| --- |
| license: cc-by-4.0 |
| pretty_name: hpXgvFBl7ZxO |
| size_categories: |
| - n<1K |
| task_categories: |
| - visual-question-answering |
| - image-classification |
| language: |
| - en |
| tags: |
| - active-perception |
| - active-vision |
| - multimodal |
| - benchmark |
| - synthetic |
| - mllm-evaluation |
| - visual-reasoning |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/manifest.json |
| --- |
| |
| # ActiveVision |
|
|
| An exam for active observers — a benchmark for diagnosing whether multimodal large language models can iteratively look at an image during reasoning, instead of compressing it into a fixed embedding once. |
|
|
| ## What's in this archive |
|
|
| | Path | Contents | |
| |---|---| |
| | `data/images/` | 85 photorealistic PNGs, the released benchmark images | |
| | `data/manifest.json` | Canonical index — one record per instance with `id`, `task`, `category`, `image`, `image_sha256`, `image_source_filename`, `question`, `answer` | |
| | `data/annotations/<task>.jsonl` | Per-task verification metadata (5 records per file × 17 tasks = 85). Includes the structural ground truth used to compute each answer (region adjacency, arrow chains, traversal paths, Hausdorff distances, etc.) | |
| | `code/<category>/<task>/creation.py` | Seedable, deterministic generator. The released images at v0.4 are produced at `--difficulty 4`. | |
| | `code/<category>/<task>/creation.md` | Per-task design and anti-shortcut spec (where present). | |
| | `code/<category>/<task>/data.json` | Per-task definition: shared question text, answer format. | |
| | `code/gpt_image_prompts.json` | One gpt-image-2 image-edit prompt per task, used to re-render the matplotlib structural draft as a photorealistic variant while preserving the discriminative structure. | |
| | `code/scope.md` | Project specification: the three task families and the six shortcut classes the design defeats. | |
| | `croissant.json` | Croissant 1.0 + Croissant-RAI 1.0 metadata for this dataset. | |
| | `LICENSE` | CC BY 4.0. | |
|
|
| ## Statistics |
|
|
| - **85 instances**, 17 tasks, 3 task families. |
| - **Distributed Scanning** (25 instances, 5 tasks): attribute_group_counting, bounded_faces_counting, counting_connected_components, counting_regions, tangled_loops. |
| - **Sequential Traversal** (25 instances, 5 tasks): arrow_chain, color_zone_sequence, line_intersections, maze, traverse_ordering. |
| - **Visual Attribute Transfer** (35 instances, 7 tasks): constellation_match_count, contour_silhouette_count, spot_the_contour_diff, spot_the_field_diff, spot_the_signal_diff, spot_the_stroke_diff, stroke_gesture_count. |
| |
| ## Loading |
| |
| ```python |
| import json, pathlib |
| root = pathlib.Path("data_neurips2026") |
| manifest = json.loads((root / "data" / "manifest.json").read_text()) |
| for item in manifest: |
| image_path = root / item["image"] |
| question = item["question"] |
| gold = item["answer"] |
| ``` |
| |
| ## Generation pipeline |
|
|
| The pipeline is the artifact. Every benchmark image is produced in two deterministic stages: |
|
|
| 1. **Geometric draft (matplotlib).** `creation.py --seed S --difficulty 4` lays out a structural specification (region partition, arrow positions, maze graph, brush-stroke field, etc.) and computes the answer in closed form. Output: a plain matplotlib PNG. |
| 2. **Photorealistic re-render (gpt-image-2).** The matplotlib draft is sent to OpenAI gpt-image-2 via the image-edit endpoint, with a per-task prompt from `code/gpt_image_prompts.json` that preserves silhouettes, positions, counts, and labels but replaces the surface material with a photorealistic style (stones on sand, hedge maze from above, starfield, etc.). |
|
|
| The released benchmark contains only the Stage-2 images. Held-out splits with unpublished seeds and additional difficulties can be regenerated from the included generators. |
|
|
| ## Responsible AI |
|
|
| See `croissant.json` for the full RAI block. Headlines: |
|
|
| - **Synthetic only**: 100% synthetic. No human subjects, no PII, no real-world events. |
| - **Use cases**: testing and validation. **Not for training.** |
| - **Limitations**: small evaluation set; adversarial-by-design (not predictive of general vision-language ability); photorealistic re-renders depend on a closed-source service. |
| - **License**: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). |
|
|
| ## Validating the Croissant file |
|
|
| Before submission, validate `croissant.json` with the official Croissant validator at: |
|
|
| > https://huggingface.co/spaces/JoaquinVanschoren/croissant-checker |
|
|
| (Run well in advance of any submission deadline — the doc warns of heavy load near deadlines.) |
|
|
| ## Citation |
|
|
| ``` |
| Anonymous. ActiveVision: An Exam for Active Observers. |
| NeurIPS 2026 Datasets and Benchmarks Track (under review). |
| ``` |
