Create README.md

README.md

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+---
+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