tasks but still struggle with spatial reasoning when critical information is not directly observable. Many such problems require imaginative perception: inferring what would be seen from an unseen viewpoint, tracing paths through occluded spaces, or integrating partial observations into a coherent spatial representation. We introduce Imaginative Perception Tokens (IPT), intermediate perceptual representations that externalize what a VLM would perceive under alternative spatial configurations while remaining consistent with the observed input. To study this capability, we formulate three tasks, Perspective Taking (PET), Path Tracing (PT), and Multiview Counting (MVC), and construct datasets of approximately 20K examples with ground truth imaginations, answers, and evaluation benchmarks. Using the unified VLM BAGEL as the backbone, IPT supervision consistently improves spatial reasoning and often outperforms textual chain of thought training, even without generating images at inference time. On MVC, IPT improves accuracy by 3.4% and achieves competitive performance with strong closed-source models on PT. We further find that combining IPT and label-only supervision yields additional gains, whereas textual chain of thought can substantially degrade performance, suggesting a modality mismatch when spatial computation is forced through language. Overall, IPT provides a principled supervision signal for reasoning about unobserved spatial structure, improving generalization while producing interpretable intermediate representations.