# Kairos: A Dataset for Fine-Grained Video-Language Modeling over Space, Time, and Dynamics

arXiv: https://arxiv.org/abs/2609.08755 · HTML: https://mlsanity.com/paper/2609.08755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 95 references that are in the map.
- First posted on arXiv 8 Sep 2026.

## Builds on (most cited first, 22 of 22)

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## Abstract (arXiv)

Many emerging video language modeling tasks require systems to move beyond clip-level abstraction and model visual content as it unfolds over extended time horizons. However, most existing video datasets rely on coarse or sparsely aligned supervision, which compresses temporal variation and limits the ability of models to learn reusable representations of continuous visual dynamics. We introduce Kairos, a video dataset for video-language modeling with time-resolved annotations. Kairos consists of long-duration videos, ranging from ten minutes to half an hour, annotated with fine-grained temporal alignment. The annotations capture ongoing actions, entity appearances and attributes, interactions, and evolving contextual cues along the video timeline. This time-resolved structure supports fine-grained evaluation, long-range modeling and reasoning, instruction data construction, representation learning, and video generation. Kairos provides a general-purpose foundation for modeling visual experiences over time.