Datasets:

activevision
/

hpXgvFBl7ZxO

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

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:

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.
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.

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).