Datasets:

cubec
/

spatialtunnel

configs:
  - config_name: size_variation
    data_files: size_variation.tsv
    sep: "\t"
    default: true
  - config_name: phase_variation
    data_files: phase_variation.tsv
    sep: "\t"
  - config_name: contrastive_probing
    data_files: contrastive_probing.tsv
    sep: "\t"
task_categories:
  - image-text-to-text

SpatialTunnel

SpatialTunnel is a Blender-rendered diagnostic dataset for studying how vision-language models (VLMs) represent spatial relations internally. It was introduced in the paper: Why Far Looks Up: Probing Spatial Representation in Vision-Language Models.

The dataset is designed to isolate spatial shortcut biases (like the perspective bias where "higher in image means farther away") by removing background and perspective correlations present in natural images.

Resources

Project page: https://cheolhong0916.github.io/whyfarlooksup.github.io/
Contrastive-probing code: https://github.com/cheolhong0916/contrastive-probing
SpatialTunnel generation code: https://github.com/cube-c/spatialtunnel-dataset-gen

Dataset Configs

Config	File	Rows	Description
`size_variation`	`size_variation.tsv`	1,100	Apparent-size questions under controlled object-size variation.
`phase_variation`	`phase_variation.tsv`	3,072	Distance questions with controlled angular-position variation.
`contrastive_probing`	`contrastive_probing.tsv`	1,200	Balanced spatial-relation questions for contrastive probing.

Format

All configs are tab-separated files with the following columns:

Column	Description
`index`	Row index within the selected config.
`image`	Base64-encoded PNG image.
`question`	Spatial question to ask the model.
`answer`	Ground-truth answer for the row.

Sample Usage

You can download specific parts of the benchmark using the Hugging Face CLI:

huggingface-cli download cubec/spatialtunnel contrastive_probing.tsv --repo-type dataset --local-dir ./data

Citation

If you use this dataset, please cite our paper:

@article{min2026whyfarlooksup,
  title   = {Why Far Looks Up: Probing Spatial Representation in Vision-Language Models},
  author  = {Min, Cheolhong and Jung, Jaeyun and Lee, Daeun and Jeon, Hyeonseong and
             Su, Yu and Tremblay, Jonathan and Song, Chan Hee and Park, Jaesik},
  journal = {arXiv preprint arXiv:2605.30161},
  year    = {2026},
}