时间盲区：为何视频语言模型看不到人类能看到的东西？
Time Blindness: Why Video-Language Models Can't See What Humans Can?
SpookyBench 提供了一个极端设定：空间信息被噪声帧完全遮蔽，只保留时序结构，可直接用来诊断 VLM 是否真正在做时序推理，而非依赖帧级空间特征；做视频理解或时序建模的工作可以把它当作压力测试集，并据此设计解耦空间依赖的架构或训练目标。
原文依据 while humans can recognize shapes, text, and patterns in these sequences with over 98% accuracy, state-of-the-art VLMs achieve 0% accuracy
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期视觉语言模型（VLM）的进展在理解视频中的时空关系方面取得了令人瞩目的成果。然而，当空间信息被遮蔽时，这些模型便难以捕捉纯粹的时序模式。我们提出 SpookyBench，这是一个信息仅编码于类噪声帧的时序序列中的基准，其形式呼应了从生物信号传递到隐蔽通信的自然现象。有趣的是，人类能以超过 98% 的准确率识别这些序列中的形状、文字与图案，而最优的 VLM 准确率为 0%。这揭示了一个关键局限：过度依赖帧级空间特征，且无法从时序线索中提取含义。克服这一局限需要新的架构或训练范式，将空间依赖与时序处理解耦。我们的系统分析表明，该问题在不同模型规模与架构上普遍存在。我们发布 SpookyBench，以推动时序模式识别研究，弥合人类与机器视频理解之间的差距。数据集与代码已在项目网站公开。
英文原文摘要 1171 字符
Recent advances in vision-language models (VLMs) have made impressive strides in understanding spatio-temporal relationships in videos. However, when spatial information is obscured, these models struggle to capture purely temporal patterns. We introduce SpookyBench, a benchmark where information is encoded solely in temporal sequences of noise-like frames, mirroring natural phenomena from biological signaling to covert communication. Interestingly, while humans can recognize shapes, text, and patterns in these sequences with over 98% accuracy, state-of-the-art VLMs achieve 0% accuracy. This highlights a critical limitation: an over-reliance on frame-level spatial features and an inability to extract meaning from temporal cues. Overcoming this limitation will require novel architectures or training paradigms that decouple spatial dependencies from temporal processing. Our systematic analysis shows that this issue persists across model scales and architectures. We release SpookyBench to catalyze research in temporal pattern recognition and bridge the gap between human and machine video understanding. Dataset and code are available on the project website.