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.gitignore

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+__pycache__/
+*.pyc
+predictions/
+metrics*.json
+release_summary.json

LICENSE

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+SABRE-Prior is licensed under the Creative Commons
+Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0).
+
+License summary:
+https://creativecommons.org/licenses/by-nc/4.0/
+
+Legal code:
+https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+Required attribution:
+SABRE: Scalable and Automated Benchmarking of VLMs under Stress
+Zixuan Lan, Luzhe Sun, Matthew R. Walter, and Jiawei Zhou (2026).
+
+The SABRE software and website are distributed separately and are not covered
+by this dataset license.

README.md

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 ---
+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}
+}
+```