---
language:
- en
license: cc-by-nc-4.0
pretty_name: "SABRE-Prior"
task_categories:
- visual-question-answering
tags:
- image
- vision-language
- benchmark
- stress-testing
gated: true
configs:
- config_name: context
  drop_labels: true
  data_files:
  - split: test
    path: data/context/**
- config_name: texture
  drop_labels: true
  data_files:
  - split: test
    path: data/texture/**
- config_name: attribute
  drop_labels: true
  data_files:
  - split: test
    path: data/attribute/**
- config_name: language
  drop_labels: true
  data_files:
  - split: test
    path: data/language/**
extra_gated_heading: "Acknowledge the SABRE-Prior data terms"
extra_gated_description: "Access is intended for non-commercial research and education."
extra_gated_button_content: "Acknowledge and request access"
extra_gated_prompt: "SABRE-Prior contains generated and edited stress-test images. The Attribute subset includes synthetic animals with non-canonical numbers of limbs, which some viewers may find unusual or unsettling. By requesting access, you acknowledge this content notice and agree to use the dataset only for non-commercial research or education under the stated license."
extra_gated_fields:
  Intended use:
    type: select
    options:
    - Non-commercial research
    - Education
    - Other non-commercial use
  I acknowledge that the Attribute subset may contain visually unusual synthetic animals: checkbox
  I agree to the non-commercial dataset license and use restriction: checkbox
---

# SABRE-Prior

SABRE-Prior is the world-prior stress-test benchmark introduced in **SABRE:
Scalable and Automated Benchmarking of VLMs under Stress**. It evaluates whether
vision-language models follow visible evidence when that evidence conflicts with
learned expectations about familiar objects, materials, scenes, and language.

Project page: https://zesearch.github.io/vlm-SABRE/  
Code: https://github.com/Zesearch/vlm-SABRE  
Paper: arXiv link coming soon

## Dataset composition

| Split | Images | Questions | Primary metric |
|:--|--:|--:|:--|
| Context | 200 | 400 | Strict four-probe pair accuracy |
| Texture | 200 | 400 | Strict four-probe pair accuracy |
| Attribute | 100 | 100 | Exact normalized count accuracy |
| Language | 100 | 100 | Exact multiple-choice accuracy |
| **Total** | **600** | **1,000** | Four-split macro average |

The benchmark has evaluation splits only and is not intended as training data.

## Loading

```python
from datasets import load_dataset

context = load_dataset("Zesearch/SABRE-Prior", "context", split="test")
texture = load_dataset("Zesearch/SABRE-Prior", "texture", split="test")
attribute = load_dataset("Zesearch/SABRE-Prior", "attribute", split="test")
language = load_dataset("Zesearch/SABRE-Prior", "language", split="test")
```

Each row contains the following common fields:

- `id`: globally unique question identifier;
- `image`: image decoded by Hugging Face from `file_name`;
- `subset`: `context`, `texture`, `attribute`, or `language`;
- `question` and `answer`;
- `eval_type`: the official scoring contract.

Context and Texture additionally contain `pair_id` and `probe`. Language
contains the four `options` and `answer_text`. Texture exposes the object and
normal/counterfactual surface labels used to construct the probe.

## Evaluation

Save one prediction file per split:

```text
predictions/
├── context.jsonl
├── texture.jsonl
├── attribute.jsonl
└── language.jsonl
```

Every line must contain the question ID and the raw model response:

```json
{"id": "context_001__base_source", "prediction": "yes"}
```

Run the included deterministic evaluator:

```bash
python evaluate.py --predictions predictions --output metrics.json
```

The official metrics are:

1. **Context:** a pair is correct only when `base_source`, `base_target`,
   `edited_source`, and `edited_target` are all correct.
2. **Texture:** a pair is correct only when `base_normal`,
   `base_counterfactual`, `edited_normal`, and `edited_counterfactual` are all
   correct.
3. **Attribute:** the normalized predicted count must exactly match the answer.
4. **Language:** the extracted A/B/C/D option must exactly match the answer.
5. **Macro accuracy:** the unweighted mean of the four split accuracies.

Question-level and probe-level accuracies for the paired splits are diagnostics,
not the main reported Context or Texture scores.

## Main results

Accuracy (%) reported in the paper:

| Model | Context | Texture | Attribute | Language | Macro avg. |
|:--|--:|--:|--:|--:|--:|
| Claude 4.6 | 10 | 40 | 17 | 58 | 31.3 |
| Kimi-k2.6 | 7 | 52 | 17 | 17 | 23.3 |
| Qwen 3.5 | 3 | 46 | 14 | 29 | 23.0 |
| Gemini 3.5 | 0 | 52 | 26 | 11 | 22.3 |
| GPT-5.4 | 1 | 28 | 20 | 23 | 18.0 |
| Grok-4.3 | 4 | 28 | 16 | 23 | 17.8 |

The mean macro-average accuracy across the six evaluated models is 22.6%.

## Intended use and limitations

SABRE-Prior is intended for non-commercial research and education involving VLM
evaluation, stress testing, robustness analysis, and benchmark methodology. It
is not intended for model training, commercial deployment, or claims about
general intelligence or real-world safety based on these scores alone.

The benchmark is intentionally adversarial and generated or edited. It does not
represent the natural frequency of objects, materials, scenes, attributes, or
language cues in the world. Performance should be interpreted only under the
evaluation protocol above.

## Content notice

The Attribute split includes synthetic images of animals and objects with
non-canonical component counts. Some animal images contain unusual numbers of
limbs and may be visually unsettling to some viewers. The content is provided
solely for benchmark research; it does not depict real animal harm.

## License and disclaimer

The dataset is released under the Creative Commons
Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0). The SABRE
software repository has its own license.

The dataset is provided "as is," without warranties of any kind. To the extent
permitted by law, the authors are not liable for claims or damages arising from
its use. Users are responsible for complying with applicable law, institutional
policies, the dataset license, and responsible research practice.

## Citation

The arXiv identifier will be added after release.

```bibtex
@article{lan2026sabre,
  title   = {SABRE: Scalable and Automated Benchmarking of VLMs under Stress},
  author  = {Lan, Zixuan and Sun, Luzhe and Walter, Matthew R. and Zhou, Jiawei},
  journal = {arXiv preprint},
  year    = {2026}
}
```