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license: cc-by-4.0
task_categories:
- image-to-video
- text-to-video
language:
- en
size_categories:
- n<1K
pretty_name: VBVR-MultiStep-Bench
tags:
- video-reasoning
- multi-step
- long-horizon
- image-to-video
- evaluation
- benchmark
---
# 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.
## 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.
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