Author

Weilin Chen

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Preprint Aug 2026

IADD-TR: Intervention-Aware Dynamics Decoupling with Targeted Regularization for Model-Based Reinforcement Learning

IADD-TR is proposed, a unified framework combining Intervention-Aware Dynamics Decoupling (IADD) and Targeted Regularization (TR), which achieves competitive returns with improved sample efficiency on five MuJoCo tasks.

Ze-Feng Liang, Jie Qiao, Rui-Chu Cai et al. · 0 citations

Jul 2026

CDFM: Towards a General-Purpose Causal Discovery Foundation Model

The Causal Discovery Foundation Model (CDFM) is formulated as a unified, general-purpose framework for zero-shot structural inference, and a principled variational framework that treats unknown causal mechanisms as latent variables and mathematically decomposes the intractable marginal likelihood into distinct, tractab...

Jie Qiao, Ruichu Cai, Zijian Li et al. · arXiv.org · 2 citations