| --- |
| 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 |
|
|
| [](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 | 73 | GLB 35 · JSON 16 · PNG 10 · PDF 8 · JPG 4 | |
| | biology | 50 | CIF 50 | |
| | chemistry | 50 | XYZ 46 · CIF 4 | |
| | mechanical-cad | 137 | PNG 66 · STEP 32 · STP 18 · SVG 13 · DXF 8 | |
| | medical | 57 | NIfTI 57 | |
|
|
| ## 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}} |
| } |
| ``` |
|
|
