Datasets:

ModalityDance
/

CoWorld-Reasoner-Dev

@@ -1,97 +1,96 @@
----
-library_name: datasets
-language:
-- en
-tags:
-- multimodal
-- reasoning
-task_categories:
-- visual-question-answering
-size_categories:
-- n<1K
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: dev120.parquet
----
-# CoWorld-Reasoner-Dev
-[![Paper](https://img.shields.io/badge/Paper-b31b1b?style=for-the-badge&logo=arxiv)](https://arxiv.org/abs/2610.21910)
-[![Hugging Face Paper](https://img.shields.io/badge/Hugging%20Face-Paper-fcc21b?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/papers/2610.21910)
-[![Code](https://img.shields.io/badge/GitHub-Code-blue?style=for-the-badge&logo=github)](https://github.com/ModalityDance/CoWorld-Reasoner)
-[![Project Page](https://img.shields.io/badge/Project%20Page-GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://modalitydance.github.io/CoWorld-Reasoner/)
-## Overview
-**CoWorld-Reasoner-Dev** contains the **120 development examples** used for harness co-evolution in CoWorld Reasoner, with **244 images**:
-- **MMSI-Bench**: 30 examples
-- **SpinBench**: 30 examples
-- **CausalSpatial**: 30 examples
-- **PerceptionBench**: 30 examples
-These examples are excluded from the corresponding benchmark evaluations. The `train` split contains the development data, not the evaluation sets.
-## Data Fields
-Each example contains:
-- `sample_id`: the example identifier.
-- `benchmark`: the source benchmark.
-- `task_type`: the task category.
-- `answer_type`: the answer format.
-- `question`: the question, including options when present.
-- `options`: structured options when available.
-- `images`: an ordered list of images embedded in the Parquet file.
-- `image_paths`: the corresponding relative paths used by the code.
-- `answer`: the reference answer.
-- `score_spec`: the scoring method.
-- `official_problem`: the original PerceptionBench problem used for scoring; otherwise null.
-## Quick Start
-### Load the dataset
-```python
-from datasets import load_dataset
-ds = load_dataset("ModalityDance/CoWorld-Reasoner-Dev", split="train")
-example = ds[0]
-print(example["question"])
-print(example["answer"])
-print("Number of images:", len(example["images"]))
-example["images"][0].show()
-```
-### Use with CoWorld Reasoner
-After installing the [code repository](https://github.com/ModalityDance/CoWorld-Reasoner#installation), run:
-```shell
-python scripts/prepare/download.py
-```
-This saves questions and images to `data/dev120/` and reference answers to `scorer_bundle/data/dev120_evaluator_only.jsonl`.
-## Acknowledgements
-The examples are drawn from [MMSI-Bench](https://arxiv.org/abs/2505.23764v3), [SpinBench](https://arxiv.org/abs/2509.25390), [CausalSpatial](https://arxiv.org/abs/2601.13304), and [PerceptionBench](https://arxiv.org/abs/2607.24957). The source datasets retain their original licenses.
-## Citation
-Please cite our paper as:
-```bibtex
-@misc{cheng2026coworldreasoner,
-  title  = {CoWorld Reasoner: Agentic Multimodal Reasoning with the Evolving Video World Model-Driven Harness},
-  author = {Dongjie Cheng and Ruifeng Yuan and Ran Wei and Ning Song and Yongqi Li and Wenjie Li},
-  year   = {2026},
-  eprint = {2610.21910},
-  archivePrefix = {arXiv},
-  primaryClass = {cs.AI},
-  url    = {https://arxiv.org/abs/2610.21910}
-}
-```

+---
+library_name: datasets
+language:
+- en
+tags:
+- multimodal
+- reasoning
+task_categories:
+- visual-question-answering
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: dev120.parquet
+---
+# CoWorld-Reasoner-Dev
+[![Paper](https://img.shields.io/badge/Paper-b31b1b?style=for-the-badge&logo=arxiv)](https://arxiv.org/abs/2610.21910)
+[![Hugging Face Paper](https://img.shields.io/badge/Hugging%20Face-Paper-fcc21b?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/papers/2610.21910)
+[![Code](https://img.shields.io/badge/GitHub-Code-blue?style=for-the-badge&logo=github)](https://github.com/ModalityDance/CoWorld-Reasoner)
+[![Project Page](https://img.shields.io/badge/Project%20Page-GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://modalitydance.github.io/CoWorld-Reasoner/)
+## Overview
+**CoWorld-Reasoner-Dev** contains the **120 development examples** used for harness co-evolution in CoWorld Reasoner, with **244 images**:
+- **MMSI-Bench**: 30 examples
+- **SpinBench**: 30 examples
+- **CausalSpatial**: 30 examples
+- **PerceptionBench**: 30 examples
+These examples are excluded from the corresponding benchmark evaluations. The `train` split contains the development data, not the evaluation sets.
+## Data Fields
+Each example contains:
+- `sample_id`: the example identifier.
+- `benchmark`: the source benchmark.
+- `task_type`: the task category.
+- `answer_type`: the answer format.
+- `question`: the question, including options when present.
+- `options`: structured options when available.
+- `images`: an ordered list of images embedded in the Parquet file.
+- `image_paths`: the corresponding relative paths used by the code.
+- `answer`: the reference answer.
+- `score_spec`: the scoring method.
+## Quick Start
+### Load the dataset
+```python
+from datasets import load_dataset
+ds = load_dataset("ModalityDance/CoWorld-Reasoner-Dev", split="train")
+example = ds[0]
+print(example["question"])
+print(example["answer"])
+print("Number of images:", len(example["images"]))
+example["images"][0].show()
+```
+### Use with CoWorld Reasoner
+After installing the [code repository](https://github.com/ModalityDance/CoWorld-Reasoner#installation), run:
+```shell
+python scripts/prepare/download.py
+```
+This saves questions and images to `data/dev120/` and reference answers to `scorer_bundle/data/dev120_evaluator_only.jsonl`.
+## Acknowledgements
+The examples are drawn from [MMSI-Bench](https://arxiv.org/abs/2505.23764v3), [SpinBench](https://arxiv.org/abs/2509.25390), [CausalSpatial](https://arxiv.org/abs/2601.13304), and [PerceptionBench](https://arxiv.org/abs/2607.24957). The source datasets retain their original licenses.
+## Citation
+Please cite our paper as:
+```bibtex
+@misc{cheng2026coworldreasoner,
+  title  = {CoWorld Reasoner: Agentic Multimodal Reasoning with the Evolving Video World Model-Driven Harness},
+  author = {Dongjie Cheng and Ruifeng Yuan and Ran Wei and Ning Song and Yongqi Li and Wenjie Li},
+  year   = {2026},
+  eprint = {2610.21910},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.AI},
+  url    = {https://arxiv.org/abs/2610.21910}
+}
+```

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