Datasets:
Formats:
parquet
Languages:
English
Size:
1M - 10M
ArXiv:
Tags:
spatial-reasoning
counterfactual-reasoning
physical-reasoning
embodied-ai
robotics
set-of-marks
License:
Add paper and code links, update citation
#1
by nielsr HF Staff - opened
README.md
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---
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-
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task_categories:
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- visual-question-answering
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- image-text-to-text
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- en
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tags:
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- spatial-reasoning
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- counterfactual-reasoning
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- set-of-marks
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- multiple-choice
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- ai2thor
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size_categories:
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- 100K<n<1M
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configs:
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- config_name: sim_colour_som
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default: true
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ground truth behind every question (optical flow, depth, instance segmentation, moved-object masks, camera and
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move metadata) is released as a separate config, `sim_ground_truth`.
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| subset | scenes | split | questions | image pairs | image styles |
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|---|---|---|---|---|---|
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| **FlowBench-Sim** | AI2-THOR indoor houses | `train` (ProcTHOR-10k) | 419,612 | 117,604 scene samples | `natural`, `colour_som`, `red_som` |
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```python
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letters = [l for l in "ABCDEF" if row[f"choice_{l}"] is not None]
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options = "
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```
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FlowBench-Real rows also include the exact text used in our evaluations: `prompt` for the counterfactual
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## Citation
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```bibtex
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@misc{
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title
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author
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year
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}
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```
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---
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language:
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- en
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license: other
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size_categories:
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- 100K<n<1M
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task_categories:
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- visual-question-answering
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- image-text-to-text
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pretty_name: FlowBench
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tags:
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- spatial-reasoning
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- counterfactual-reasoning
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- set-of-marks
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- multiple-choice
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- ai2thor
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configs:
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- config_name: sim_colour_som
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default: true
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ground truth behind every question (optical flow, depth, instance segmentation, moved-object masks, camera and
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move metadata) is released as a separate config, `sim_ground_truth`.
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**Paper:** [FlowBench: Counterfactual Imagination Training for Spatial Intelligence](https://huggingface.co/papers/2611.03937) · **Code:** [github.com/liuyifan22/FlowBench](https://github.com/liuyifan22/FlowBench)
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| subset | scenes | split | questions | image pairs | image styles |
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|---|---|---|---|---|---|
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| **FlowBench-Sim** | AI2-THOR indoor houses | `train` (ProcTHOR-10k) | 419,612 | 117,604 scene samples | `natural`, `colour_som`, `red_som` |
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```python
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letters = [l for l in "ABCDEF" if row[f"choice_{l}"] is not None]
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options = "
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".join(f"{l}. {row[f'choice_{l}']}" for l in letters)
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counterfactual_prompt = f"{row['motion_command']}
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{row['question']}
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{options}" # with image_before
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static_prompt = f"{row['static_question']}
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{options}" # with image_after
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```
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FlowBench-Real rows also include the exact text used in our evaluations: `prompt` for the counterfactual
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## Citation
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```bibtex
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@misc{liu2026flowbench,
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title = {FlowBench: Counterfactual Imagination Training for Spatial Intelligence},
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author = {Liu, Yifan and Zhang, Baihan and Aw, Khai Loong and Kotar, Klemen and Yamins, Daniel L. K.},
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year = {2026},
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howpublished = {\url{https://github.com/liuyifan22/FlowBench}},
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note = {Yifan Liu, Baihan Zhang, Khai Loong Aw, and Klemen Kotar contributed equally. Paper: \url{https://huggingface.co/papers/2611.03937}}
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}
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```
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