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LICENSE

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+FACTOR-Bench — License
+
+ANNOTATIONS
+-----------
+The FACTOR-Bench annotations (captions, labels, oracle parses, and all metadata
+in factor_bench.json / factor_bench.jsonl) are released under the
+Creative Commons Attribution 4.0 International License (CC-BY-4.0):
+
+    https://creativecommons.org/licenses/by/4.0/
+
+You are free to share and adapt the material for any purpose, provided you give
+appropriate credit. Please cite the paper (see README.md / DATACARD.md).
+
+IMAGES
+------
+FACTOR-Bench does NOT distribute images. It references images from the COCO
+val2017 dataset by id and relative path (val2017/<id>.jpg). COCO images are
+subject to the COCO terms of use and the Flickr terms of the underlying
+photographs:
+
+    https://cocodataset.org/#termsofuse
+
+Obtain images from https://cocodataset.org/#download .

README.md

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+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: FACTOR-Bench
+size_categories:
+- 1K<n<10K
+tags:
+- benchmark
+- vision-language
+- clip
+- compositionality
+- negation
+- boolean-operators
+- image-text-matching
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: factor_bench.jsonl
+---
+
+# FACTOR-Bench
+
+A diagnostic benchmark that measures whether a vision-language scoring
+interface executes **Boolean operators** (negation, conjunction, disjunction,
+exclusion, NOR) or merely tracks which concepts are mentioned. It contains
+1,695 image/caption-pair samples built from COCO val2017, all OWL-ViT-validated.
+
+Introduced in *Similarity Is Not Logic: Factored Inference for Dual-Encoder
+Vision-Language Models* (ICML 2026).
+
+- **Project page:** https://sultanmo.github.io/factored-vlm/
+- **Evaluation harness and reference results:** https://github.com/SultanMo/factored-vlm
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("sulmo/FACTOR-Bench", split="test")
+```
+
+Each sample is a two-alternative forced choice: one image and two captions
+that share the same concepts but differ in logical structure. Score both
+captions and pick the higher; `correct` gives the answer.
+
+```json
+{
+  "sample_id": "not_explicit_0000",
+  "test_type": "operator",
+  "operator": "NOT",
+  "image_id": 233825,
+  "image": "val2017/000000233825.jpg",
+  "caption_a": "an orange",
+  "caption_b": "there is no orange",
+  "correct": "b",
+  "parse_a": {"concepts": [{"text": "orange", "is_negated": false}], "operator": "SINGLE"},
+  "parse_b": {"concepts": [{"text": "orange", "is_negated": true}],  "operator": "SINGLE"},
+  "meta": {"difficulty": "easy", "position_swapped": true}
+}
+```
+
+Oracle parses are embedded in every sample, so evaluation does not depend on
+any text parser. Filter by `test_type` for the three splits:
+
+- **operator** (1,100): NOT / AND / OR / BUT_NOT / NEITHER. The main accuracy metric, chance = 50%.
+- **equivalence** (450): De Morgan / double negation / commutativity, `correct="both"`. Metric = score-consistency violation rate.
+- **compound** (145): 3-4 concepts, mixed polarity.
+
+## Images
+
+The dataset distributes captions, labels, parses, and **COCO image IDs**, not
+the images themselves. Download COCO val2017 (~1 GB) and resolve the `image`
+field against it:
+
+```bash
+wget http://images.cocodataset.org/zips/val2017.zip && unzip val2017.zip -d coco
+```
+
+## Anti-shortcut design
+
+Balanced answer positions (a/b about 50/50), balanced polarity on negation
+(about half the correct answers are the negated caption), 5+ template
+phrasings per operator, and OWL-ViT validation of every concept presence and
+absence.
+
+## License
+
+Annotations: **CC-BY-4.0**. Images: COCO val2017 under its original terms
+(not redistributed). See the LICENSE file.
+
+## Citation
+
+```bibtex
+@inproceedings{alshehri2026similarity,
+  title     = {Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models},
+  author    = {Alshehri, Sultan and Yang, Zhantao and Zhang, Han and Savvides, Marios},
+  booktitle = {International Conference on Machine Learning (ICML)},
+  year      = {2026}
+}
+```