diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d7e052970ac68b6ec163740b05d72b3b0e796fbe --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +__pycache__/ +*.pyc +predictions/ +metrics*.json +release_summary.json diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..9ca8afa1e9cdf04284b4b32e1eb45a0050535519 --- /dev/null +++ b/LICENSE @@ -0,0 +1,15 @@ +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. diff --git a/README.md b/README.md index e5f99753cd5578d5f86c70bb7da94c52f8fe692b..abbf4874b3448a55e05f782671563b4e36abac0c 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,190 @@ --- +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} +} +``` diff --git a/data/attribute/images/001.png b/data/attribute/images/001.png new file mode 100644 index 0000000000000000000000000000000000000000..c143d9dbb0be8e2d4a8f434e803ae1af01013176 --- /dev/null +++ b/data/attribute/images/001.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f3e96f851574fd187cd8d28ee1cd0299263c405f874b5061698f9013b19104a9 +size 674263 diff --git a/data/attribute/images/002.png b/data/attribute/images/002.png new file mode 100644 index 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"eval_type": "count_exact"} +{"id": "object_prior_000045", "subset": "attribute", "file_name": "images/045.jpg", "question": "How many visible round holes are on the flute?", "answer": "8", "eval_type": "count_exact"} +{"id": "object_prior_000046", "subset": "attribute", "file_name": "images/046.jpg", "question": "How many round paint wells are visible on the palette?", "answer": "10", "eval_type": "count_exact"} +{"id": "object_prior_000047", "subset": "attribute", "file_name": "images/047.jpg", "question": "How many round paint wells are visible on the palette?", "answer": "8", "eval_type": "count_exact"} +{"id": "object_prior_000048", "subset": "attribute", "file_name": "images/048.jpg", "question": "How many rectangular compartments are visible in the ice cube tray?", "answer": "18", "eval_type": "count_exact"} +{"id": "object_prior_000049", "subset": "attribute", "file_name": "images/049.jpg", "question": "How many white grids are shown on the chessboard?", "answer": "32", "eval_type": "count_exact"} +{"id": "object_prior_000050", "subset": "attribute", "file_name": "images/050.jpg", "question": "How many 0 digit buttons are visible on the calculator keypad?", "answer": "2", "eval_type": "count_exact"} +{"id": "object_prior_000051", "subset": "attribute", "file_name": "images/051.jpg", "question": "How many horizontal lines are visible in the first music staff?", "answer": "6", "eval_type": "count_exact"} +{"id": "object_prior_000052", "subset": "attribute", "file_name": "images/052.jpg", "question": "How many horizontal lines are visible in the last music staff?", "answer": "6", "eval_type": "count_exact"} +{"id": "object_prior_000053", "subset": "attribute", "file_name": "images/053.jpg", "question": "How many wheels are shown on this BMW？", "answer": "3", "eval_type": "count_exact"} +{"id": "object_prior_000054", "subset": "attribute", "file_name": "images/054.jpg", "question": "How many wheels are visible on the bicycle?", "answer": "3", "eval_type": "count_exact"} +{"id": "object_prior_000055", "subset": "attribute", "file_name": "images/055.jpg", "question": "How many blades are visible on the ceiling fan?", "answer": "7", "eval_type": "count_exact"} +{"id": "object_prior_000056", "subset": "attribute", "file_name": "images/056.jpg", "question": "How many handles are visible on the coffee mug?", "answer": "2", "eval_type": "count_exact"} +{"id": "object_prior_000057", "subset": "attribute", "file_name": "images/057.jpg", "question": "How many handles are visible on the coffee mug?", "answer": "3", "eval_type": "count_exact"} +{"id": "object_prior_000058", "subset": "attribute", "file_name": "images/058.jpg", "question": "How many tines does the fork have?", "answer": "5", "eval_type": "count_exact"} +{"id": "object_prior_000059", "subset": "attribute", "file_name": "images/059.jpg", "question": "How many tines does the fork have?", "answer": "6", "eval_type": "count_exact"} +{"id": "object_prior_000060", "subset": "attribute", "file_name": "images/060.jpg", "question": "How many strings are visible on the guitar?", "answer": "7", "eval_type": "count_exact"} +{"id": "object_prior_000061", "subset": "attribute", "file_name": "images/061.jpg", "question": "How many tentacles are visible on the octopus?", "answer": "12", "eval_type": "count_exact"} +{"id": "object_prior_000062", "subset": "attribute", "file_name": "images/062.jpg", "question": "How many legs are visible on the stool?", "answer": "8", "eval_type": "count_exact"} +{"id": "object_prior_000063", "subset": "attribute", "file_name": "images/063.jpg", "question": "How many circular lamps are visible on the traffic light?", "answer": "5", "eval_type": "count_exact"} +{"id": "object_prior_000064", "subset": "attribute", "file_name": "images/064.jpg", "question": "How many strings are visible on the violin?", "answer": "6", "eval_type": "count_exact"} +{"id": "object_prior_000065", "subset": "attribute", "file_name": "images/065.jpg", "question": "How many receptacles are visible on the wall outlet?", "answer": "4", "eval_type": "count_exact"} +{"id": "object_prior_000066", "subset": "attribute", "file_name": "images/066.jpg", "question": "How many show five pips on the visible face?", "answer": "2", "eval_type": "count_exact"} +{"id": "object_prior_000067", "subset": "attribute", "file_name": "images/067.jpg", "question": "How many legs are visible on the dog?", "answer": "9", "eval_type": "count_exact"} +{"id": "object_prior_000068", "subset": "attribute", "file_name": "images/068.jpg", "question": "How many legs are visible on the goat?", "answer": "9", "eval_type": "count_exact"} +{"id": "object_prior_000069", "subset": "attribute", "file_name": "images/069.jpg", "question": "How many visible legs does the zebra have?", "answer": "9", "eval_type": "count_exact"} +{"id": "object_prior_000070", "subset": "attribute", "file_name": "images/070.jpg", "question": "How many legs are visible on the deer?", "answer": "9", "eval_type": 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