English(EN) Zhejiang University's SpaceCast-Bench: Best VLM Scores 58% at Predicting Spatial Change, Humans 87%

浙江大学SpaceCast-Bench揭示VLM在空间推理方面的局限性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-13 07:15

浙江大学的研究人员开发了SpaceCast-Bench，这是一个旨在评估视觉语言模型（VLM）空间推理能力的新基准。在测试中，表现最佳的VLM得分仅为58.0%，远低于人类的87.2%，凸显了当前人工智能在理解和预测空间变化方面的局限性。 AI

影响凸显了当前人工智能在复杂空间推理方面的局限性，指明了未来VLM发展的方向。

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Pandaily TIER_1 English(EN) · [email protected] (Pandaily) · 2026-11-13 07:15

浙江大学SpaceCast-Bench：VLM预测空间变化得分58%，人类得分87%

Zhejiang University's SpaceCast-Bench tests whether VLMs can predict spatial relations after a scene changes. The best of 21 models scored 58.0% against 87.2% for humans.