A Matter of Time: Revealing the Structure of Time in Vision-Language Models

• URL: http://arxiv.org/abs/2510.19559v1
• Date: Wed, 22 Oct 2025 13:14:02 GMT
• Title: A Matter of Time: Revealing the Structure of Time in Vision-Language Models
• Authors: Nidham Tekaya, Manuela Waldner, Matthias Zeppelzauer,
• Abstract summary: We introduce TIME10k, a benchmark dataset of over 10,000 images with temporal ground truth.<n>We evaluate the time-awareness of 37 vision-language models (VLMs) by a novel methodology.<n>We propose methods to derive an explicit timeline'' representation from the embedding space.
• Score: 1.0095483062454675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large-scale vision-language models (VLMs) such as CLIP have gained popularity for their generalizable and expressive multimodal representations. By leveraging large-scale training data with diverse textual metadata, VLMs acquire open-vocabulary capabilities, solving tasks beyond their training scope. This paper investigates the temporal awareness of VLMs, assessing their ability to position visual content in time. We introduce TIME10k, a benchmark dataset of over 10,000 images with temporal ground truth, and evaluate the time-awareness of 37 VLMs by a novel methodology. Our investigation reveals that temporal information is structured along a low-dimensional, non-linear manifold in the VLM embedding space. Based on this insight, we propose methods to derive an explicit ``timeline'' representation from the embedding space. These representations model time and its chronological progression and thereby facilitate temporal reasoning tasks. Our timeline approaches achieve competitive to superior accuracy compared to a prompt-based baseline while being computationally efficient. All code and data are available at https://tekayanidham.github.io/timeline-page/.

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