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

Ruibo Ming, Lei Sun, Deheng Zhang, He Zhang, Jialu Li, Jian Wang, Zhendong Li, Mengshun Hu, Danda Pani Paudel, Luc Van Gool, Jinjin Gu

arXiv:2609.08755v1cs.CVcs.AI

TL;DR

Existing video datasets provide coarse or sparsely aligned supervision that misses fine-grained spatial, temporal, and dynamic evolution, particularly in long-form videos. KAIROS introduces a large long-video dataset and annotation pipeline with temporally resolved, entity-consistent representations, together with a grounded benchmark. Its experiments show long-range evidence remains difficult for current models, while KAIROS-derived fine-tuning improves performance on external long-video benchmarks.

  • Problem

    Existing datasets lack fine-grained spatial annotation, temporal alignment, and dynamic reasoning supervision, with these limitations becoming more pronounced in long-form videos.

  • Method

    KAIROS uses an automated pipeline to create multi-level, time-resolved annotations with cross-shot entity consistency, audio integration, and a temporally grounded benchmark.

  • Results

    19,004 videos provide 5,420 annotated hours, and fine-tuning on KAIROS-derived data improves accuracy on KAIROS and three external long-video benchmarks.

  • Takeaways & Limitations

    KAIROS supports evaluation and supervision of long-form video understanding at the temporal granularity at which visual experiences unfold.

  • Takeaways & Limitations

    All annotations are produced by Qwen3-VL-8B-Instruct, so systematic perception bias in that model can bias KAIROS descriptions.

Abstract

from arXiv · show

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.

1 INTRODUCTION

Existing video datasets remain limited in spatial granularity, temporal resolution, and dynamic reasoning, especially for long-form videos. KAIROS addresses these gaps with time-resolved, entity-consistent annotations and a temporally grounded benchmark for video-language modeling.

  • Motivation: Prior datasets fall short across spatial detail, temporal alignment, and fine-grained dynamics, limiting coverage of entities, attributes, relations, and evolving scene states.
  • Motivation: Long-form videos intensify these limitations because richer temporal dependencies accumulate while annotations remain sparse.
  • KAIROS: Its automated pipeline produces multi-level descriptions spanning shot-level visual content, within-shot evolution, and full-video temporal integration.
  • KAIROS: Entity consistency links recurring entities and semantic attributes across distant segments while speech and environmental audio contribute to a coherent narrative.
  • KAIROS: KAIROS supports dense captions, temporally grounded questions, explicit reasoning paths, and evaluation or improvement of video-language models.
  • Dataset and benchmark: 19,004 videos average 17 minutes and total 5,420 annotated hours across 202 scenario types, while a verified 820-video subset yields 2,870 temporally grounded questions.
  • Dataset and benchmark: Experiments show performance degrades when evidence spans several shots or an entire video, while KAIROS-derived fine-tuning improves performance on other long-video benchmarks.
  • KAIROS: KAIROS introduces a dataset for fine-grained video-language modeling over Space, Time, and Dynamics.

2 THE KAIROS DATASET

KAIROS converts long-form videos into dense, structured annotation streams that preserve local detail, temporal change, audio, and cross-shot entity continuity. The dataset is curated from YouTube and Bilibili across diverse scenarios, with automated processing designed to retain long-range dynamics at scale.

  • Annotation format: KAIROS refreshes annotations at 1 FPS, organizing frames into coherent shots and tracking entities, attributes, and relationships across the video.
  • Annotation pipeline: The automated pipeline first structures each video globally, then performs fine-grained annotation to preserve local details and long-range dynamics under a tractable computational budget.
  • Video structure parsing: Cross-shot parsing builds a video-level entity bank for eight entity types and reuses canonical labels to maintain continuity across distant shots.
  • In-shot annotation: Within each shot, the reference frame receives a full description, while subsequent 1 FPS frames record changes relative to the previous second.
  • Audio annotation: Timestamped speech transcription and 30-second ambient-audio summaries are aligned to the annotation timeline.
  • Video curation: The final dataset contains 19,004 videos totaling 5,420 hours, sourced from 9,434 YouTube and 9,570 Bilibili videos across 202 scenarios.
  • Video curation: The collection uses 12 high-level domains, 35 categories, and 202 leaf-level scenarios, while excessively long videos are segmented into clips no longer than 30 minutes.

3 THE KAIROS-BENCH

KAIROS-Bench converts dense, time-resolved annotations into human-verified questions spanning Space, Time, and Dynamics. Its design controls leakage and evaluates models across fine-grained capabilities and temporal scopes.

  • Benchmark design: KAIROS-Bench generates questions from timestamped annotations across orthogonal Space, Time, and Dynamics axes.The benchmark uses source samplers and temporal tiers to link evidence spans with capability labels.
  • Benchmark design: Distractors come from non-overlapping time windows in the same video, requiring models to localize the relevant evidence rather than reject implausible options.This design preserves semantic plausibility while separating correct and incorrect temporal contexts.
  • Benchmark construction: 2,870 human-verified MCQs over 820 videos form the final benchmark after leakage control and review for validity, grounding, uniqueness, and video dependence.Only questions receiving unanimous human pass verdicts are retained.
  • Benchmark statistics: The benchmark covers evidence spans from moment-level perception through within-shot change, cross-shot continuity, and full-video reasoning.Its 17 capabilities are grouped by temporal scope, while the benchmark distribution includes long-context questions and multiple source types.
  • Results: Model accuracy drops when required evidence spans several shots or the whole video, exposing the difficulty of long-range video reasoning.The benchmark evaluates seven closed-source and fourteen open-source models under model-specific frame or native-video inputs.
  • Results: Fine-tuning Qwen2.5-VL-7B-Instruct on 232,101 KAIROS-derived examples improves performance over the base model across KAIROS-Bench and three public long-video benchmarks.The evaluation uses only KAIROS-derived training data and reports gains without external training data.

4 CONCLUSION

KAIROS addresses coarse spatial and temporal supervision by providing long-form videos with fine-grained, temporally grounded annotations and a benchmark spanning Space, Time, and Dynamics. Its automated pipeline structures visual and audio content across frames, shots, and videos for long-range video-language modeling.

  • Conclusion: KAIROS contains 19,004 long-form videos with 1 FPS temporally grounded annotations, while KAIROS-Bench provides 2,870 questions across Space, Time, and Dynamics.The benchmark includes both multiple-choice and OpenQA questions.
  • Conclusion: Fine-tuning on KAIROS-derived training data improves performance on KAIROS and three external long-video benchmarks without using their training data.Experiments also show that performance degrades when evidence spans several shots or the whole video.
  • Conclusion: Existing video-language datasets often provide coarse spatial and temporal supervision, leaving many entities, attributes, relations, and evolving dynamics insufficiently specified.The limitation is especially pronounced for videos longer than 10 minutes.
  • Conclusion: The annotation pipeline uses a 1 FPS visual timeline, differential descriptions within shots, audio transcription and environmental summaries, and prompt-based multimodal annotation.Long shots are periodically re-anchored to maintain a stable reference state.

L. Computer Use

The corpus includes software-tutorial scenarios and filters source videos into a 10–30-minute regime, splitting longer sources into consecutive parts rather than truncating them.

  • L. Computer Use: Software-tutorial content includes Word, Excel, PowerPoint, PhotoShop, LightRoom, Premiere, Blender, and VS Code.
  • L. Computer Use: Candidate videos are filtered to 10–30 minutes, while longer sources are split into consecutive 30-minute parts so no footage is discarded.The duration regime targets long-context video understanding.

C.3 LENGTH AND STRUCTURE

KAIROS is designed around diverse content and long, multi-shot videos rather than short clips. Its YouTube partition is concentrated in the 10–30-minute range, with Sports and Media & Entertainment among the largest domains.

  • C.3 LENGTH AND STRUCTURE: The corpus’s taxonomy and length distribution substantiate its focus on the multi-shot, long-context regime that single-clip benchmarks do not exercise.The design claim is summarized through Figures 6–9 and a deeper per-scenario decomposition in Figure 10.
  • C.3 LENGTH AND STRUCTURE: Sports and Media & Entertainment account for the largest shares of the 12-domain taxonomy, while Public Safety, Embodied AI, and Drones & Remote Sensing remain single-digit domains.The taxonomy contains 35 nested categories.
  • C.3 LENGTH AND STRUCTURE: 9,434 YouTube-partition videos total 2,857 hours, with a 17.0-minute median, 18.2-minute mean, and 32.7-minute maximum duration.Most videos fall within the 10–30-minute target band.

D.1 SOURCE-TYPE SAMPLERS AND TIER MAPPING

KAIROS generates benchmark questions from timestamped annotation streams using source-type samplers whose temporal tiers are determined by actual evidence spans. The pipeline combines shot structure, long-shot handling, and atomic question generation.

  • D.1 SOURCE-TYPE SAMPLERS AND TIER MAPPING: Each source-type sampler returns annotations, timestamps, evidence spans, neighboring reference-frame context, and candidate distractors without accessing raw frames.A question’s temporal tier is determined by its realized evidence span rather than assigned afterward by the language model.
  • D.1 SOURCE-TYPE SAMPLERS AND TIER MAPPING: Median videos contain 91 shots, while the maximum reaches 1,235 shots; the right tail is dominated by fast-cut sports broadcasts and clip compilations.This distribution motivates the differential-frame chain in the annotation pipeline.
  • D.1 SOURCE-TYPE SAMPLERS AND TIER MAPPING: Median shot duration is 3.7 seconds, but held shots longer than 60 seconds make Time-axis questions non-trivial.Long shots also exercise the 300-frame re-anchor schedule.
  • D.1 SOURCE-TYPE SAMPLERS AND TIER MAPPING: The released benchmark reports source-type composition alongside the tier ranges that each sampler’s evidence span can occupy.The source types are tied to specific capability labels, including audio comprehension and narrative transition.
  • D.1 SOURCE-TYPE SAMPLERS AND TIER MAPPING: GEMINI-2.5-FLASH atomically generates each question, answer, distractor set, and reasoning field from sampled evidence and neighboring frame context.Producing all four fields in one call is intended to preserve answer–reasoning consistency.

D.2 CROSS-BENCHMARK TEXT-ONLY LEAKAGE — PROTOCOL

The protocol fixes the solver, prompt, shuffling, and sample size across benchmarks, then examines text-only leakage against question length. KAIROS-Bench is reported as a low-leakage benchmark despite long question stems.

  • Protocol: The probe fixes the solver, prompt, option-shuffle protocol, and sample size identically across benchmarks.The sample uses 500 questions per benchmark under a standardized text-only evaluation setup.
  • Diagnostic context: The diagnostics organize benchmark composition and capability coverage alongside the leakage analysis.The supplied figures include scenario distributions and a two-ring capability view spanning four levels and 17 capability cells.
  • Leakage analysis: The leakage–length scatter places KAIROS as the rightmost low-leakage benchmark.The scatter summarizes the ten-benchmark comparison underlying the regression.
  • Leakage analysis: KAIROS-Bench has the longest stems among four-option benchmarks but lies below the leakage-versus-length regression line.This indicates lower leakage than its question length would predict.

D.3 BENCHMARK DISTRIBUTION DIAGNOSTICS

The benchmark preserves broad corpus coverage while distributing videos across categories and scenarios rather than concentrating them in a few high-volume cells. This supports per-domain and per-scenario evaluation but leaves small absolute counts in the long tail.

  • Distribution: The 820 benchmark videos preserve the corpus-level taxonomy distribution across content categories and scenarios.The expanded diagnostics report per-category and per-scenario decompositions and confirm broad coverage.
  • Distribution: Small-tail domains G, H, and I are over-sampled relative to their corpus share to keep per-domain question counts non-trivial.The per-category diagnostic describes this balancing choice explicitly.
  • Distribution: The benchmark is intentionally spread thin across 202 scenarios rather than concentrated in a few high-volume cells.This makes per-scenario evaluation well-defined for more cells, at the cost of smaller long-tail counts.

E EVALUATION PROTOCOL

The evaluation runs models through standardized backends with model-specific video-input modes, frame budgets, and decoding settings. It reports both multiple-choice and open-ended performance under controlled scoring procedures.

  • Backends: The evaluation covers 21 models using a unified runner that groups questions by video and dispatches them through five backend types.The backends include vLLM, HuggingFace Transformers, OpenAI direct, Gemini direct, and Anthropic direct.
  • Video input: Only GEMINI-2.5-FLASH, GEMINI-3.1-PRO, and LLAVA-VIDEO-7B receive native video input; other models use uniformly subsampled frames.Input mode therefore varies by model rather than being uniform across the leaderboard.
  • Decoding and frames: The frame budget is capped per question, with actual frames equal to min(budget, ⌊video dur · 1⌋), and decoding is greedy except for GPT-5.5.This prevents short videos from being up-sampled beyond their native 1 FPS rate.
  • Scoring: The protocol adds OpenQA on the same 2,870 questions so models must generate free-form answers rather than select letters.OpenQA uses at least 256 decoding tokens and is judged against reference answers by GEMINI-2.5-FLASH.
  • Fine-tuning: Fine-tuning uses LoRA with a frozen vision tower, one epoch, a cosine schedule, and a 16,384-token cutoff for long prompts.The evaluation also tests the fine-tuned checkpoint with 16, 32, and 64 uniformly subsampled frames.

G.1 LIMITATIONS

The paper identifies limitations in annotation bias, language-only entity matching, residual text leakage, and uneven per-cell populations. It also states intended uses and risks associated with public platform-distributed videos.

  • Limitations: All annotations come from Qwen3-VL-8B-Instruct, so systematic perception bias in that VLM can bias KAIROS descriptions.The authors note that rerunning the pipeline with another VLM could quantify this bias.
  • Limitations: Language-only cross-shot entity matching can split identities when labels are paraphrased and merge mentions only when labels match exactly.A vision-only re-identification stage is left for future work.
  • Limitations: +9.6 pp lift over random remains in the text-only leakage probe, although it is the lowest of the ten tested benchmarks.The authors attribute residual leakage to linguistic anchors and retain the grounding-versus-leakage trade-off.
  • Limitations: Two capability cells contain only 5 and 14 questions, making their per-cell accuracy high-variance.The paper says these cells should be read as coarse signals rather than precise numbers.
  • Scope and impact: KAIROS is intended for supervision, long-form video QA benchmarking, and evaluation of alignment, entity tracking, and temporal grounding.The stated scope goes beyond clip-level decisions.
  • Scope and impact: The dataset contains only public, platform-distributed videos, with platform-takedown semantics preserved and annotation-side frames not redistributed.The paper characterizes the remaining misuse risks as typical of large video corpora.
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