Add dataset card

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - video-text-to-text
+  - video-classification
+language:
+  - en
+size_categories:
+  - n<1K
+pretty_name: VBVR-MultiStep-Bench
+tags:
+  - video-reasoning
+  - multi-step
+  - long-horizon
+  - image-to-video
+  - evaluation
+  - benchmark
+configs:
+  - config_name: default
+    data_files:
+      - split: test
+        path: "Multi-*/Multi-*_task/Multi-*_*/question_metadata.json"
+---
+
+# VBVR-MultiStep-Bench
+
+The frozen **180-instance public evaluation split** released alongside the [VBVR-MultiStep](https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep) training corpus. Designed for long-horizon multi-step image-to-video (I2V) reasoning evaluation.
+
+This dataset is part of the **VBVR (Very Big Video Reasoning Suite)** project. See the parent suite at <https://video-reason.com> and the suite paper [VBVR: A Very Big Video Reasoning Suite (Wang et al., ICML 2026)](https://icml.cc/virtual/2026/poster/65709).
+
+## At a glance
+
+| Property | Value |
+|---|---|
+| Tasks | **36** parameterized tasks (`Multi-01` … `Multi-36`) |
+| Reasoning families | Navigation, Planning, CSP, Execution, Geometry, Physics |
+| Instances | **180** (5 per task × 36) |
+| Per-instance artifacts | 5 (see below) |
+| License | CC-BY-4.0 |
+
+## Five-artifact data contract
+
+Every instance lives at:
+
+```
+Multi-XX_<name>_data-generator/Multi-XX_<name>_data-generator_task/Multi-XX_<name>_data-generator_<id>/
+```
+
+and contains exactly:
+
+| File | Role |
+|---|---|
+| `first_frame.png` | Model conditioning image (the only visual input the model receives at inference) |
+| `prompt.txt` | Natural-language task contract |
+| `final_frame.png` | Target endpoint (held out from the model) |
+| `ground_truth.mp4` | Reference rollout demonstrating the correct trajectory |
+| `question_metadata.json` | Seed, version, tolerances, task-specific fields |
+
+A top-level `metadata.parquet` indexes every instance with the task id, family, seed, and per-instance metadata for fast filtering.
+
+## Reasoning families
+
+| Family | Characteristic | Released tasks |
+|---|---|---|
+| Navigation | Discrete motion under adjacency / obstacle constraints | 6 |
+| Planning | Operator-based state transformation | 6 |
+| CSP | Incremental labeling under global consistency | 6 (3 used for human judging) |
+| Execution | Clocked deterministic update rules | 6 |
+| Geometry | Ordered constructive geometry | 6 |
+| Physics | Continuous dynamics with contact / conservation | 6 |
+
+Tasks `Multi-13`, `Multi-14`, `Multi-15` (CSP) are excluded from the human-judging pool described in the paper but are included in this release for completeness.
+
+## Intended use
+
+- **Primary use**: trajectory-level evaluation of I2V systems under a fixed five-artifact contract.
+- **Comparison protocol**: blind human pairwise judging on three independent axes — process correctness, reference fidelity, render quality.
+- **Companion training corpus**: [Video-Reason/VBVR-MultiStep](https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep) (~360k samples).
+
+## Loading
+
+```python
+import pandas as pd
+meta = pd.read_parquet("hf://datasets/Video-Reason/VBVR-MultiStep-Bench/metadata.parquet")
+```
+
+Or pull a single instance:
+
+```python
+from huggingface_hub import hf_hub_download
+prompt_path = hf_hub_download(
+    "Video-Reason/VBVR-MultiStep-Bench",
+    "Multi-01_maze_shortest_path_data-generator/Multi-01_maze_shortest_path_data-generator_task/Multi-01_maze_shortest_path_data-generator_00000000/prompt.txt",
+    repo_type="dataset",
+)
+```
+
+## License
+
+Released under **CC-BY-4.0**. The reference rollouts are produced from generators that consume only released task definitions; no third-party copyrighted content is embedded.
+
+Wan2.2-I2V-A14B (Apache-2.0) is referenced as a baseline model and a fine-tuning ancestor for `VBVR-Wan2.2`; this dataset does not redistribute Wan2.2 weights.
+
+## Citation
+
+```bibtex
+@inproceedings{vbvr_multistep_2026,
+  title={Could Video Generation Models Solve Long-Horizon Multi-Step Reasoning Tasks?},
+  author={Anonymous},
+  booktitle={NeurIPS Datasets and Benchmarks},
+  year={2026},
+  note={Under review.}
+}
+```
+
+The parent VBVR suite:
+
+```bibtex
+@inproceedings{wang2026vbvr,
+  title={A Very Big Video Reasoning Suite},
+  author={Wang, Maijunxian and others},
+  booktitle={ICML},
+  year={2026},
+  url={https://icml.cc/virtual/2026/poster/65709}
+}
+```
+
+## Responsible AI
+
+This dataset is fully synthetic — generators produce every instance from controlled parameters. There are no human subjects, no scraped media, and no personal information. See the [Croissant file](./croissant.json) for the complete RAI metadata.