README.md

---
license: cc-by-4.0
task_categories:
- visual-question-answering
- video-text-to-text
language:
- en
tags:
- video-question-answering
- scene-understanding
- spatio-temporal-reasoning
- temporal-reasoning
- benchmark
- synthe
- diagnostic
- video
- vlm
- clevr
pretty_name: CycliST
size_categories:
- 10K<n<100K
---


# CycliST: A Video Language Model Benchmark for Reasoning on Cyclical State Transitions

[![Paper](https://img.shields.io/badge/arXiv-2512.01095-b31b1b.svg)](https://arxiv.org/abs/2512.01095)
[![Project Page](https://img.shields.io/badge/Project-Page-blue.svg)](https://simon-kohaut.github.io/CycliST/)
[![Code](https://img.shields.io/badge/GitHub-Code-181717.svg)](https://github.com/simon-kohaut/CycliST)
[![OpenReview](https://img.shields.io/badge/OpenReview-l03g53HUL2-8c1b13.svg)](https://openreview.net/forum?id=l03g53HUL2)

**CycliST** is a synthetic, diagnostic benchmark for evaluating Video Language Models (VLMs)
on their ability to reason over **cyclical state transitions**; periodic patterns in object
motion and visual attributes. Published in the *Journal of Data-centric Machine Learning
Research* (DMLR), 2026.

## Dataset Summary

Cyclical patterns are everywhere in the physical world, from traffic lights and orbiting
satellites to heartbeats and conveyor belts. Yet existing video-reasoning benchmarks largely
capture linear or causal structure and rarely test whether a model can detect, track, and
exploit *periodic* dynamics. CycliST is built to fill this gap.

Inspired by the diagnostic tradition of CLEVR, CycliST renders short, richly annotated video
sequences in which objects undergo smooth, periodic changes and always return to each
configuration at regular intervals. Each video is paired with template-generated
question–answer pairs and complete ground-truth scene metadata, enabling fine-grained
evaluation of spatio-temporal reasoning.

- **14,800** Full-HD videos (1920×1080), rendered at 32 fps, 5 seconds / 160 frames each
- **~120k** template-generated question–answer pairs
- **5 difficulty tiers** varying the number of cyclic objects, scene clutter, and lighting
- Physically based rendering via **Blender** (Cycles engine)
- Complete per-frame ground truth (positions, scale, rotation, color, spatial relations)

In the accompanying experiments, state-of-the-art open-source and proprietary VLMs (Intern,
LLaVA-Video, LLaVA-OneVision, and Gemini families, 7B–78B) struggle to reliably recognize
cyclic patterns, count objects in motion, or estimate periodicity, revealing a significant
gap in current temporal reasoning capabilities.

## Sample Videos
One example per difficulty tier. The full-resolution 1920×1080 renders live under videos/<tier>/.

<table>
  <tr>
    <td align="center" width="33%">
      <video src="https://simon-kohaut.github.io/CycliST/static/videos/unicycle.mp4" controls autoplay muted loop playsinline width="100%"></video>
      <br><b>L1 – Unicycle</b><br><sub>One cyclic object, 2–3 clutter objects</sub>
    </td>
    <td align="center" width="33%">
      <video src="https://simon-kohaut.github.io/CycliST/static/videos/unicycle_cluttered.mp4" controls autoplay muted loop playsinline width="100%"></video>
      <br><b>L2 – Unicycle-Cluttered</b><br><sub>One cyclic object, 4–9 clutter objects</sub>
    </td>
    <td align="center" width="33%">
      <video src="https://simon-kohaut.github.io/CycliST/static/videos/bicycle.mp4" controls autoplay muted loop playsinline width="100%"></video>
      <br><b>L3 – Bicycle</b><br><sub>Two cyclic objects</sub>
    </td>
  </tr>
  <tr>
    <td align="center" width="33%">
      <video src="https://simon-kohaut.github.io/CycliST/static/videos/tricycle.mp4" controls autoplay muted loop playsinline width="100%"></video>
      <br><b>L4 – Tricycle</b><br><sub>Three cyclic objects</sub>
    </td>
    <td align="center" width="33%">
      <video src="https://simon-kohaut.github.io/CycliST/static/videos/nightrider.mp4" controls autoplay muted loop playsinline width="100%"></video>
      <br><b>L5 – Nightrider</b><br><sub>Adds a scene-wide periodic light cycle</sub>
    </td>
    <td align="center" width="33%"></td>
  </tr>
</table>

## Supported Tasks

- **Video Question Answering (VQA):** Free-form answers to template-generated questions,
  evaluated with an LLM judge (Llama3-70B) for robustness to phrasing.
- **Scene Understanding / Captioning:** Describing all objects, their attributes, and their
  cyclic transitions, scored against ground-truth scene graphs (precision / recall / F1).

## Dataset Structure

The repository is organized into three top-level folders:

| Folder | Contents |
| --- | --- |
| `videos/` | Rendered Full-HD `.mp4` video sequences (160 frames @ 32 fps). |
| `scenes/` | Per-scene ground-truth metadata (JSON): object attributes and the full temporal evolution of positions, scale, rotation, color, and spatial relations. |
| `questions/` | Template-generated question–answer pairs (JSON), organized by question template and tier. |

### Splits

The dataset is divided into `train`, `validation`, and `test` splits (videos):

| Tier | Train | Test | Validation | Total |
| --- | ---: | ---: | ---: | ---: |
| L1 – Unicycle | 1,500 | 750 | 750 | 3,000 |
| L2 – Unicycle-Cluttered | 1,500 | 750 | 750 | 3,000 |
| L3 – Bicycle | 1,500 | 750 | 750 | 3,000 |
| L4 – Tricycle | 1,540 | 770 | 770 | 3,080 |
| L5 – Nightrider | 1,360 | 680 | 680 | 2,720 |
| **Total** | **7,400** | **3,700** | **3,700** | **14,800** |

### Difficulty Tiers

Each tier changes one aspect of scene complexity:

- **L1 – Unicycle:** Exactly one cyclic object with 2–3 clutter objects. Tests fundamental
  perception of a single periodic motion or attribute change in isolation.
- **L2 – Unicycle-Cluttered:** One cyclic object amidst 4–9 clutter objects, raising the
  difficulty of isolating the relevant entity.
- **L3 – Bicycle:** Two cyclic objects (plus 2–3 clutter objects), introducing interacting
  cycles and relative-phase reasoning.
- **L4 – Tricycle:** Three cyclic objects, further escalating spatio-temporal complexity.
- **L5 – Nightrider:** Adds a scene-wide periodic **light cycle** (smoothly interpolating
  between bright and dark) on top of a balanced mix of L1/L3/L4-style scenes.

## Cycle Types

Objects evolve via cycle functions that always return to their initial state after one full
period:

- **Motion cycles** (change position): *linear* (back-and-forth between two points) and
  *orbiting* (circular trajectory around a center object, which may itself be moving).
- **Attribute cycles** (change appearance): *size* (small ↔ large), *color* (continuous hue
  interpolation), and *orientation* (continuous rotation; omitted for rotation-invariant
  shapes such as spheres).
- **Light cycles** (scene-wide): sinusoidal modulation of light intensity (used in the
  Nightrider tier).

## Question Categories

Questions are produced with a template-based functional program executed over each scene's
ground-truth graph, following the CLEVR/CLEVRER tradition and extended with novel temporal
and cyclical operators.

**Temporal Descriptive** (balanced yes/no, random baseline ≈ 50%), each with an existential
(∃, "ever true") and a universal (∀, "always true") quantifier:

- **Query** – probe an attribute of a single object over time.
- **Compare** – compare an attribute between two objects.
- **Relate** – test a spatial relationship between two objects.

**Scene Representative** (random baseline ≈ 30% where multi-valued):

- **Cyclic** – *orbit* (which object is the orbit center), *orbit direction*
  (clockwise / counterclockwise), and *initial / transition* attribute values.
- **Numeric** – *counting* (number of cyclic objects), *periodicity* (frames per cycle),
  and *occurrence* (number of completed cycles).

The full set of question templates, placeholders, and functional operators is documented in
the appendix of the paper.

## Usage

The data is distributed as raw video, scene, and question files. Because the splits span
multiple per-template JSON files, the easiest way to start is to clone the repository and read
the files directly:

```bash
# Requires git-lfs (the videos are large: ~15.6 GB total)
git lfs install
git clone https://huggingface.co/datasets/AIML-TUDA/CycliST
```

Or download a subset with the Hub API:

```python
from huggingface_hub import snapshot_download

# Download only the question and scene metadata (skip the large video files)
snapshot_download(
    repo_id="AIML-TUDA/CycliST",
    repo_type="dataset",
    allow_patterns=["questions/*", "scenes/*"],
)
```

Refer to the [GitHub repository](https://github.com/simon-kohaut/CycliST) for the render
pipeline, question-generation scripts, dataset-split definitions, and the LLM-judge
calibration scripts used in the paper.

## Data Generation

Scenes are generated procedurally and validated incrementally with a backtracking placement
mechanism that enforces margins to scene boundaries and between objects across all frames.
Valid scenes are rendered in Blender using the Cycles engine for photorealistic, physically
based rendering, with keyframed transformations and built-in interpolation. Each scene yields
a 1920×1080 video at 32 fps plus a JSON file recording the complete temporal metadata
(position, scale, rotation, color over time) and spatial relationships used to generate the
VQA tasks. Lighting and camera setup follow the CLEVR configuration with randomized
translations for visual diversity.

## Evaluation

Because VLMs produce free-form text, answers are scored with an LLM judge (Llama3-70B),
calibrated on 100 questions per pipeline. Reported judge alignment with human annotation is
100% for yes/no and numeric answers, 92.6% for attribute answers, and an F1 of 87.6% for
object mapping in scene understanding. Indefinite answers are counted as incorrect for
accuracy and excluded from Mean Absolute Error (MAE). The paper reports temporal-descriptive
accuracy, orbit/transition accuracy, cycle counting (accuracy and MAE), periodicity estimation
(MAE), and scene/cycle captioning (precision, recall, F1).

## Limitations

- The dataset is **entirely synthetic** and does not capture the full nuance of real-world
  scenes.
- Cycles use **stationary frequencies**; real-world cyclicity is often non-stationary
  (e.g., varying day–night intervals).
- Object **geometry and material are fixed** over time, and **causal events** between objects
  (e.g., a traffic light affecting vehicle flow) are not modeled.
- The experiments characterize benchmark-level performance gaps rather than reverse-engineering
  individual models' internal failure modes.

## Licensing

Released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
license.

## Citation

```bibtex
@article{kohaut2026cyclist,
  title   = {CycliST: A Video Language Model Benchmark for Reasoning on Cyclical State Transitions},
  author  = {Simon Kohaut and Daniel Ochs and Shun Zhang and Benedict Flade and Julian Eggert and Kristian Kersting and Devendra Singh Dhami},
  journal = {Journal of Data-centric Machine Learning Research},
  year    = {2026},
  url     = {https://openreview.net/forum?id=l03g53HUL2}
}
```

## Authors

Simon Kohaut¹²\*, Daniel Ochs¹\*, Shun Zhang¹, Benedict Flade³, Julian Eggert³,
Kristian Kersting¹, Devendra Singh Dhami⁴ &nbsp;(\*equal contribution)

- ¹ Artificial Intelligence and Machine Learning Lab, TU Darmstadt
- ² Konrad Zuse School of Excellence in Learning and Intelligent Systems (ELIZA)
- ³ Honda Research Institute Europe GmbH
- ⁴ Uncertainty in Artificial Intelligence Group, TU Eindhoven