| --- |
| pretty_name: ActiveVision |
| license: cc-by-4.0 |
| language: |
| - en |
| task_categories: |
| - visual-question-answering |
| - image-text-to-text |
| tags: |
| - benchmark |
| - visual-reasoning |
| - iterative-reasoning |
| - active-vision |
| - multimodal |
| - vqa |
| - evaluation |
| size_categories: |
| - n<1K |
| --- |
| |
| <p align="center"> |
| <img src="assets/banner.png" alt="ActiveVision — An Exam for Active Observers. Vision is a loop, not a glance." width="820"> |
| </p> |
|
|
| # ActiveVision — An Exam for Active Observers |
|
|
| **ActiveVision** is a benchmark for **iterative visual reasoning**: 85 photorealistic |
| items across **17 tasks** that cannot be solved from a single glance — the model has |
| to keep returning to the image to *scan*, *trace*, and *compare*. Every scene is |
| generated by a deterministic program and re-rendered photorealistically while |
| preserving the structure, so answers are exact by construction. |
|
|
| **Frontier models reach about 10%** with pure chain-of-thought (50.6% with an |
| autonomous coding agent), while **unaided humans average 96.1%**. |
|
|
| 🌐 [Website](https://activevision.dev) · 📄 [Paper](https://arxiv.org/pdf/2607.16165) · 💻 [Evaluation code](https://github.com/saccharomycetes/ActiveVision) |
|
|
| ## Examples |
|
|
| <table> |
| <tr> |
| <td width="50%" align="center"><img src="assets/example_traverse.jpg" alt="Traversal point ordering example" width="420"></td> |
| <td width="50%" align="center"><img src="assets/example_regions.jpg" alt="Region counting example" width="420"></td> |
| </tr> |
| </table> |
|
|
| ## The 17 tasks |
|
|
| | Family | Tasks | |
| |---|---| |
| | **Distributed Scanning** — find and count every signal | bounded_face_counting · connected_component_counting · region_counting · singleton_shape_counting · tangled_loop_counting | |
| | **Sequential Traversal** — follow a structure, step by step | arrow_chain_following · color_zone_sequencing · line_intersection_sequencing · maze_path_tracing · traversal_point_ordering | |
| | **Visual Attribute Transfer** — compare a property across regions | constellation_match_counting · contour_difference_spotting · field_difference_spotting · signal_difference_spotting · silhouette_match_counting · stroke_difference_spotting · stroke_match_counting | |
| |
| 5 items per task, 85 items total. Each item is **one still image and one question**; |
| answers are integers, letters, or ordered sequences — never small multiple choice, so |
| the guess floor is near zero. |
| |
| ## Dataset structure |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("activevisionai/ActiveVision", split="train") |
| # columns: image, id, task, category, question, answer |
| ``` |
| |
| | Field | Description | |
| |---|---| |
| | `image` | the photorealistic scene (PNG, up to 1536×1024) | |
| | `id` | item id, `<TASK-PREFIX>-<index>` (e.g. `MPT-2`) | |
| | `task` | one of the 17 task names above | |
| | `category` | task family: `distributed_scanning`, `sequential_traversal`, `visual_attribute_transfer` | |
| | `question` | the full question, instructing the model to answer in `<answer>...</answer>` tags | |
| | `answer` | ground truth (exact-match scoring, separator-insensitive) | |
| |
| ## Evaluation |
| |
| Score the contents of the model's **last** `<answer>...</answer>` block by exact |
| match after normalizing case, whitespace, and separators (`"C, A, G"` ≡ `"CAG"`; |
| integer answers also match numerically, `"07"` ≡ `7`). |
| |
| ## Headline results (pure chain-of-thought, best per model) |
| |
| | Model | Accuracy | |
| |---|---| |
| | Human (N=3, unaided) | **96.1%** | |
| | GPT-5.5 (xhigh) | 10.6% | |
| | Gemini 3.5 Flash (high) | 8.2% | |
| | Gemini 3.1 Pro (high) | 5.9% | |
| | Claude Opus 4.7 (max) | 4.7% | |
| | Claude Fable 5 (max) | 3.5% | |
| |
|  |
| |
| More reasoning does not close the gap: scaling effort moves models along the cost |
| axis, not toward the human band. Tool-using coding agents reach at most 50.6% |
| (Claude Code, Fable 5) at $2.74–$7.63 and 12–15 minutes per item — a human answers |
| unaided in 34 seconds. |
| |
| ## Citation |
| |
| ```bibtex |
| @article{zhang2026exam, |
| title={An exam for active observers}, |
| author={Zhang, Jiarui and Tao, Muzi and Wang, Shangshang and Liu, Ollie and Ma, Xuezhe and Neiswanger, Willie}, |
| journal={arXiv preprint arXiv:2607.16165}, |
| year={2026} |
| } |
| ``` |
| |
