Datasets:

facebook
/

sixthview_benchmark

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SixthView Benchmark

SixthView evaluates spatial reasoning by asking models to predict a withheld face of a block assembled from interlocking cube pieces. Each example includes text and image inputs together with the underlying 3D structure and answer.

file	rows	what
`benchmark/inputs.jsonl`	604	the exact model input for every arm, keyed by `id`
`benchmark/golden.jsonl`	604	the real 3D structure and the expected model output, keyed by `id`
`benchmark/images/<id>/`	11,634 PNGs	every image referenced by `inputs.jsonl`

Usage

Clone this repository to download the benchmark:

git lfs install
git clone https://huggingface.co/datasets/facebook/sixthview_benchmark

Read benchmark/inputs.jsonl and resolve its image paths relative to benchmark/. Join examples with benchmark/golden.jsonl by id for evaluation.

inputs.jsonl

id, n, k, level, held_out
arms: {
  arm1: {arm_name: "text",                   prompt_text, images: []}
  arm2: {arm_name: "multiple images",        prompt_text, images: [6 faces + 2 renders per piece]}
  arm3: {arm_name: "text + multiple images", prompt_text, images: [same as arm2]}
  arm4: {arm_name: "single image",           prompt_text, images: [merged sheet]}
}

prompt_text is byte-identical to what was sent (prompt_sha is included for verification). Images are sent in the listed order; paths are relative to benchmark/. The held-out face image is the PREDICT THIS placeholder, never the real face — this folder contains no answer-revealing images.

golden.jsonl

field
`structure_3d`	the real internal structure, an n×n×n array indexed `[z][y][x]`, z = 0 at the bottom. Each cell is the letter of the piece occupying it, or `"."` for empty.
`pieces`	the same structure per piece: `{letter: [[x, y, z], ...]}` in block coordinates
`held_out`	which face is withheld (`+X -X +Y -Y +Z -Z`); the camera sits on that side and looks inward
`answer_grid`	the withheld face: list of n strings, top row first, leftmost character first. Letter = first piece the ray meets, `"."` = see-through.
`expected_output`	the output we expect from the model, i.e. the JSON object the prompt asks for. The prompt also asks for a free-text `"reason"` key, which is recorded but not scored. Scoring is an exact match on `answer`.
`majority_baseline`, `opposite_baseline`	cell accuracy from always guessing the most common letter, and from mirroring the opposite face

Screen axes of each face (right, up): +X(+Y,+Z) -X(-Y,+Z) +Y(-X,+Z) -Y(+X,+Z) +Z(+X,+Y) -Z(-X,+Y). Ray-casting structure_3d from held_out with these axes reproduces answer_grid exactly. This was checked on all 604 rows.

Validation

With Python 3.10 or newer, run:

python scripts/check_benchmark.py

The check verifies example IDs, prompt hashes, image references, PNG headers, piece coordinates, and answers computed from the 3D structures. GitHub Actions runs this check on pushes and pull requests.

License

This repository, including the benchmark data, images, documentation, and validation script, is licensed under the Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0). You may share and adapt the material for noncommercial purposes with appropriate attribution and an indication of changes. Commercial use is not permitted under this license. See LICENSE for the full terms.

Source: https://github.com/facebookresearch/sixthview_benchmark

Prepared from source commit 0de761309cfb16044cee09cd6ed141b1122a5514. The benchmark JSONL files and PNGs are copied byte for byte. This dataset card adapts the source README for Hugging Face.

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