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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.07712 |
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| _version_ | 1866911578097451008 |
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| author | Ding, Ziyi Lai, Xianxin Chen, Weiyu Zhang, Xiao-Ping Chen, Jiayu |
| author_facet | Ding, Ziyi Lai, Xianxin Chen, Weiyu Zhang, Xiao-Ping Chen, Jiayu |
| contents | In this work, CausalVAE is introduced as a plug-in structural module for latent world models and is attached to diverse encoder-transition backbones. Across the reported benchmarks, competitive factual prediction is preserved and intervention-aware counterfactual retrieval is improved after the plug-in is added, suggesting stronger robustness under distribution shift and interventions. The largest gains are observed on the Physics benchmark: when averaged over 8 paired baselines, CF-H@1 is improved by +102.5%. In a representative GNN-NLL setting on Physics, CF-H@1 is increased from 11.0 to 41.0 (+272.7%). Through causal analysis, learned structural dependencies are shown to recover meaningful first-order physical interaction trends, supporting the interpretability of the learned latent causal structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07712 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamics Ding, Ziyi Lai, Xianxin Chen, Weiyu Zhang, Xiao-Ping Chen, Jiayu Machine Learning In this work, CausalVAE is introduced as a plug-in structural module for latent world models and is attached to diverse encoder-transition backbones. Across the reported benchmarks, competitive factual prediction is preserved and intervention-aware counterfactual retrieval is improved after the plug-in is added, suggesting stronger robustness under distribution shift and interventions. The largest gains are observed on the Physics benchmark: when averaged over 8 paired baselines, CF-H@1 is improved by +102.5%. In a representative GNN-NLL setting on Physics, CF-H@1 is increased from 11.0 to 41.0 (+272.7%). Through causal analysis, learned structural dependencies are shown to recover meaningful first-order physical interaction trends, supporting the interpretability of the learned latent causal structure. |
| title | CausalVAE as a Plug-in for World Models: Towards Reliable Counterfactual Dynamics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.07712 |