title: Time Blindness: Why Video-Language Models Can't See What Humans Can?
summary: 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 performance
  gap highlights a critical limitation: an over-reliance on frame-level spatial
  features and an inability to extract meaning from temporal cues. Furthermore,
  when trained in data sets with low spatial signal-to-noise ratios (SNR),
  temporal understanding of models degrades more rapidly than human perception,
  especially in tasks requiring fine-grained temporal reasoning. 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 has been
  made available on our project website: https://timeblindness.github.io/.
paper_authors._id: 683d3d6f3f97feb881155aef
paper_authors.name: Ujjwal Upadhyay
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-06-03T11:04:07.792Z
paper_authors._id: 5df7ca7cda6d0311fd3d53f2
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paper_authors.fullname: Ujjwal Upadhyay
paper_authors.type: user
paper_authors.user: ujjwal9
paper_authors._id: 683d3d6f3f97feb881155af0
paper_authors.name: Mukul Ranjan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:40:23.895Z
paper_authors._id: 65262a396b41932089fd7bae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65262a396b41932089fd7bae/6YIEoAfJojuTW1UOKlwZT.png
paper_authors.fullname: Mukul Ranjan
paper_authors.type: user
paper_authors.user: mukul54
paper_authors._id: 683d3d6f3f97feb881155af1
paper_authors.name: Zhiqiang Shen
paper_authors._id: 683d3d6f3f97feb881155af2
paper_authors.name: Mohamed Elhoseiny
paper_title: Time Blindness: Why Video-Language Models Can't See What Humans Can?
paper_summary: 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 performance
  gap highlights a critical limitation: an over-reliance on frame-level spatial
  features and an inability to extract meaning from temporal cues. Furthermore,
  when trained in data sets with low spatial signal-to-noise ratios (SNR),
  temporal understanding of models degrades more rapidly than human perception,
  especially in tasks requiring fine-grained temporal reasoning. 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 has been
  made available on our project website: https://timeblindness.github.io/.
paper_ai_summary: SpookyBench is a benchmark for temporal pattern recognition in videos that highlights the limitations of vision-language models in processing noise-like frames without spatial information.
paper_ai_keywords: vision-language models VLMs spatio-temporal relationships temporal sequences noise-like frames biological signaling covert communication frame-level spatial features temporal understanding data sets low spatial signal-to-noise ratios SNR temporal reasoning novel architectures training paradigms systematic analysis