---
pretty_name: ScienceVision
license: mit
language:
- en
task_categories:
- visual-question-answering
- question-answering
size_categories:
- n<1K
tags:
- 3d
- stem
- expert-annotation
- benchmark
- scientific-reasoning
- multimodal
configs:
- config_name: default
  data_files:
  - split: test
    path: data/tasks.jsonl
---

# ScienceVision

[![Blog](https://img.shields.io/badge/Blog-Read%20More-blue)](https://unipat.ai/blog/ScienceVision)

ScienceVision is a benchmark of **250 expert-authored questions about 3D scientific data**. Each task pairs an English question with a self-contained asset bundle — a 3D scene, a molecular structure, a CAD assembly, or a medical volume — and asks the model to read a quantity, identify a component, or make a geometric decision from that data.

Read the [ScienceVision blog post](https://unipat.ai/blog/ScienceVision) for the release overview.

The benchmark covers five domains, 50 tasks each: architecture, biology, chemistry, mechanical CAD and medical imaging. Tasks are grouped into 22 `sub_domain` buckets and 30 `direction` labels.

## What each task contains

Every task is a self-contained directory:

```
<domain>/<task_id>/
  question.md                  the English question
  answer.json                  the graded gold and its shape
  normalized.json              the canonical question, answer and taxonomy
  task_and_trajectory.json     the expert's annotated trajectory
  _meta.json                   task metadata and step/asset counts
  allow_mcp.json               the MCP tools the task may use
  assets/                      the 3D or volume assets the question is about
  images/                      expert screenshots referenced by the trajectory
  views/                       rendered 2D views (four view settings)
  views-aligned/               aligned 2D views (four view settings)
```

A verifier-side rubric lives outside the task directory at `verifier/scoring/<task_id>.json`. It is not staged into the agent environment.

Every task also ships a reference solution under `oracles/<task_id>/solution/` (`solution.sh`, `oracle.py`, `reasoning.md`, `oracle_trajectory.jsonl`, `notes.json`). The oracle drives the ScienceVision MCP tools inside the task container, writes `/logs/agent/sci3d_answer.json`, and scores 1.0 through the task's own scorer.

## Distribution

| Domain | Tasks | `sub_domain` (tasks) |
|---|---:|---|
| architecture | 50 | `arch_alignment_and_stacking` 12 · `arch_dimensional_compliance` 16 · `arch_interference_and_clearance` 12 · `arch_object_geometry_readings` 10 |
| biology | 50 | `bio_interaction_typing` 8 · `bio_molecular_contact` 14 · `bio_occluded_target_geometry` 6 · `bio_proximity_choice` 8 · `bio_spatial_arrangement` 14 |
| chemistry | 50 | `chem_conformation_occlusion` 21 · `chem_crystal_periodicity` 4 · `chem_dihedral_measurement` 9 · `chem_hybridization_state` 7 · `chem_molecular_extent` 9 |
| mechanical-cad | 50 | `cad_assembly_composition_count` 10 · `cad_mechanism_motion` 6 · `cad_part_instance_count` 20 · `cad_part_property_identification` 14 |
| medical | 50 | `med_anatomy_pointing` 5 · `med_geometry_measurement` 8 · `med_lesion_pointing` 25 · `med_slice_comparison` 12 |

Every answer is one JSON object: 143 tasks ask for a single field, 107 for two or more (a reading plus the evidence it rests on).

| Domain | `assets/` files | Formats (files) |
|---|---:|---|
| architecture | 57 | GLB 35 · JSON 16 · PNG 6 |
| biology | 50 | CIF 50 |
| chemistry | 50 | XYZ 46 · CIF 4 |
| mechanical-cad | 110 | PNG 41 · STEP 32 · STP 18 · SVG 11 · DXF 8 |
| medical | 58 | NIfTI 56 · PNG 2 |

## Usage

The tabular task table loads with the standard `datasets` API:

```python
from datasets import load_dataset
import json

ds = load_dataset("UnipatAI/ScienceVision", split="test")
row = ds[0]
question = row["question"]
answer = json.loads(row["answer_json"])
```

Each row carries `public_id`, `task_id`, `domain`, `sub_domain`, `direction`, the full `question`, the gold in `answer_json`, the declared `answer_fields`, step and asset counts, the asset list, and the relative paths of the task and oracle directories.

To use an asset, download the repository and resolve the path relative to the row's `task_dir`:

```python
from huggingface_hub import snapshot_download

root = snapshot_download("UnipatAI/ScienceVision", repo_type="dataset")
task_dir = f"{root}/{row['task_dir']}"
```

`ID_MAP.csv` maps the stable `public_id` to the original `task_id`. `MANIFEST.json` records the release counts.

## Scoring

Task answers are graded by a deterministic scorer. The verifier-side rubric is in `verifier/scoring/<task_id>.json`; merge its `scorer` block into the task record at evaluation time. Tasks without a file use the default structured-answer scorer.

## Harbor tasks

`harbor/` contains a generator and one manifest per task. It produces runnable
Harbor task directories without duplicating the asset tree in this repository:

```bash
python harbor/build_tasks.py --out /tmp/sciencevision-harbor --route cc-mcp
```

The generated tasks use the standard Harbor layout (`task.toml`,
`instruction.md`, `tests/`, `environment/`) and stage the task's assets or
rendered views into the per-task Docker build context. See `harbor/README.md`
for the available routes, the manifest schema, and the image prerequisite.

## Quality

The release was checked task by task:

- **250/250** tasks have a gold answer; `answer.json`, `normalized.json` and `task_and_trajectory.json` agree in all 250.
- **250/250** golds score themselves under their rubric; **250/250** oracle answers score 1.0.
- **250/250** tasks carry their expert steps; 2,405 of 2,406 steps have their screenshot.
- **0** non-ASCII file names, **0** text files with Chinese, **0** internal paths.

Remaining known issues are listed in `KNOWN_ISSUES.md`.

## License

Released under the MIT License; see `LICENSE`.

## Citation

If you use ScienceVision, cite the release:

```bibtex
@misc{sciencevision2026,
  title        = {ScienceVision: Can AI Observe and Reason Like a Scientist?},
  author       = {{Unipat AI}},
  year         = {2026},
  howpublished = {\url{https://unipat.ai/blog/ScienceVision}},
  note         = {Dataset: \url{https://huggingface.co/datasets/UnipatAI/ScienceVision}}
}
```