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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).
``` |