arXiv AI By Jianjie Fang, Xvyuan Liu, Ziyou Wang, Rongze Tang, Zhaolu Wang, Zhuohang Li, Xin Zhang, Haisheng Su, Chen Gao, Wei Wu, Xinlei Chen, Yong Li

CAER: Causal Action Effect Reweighting for World Model Training

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arXiv Machine Learning
Oct 9

CausalDreamer: Learning Predictive World Models with Latent Disentanglement

CausalDreamer enhances generative world models by freezing the original video tokenizer and re-encoding its latent space into a factored representation that separates controllable versus uncontrollable dynamics and reward-relevant versus reward-irrelevant information. The pretrained dynamics model is fine‑tuned to predict this factored representation, enabling more accurate model‑predictive planning. On the MMBench2 benchmark, CausalDreamer improves normalized scores by 14% on clean tasks and 25% on manipulated variants compared to the baseline, while both models perform poorly on entirely new environments.

By Prince Jha, Nils Lukas, Kun Zhang, Salem Lahlou
arXiv Computer Vision
Oct 16

Can World and Action Modeling Benefit Each Other under Unified Causal Training?

The paper introduces OneWM, a unified causal training framework that treats actions as both conditions for world prediction and targets for policy learning within a shared video-action architecture. Experiments on LIBERO, LIBERO-Plus, and RoboCasa365 show that learning forward dynamics conditioned on specified actions improves policy performance and generalization, while action learning enhances multi‑view world‑prediction fidelity. These findings demonstrate reciprocal benefits between world modeling and action generation under unified causal training.

By Yue Su, Bokai Lin, Xinkai Wang, Chubin Zhang, Xiangnan Wu, Kuan Zhang, Weiyang Jin, Jiwen Yu, Yiming Li, Cewu Lu, Lixin Yang, Xihui Liu
arXiv AI
Sep 17

CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models

The paper introduces CSWAM, a Causal Semantic World Action Model that enhances FastWAM by integrating a causal semantic expert based on V-JEPA 2.1. This expert provides temporally grounded, appearance‑agnostic representations of semantic state changes and motion, leveraging sparse observation history and causal attention to improve action‑only inference. Experiments on simulation and real‑robot tasks show that CSWAM significantly boosts out‑of‑distribution generalization, raising success rates from 10.16% to 45.18% on RoboTwin 2.0 and from 27.5% to 70.0% across real‑robot tasks.

By Tianbin Liu, Jian Zhu, Taiyi Su, Jianjun Zhang, Chong Ma, Zitai Huang, Yi Xu