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README.md
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---
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license: mit
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language:
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- en
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size_categories:
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- n<1K
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---
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# VIPER Benchmark
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- **Physics**: Understanding of physical laws and dynamics (32 samples)
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- **Planning**: Spatial navigation and object manipulation (44 samples)
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- **Spatial**: 3D spatial reasoning and transformations (60 samples)
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- **Structural**: Pattern recognition and constraint satisfaction (70 samples)
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- **Symbolic**: Knowledge reasoning and mathematical problem-solving (60 samples)
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- **Temporal**: Temporal dynamics and object tracking (43 samples)
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## Dataset Structure
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### Data Instances
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Each instance contains:
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```json
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{
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"domain": "Spatial",
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"id": "block_rotate_0",
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"image": "Spatial/images/block_rotate_0.jpg",
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"reference": {
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"frames": ["Spatial/references/block_rotate_0_0.jpg"],
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"text": []
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},
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"prompt": "A structure composed of 9 cubes viewed from...",
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"protocol": ["Maintain the block structure..."],
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"task_type": "block_rotate"
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}
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```
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### Data Fields
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- `domain`: The reasoning domain (Physics, Planning, Spatial, Structural, Symbolic, Temporal)
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- `id`: Unique identifier for the sample
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- `image`: Path to the input image
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- `reference`: Reference materials for evaluation
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- `frames`: Reference image frames
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- `text`: Reference text descriptions
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- `prompt`: Text prompt describing the task or desired output
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- `protocol`: Evaluation criteria or constraints
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- `task_type`: Specific task category within the domain
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## Dataset Statistics
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| Symbolic | 60 | knowledge, math, multimodal |
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| Temporal | 43 | obj_move, zoom |
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**Physics** (32 samples):
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- experiment: 18 samples
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- game: 14 samples
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- navigation: 25 samples
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- obj_manipulation: 19 samples
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**Spatial** (60 samples):
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- block_rotate: 25 samples
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- dice: 20 samples
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- image_restore: 15 samples
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**Structural** (70 samples):
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- chess: 20 samples
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- maze: 20 samples
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- sudoku: 20 samples
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- ttt: 10 samples
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**Symbolic** (60 samples):
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- knowledge: 20 samples
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- math: 20 samples
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- multimodal: 20 samples
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**Temporal** (43 samples):
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- obj_move: 25 samples
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- zoom: 18 samples
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## Usage
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```python
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from datasets import load_dataset
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```
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```bibtex
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@article{li2026viper,
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---
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dataset_info:
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features:
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- name: id
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dtype: string
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- name: domain
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dtype: string
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- name: task_type
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dtype: string
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- name: prompt
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dtype: string
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- name: image
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dtype: image
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- name: reference_frames
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sequence: image
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- name: reference_text
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sequence: string
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- name: protocol
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dtype: string
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configs:
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- config_name: default
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data_files:
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- split: train
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path: dataset.parquet
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license: mit
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language:
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- en
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---
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# Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning
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## 👀 About VIPER
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- Overview: Process-aware evaluation for Generative Video Reasoning tasks.
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- Statistics: 309 carefully curated samples spanning 6 distinct domains (i.e., temporal, structural, symbolic, spatial, physics and planning reasoning).
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- New Metric: Process-outcome Consistency (POC@$r$). POC@$r$ evaluate video correctness at both process- and outcome-level, with multiple frames uniformly sampled from the whole video at rate $r$, instead of the last frame only.
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## Dataset Statistics
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| Symbolic | 60 | knowledge, math, multimodal |
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| Temporal | 43 | obj_move, zoom |
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## 📦 Dataset Usage
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### Download
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```python
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from datasets import load_dataset
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```
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### Data Fields
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- `id`: Unique identifier for the sample
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- `domain`: The reasoning domain (Physics, Planning, Spatial, Structural, Symbolic, Temporal)
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- `task_type`: Specific task category within the domain
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- `prompt`: Text prompt describing the task
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- `image`: The input image
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- `reference_frames`: Ground-truth image frames
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- `reference_texts`: Ground-truth text descriptions
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- `protocol`: Process-level task constraints
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## 📝 Citation
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If you find our benchmark useful, please consider citing us:
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```bibtex
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@article{li2026viper,
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