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  ---
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  license: cc-by-4.0
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  pretty_name: CausalCascade
 
 
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  tags:
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  - video
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  - image-to-video
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  # CausalCascade
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- ![Paper](https://img.shields.io/badge/Paper-arXiv_pending-red)
 
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  [![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/deepdeltalab/CausalCascade)
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  [![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/SJTU-DeepDeltaLab/CausalCascade)
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  The release contains **50 event-rich Blender scenes**, structured causal event graphs, **872 graph-grounded diagnostic questions**, three levels of future-event specification, and **2,832 controlled counterfactual variants**. A subset of **225 matched factual-counterfactual pairs** supports evaluation of selective responsiveness to physical interventions.
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- <!-- Uncomment after replacing ARXIV_ID with the published preprint identifier.
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- [Paper on arXiv](https://arxiv.org/abs/ARXIV_ID)
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- -->
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-
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  ## Evaluation
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  The benchmark supports factual causal-cascade evaluation under three levels of future-event specification and paired counterfactual evaluation under *delete*, *move*, scale, and mass interventions. Motion-based metrics and graph-grounded diagnostic VQA provide complementary measures of physical evolution and causal-process fidelity.
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  Some `.blend` source scenes and rendered media incorporate textures, HDRIs, and 3D models from [Poly Haven](https://polyhaven.com), released under [CC0 1.0](https://polyhaven.com/license). See [ATTRIBUTION.md](ATTRIBUTION.md).
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- ## Citation (TBD)
 
 
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  ```bibtex
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- @misc{causalcascade2026,
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  title = {A Plausible Future Is Not a Causal Future: Can Video Models Generate How the World Unfolds?},
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- author = {AUTHOR_LIST},
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  year = {2026},
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- eprint = {ARXIV_ID},
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  archivePrefix = {arXiv},
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- primaryClass = {ARXIV_CATEGORY},
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- url = {https://arxiv.org/abs/ARXIV_ID}
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  }
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  ```
 
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  ---
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  license: cc-by-4.0
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  pretty_name: CausalCascade
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+ task_categories:
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+ - image-to-video
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  tags:
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  - video
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  - image-to-video
 
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  # CausalCascade
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+ [![Paper](https://img.shields.io/badge/Paper-arXiv_2610.15562-red)](https://arxiv.org/abs/2610.15562)
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+ [![Paper](https://img.shields.io/badge/HF-Paper-yellow)](https://huggingface.co/papers/2610.15562)
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  [![Dataset](https://img.shields.io/badge/Dataset-HuggingFace-yellow)](https://huggingface.co/datasets/deepdeltalab/CausalCascade)
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  [![Code](https://img.shields.io/badge/Code-GitHub-black)](https://github.com/SJTU-DeepDeltaLab/CausalCascade)
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  The release contains **50 event-rich Blender scenes**, structured causal event graphs, **872 graph-grounded diagnostic questions**, three levels of future-event specification, and **2,832 controlled counterfactual variants**. A subset of **225 matched factual-counterfactual pairs** supports evaluation of selective responsiveness to physical interventions.
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  ## Evaluation
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  The benchmark supports factual causal-cascade evaluation under three levels of future-event specification and paired counterfactual evaluation under *delete*, *move*, scale, and mass interventions. Motion-based metrics and graph-grounded diagnostic VQA provide complementary measures of physical evolution and causal-process fidelity.
 
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  Some `.blend` source scenes and rendered media incorporate textures, HDRIs, and 3D models from [Poly Haven](https://polyhaven.com), released under [CC0 1.0](https://polyhaven.com/license). See [ATTRIBUTION.md](ATTRIBUTION.md).
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+ ## Citation
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+
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+ If you use the CausalCascade dataset in your research, please cite:
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  ```bibtex
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+ @misc{causalcascade,
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  title = {A Plausible Future Is Not a Causal Future: Can Video Models Generate How the World Unfolds?},
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+ author = {Jialiang Li and Zhengning Xu and Zongyang Ma and Shaobo Cui},
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  year = {2026},
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+ eprint = {2610.15562},
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  archivePrefix = {arXiv},
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+ primaryClass = {cs.CV},
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+ url = {https://arxiv.org/abs/2610.15562}
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  }
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  ```