CausalWM: Causal Chain-of-Thought Reasoning for Embodied World Model

arXiv:2609.23184 · cs.CV · Submitted 2026-09-19 · Read on arXiv

cs.CV

Submitted: 2026-09-19

Updated: 2026-09-22

Code: https://github.com/AetherLabsAI/CausalWM

Project page: https://fysics-ai.github.io/Fysiverse-3D-project-page

Terminology

• World Model — 42× in this paper · explained in 7 episodes
• Consistency — 13× in this paper · explained in 3 episodes
• Benchmark — 12× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Optical Flow — 9× in this paper · explained in 2 episodes
• Accuracy — 6× in this paper · explained in 1 episode
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Foundation Model — 4× in this paper · explained in 3 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• GRPO — 3× in this paper · explained in 6 episodes
• Rectified Flow — 3× in this paper · explained in 3 episodes
• Action-Conditioned Dynamics — 2× in this paper · explained in 1 episode
• Actor — 2× in this paper · explained in 3 episodes
• Causal Reasoning — 2× in this paper · explained in 2 episodes
• Chain-of-Thought (CoT) — 2× in this paper · explained in 4 episodes
• Chain-of-Thought (CoT) Reasoning — 2× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Visual Question Answering — 2× in this paper
• alpha — 2× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Flow Matching — 1× in this paper · explained in 8 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Verifiable Rewards — 1× in this paper · explained in 2 episodes

Sources

• Cosmos World Foundation Model Platform for Physical AI
• ABot-PhysWorld: Interactive World Foundation Model for Robotic Manipulation with Physics Alignment
• Qwen3-VL Technical Report
• CoPhy: Counterfactual Learning of Physical Dynamics
• RT-1: Robotics Transformer for Real-World Control at Scale
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
• BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• FlowWAM: Optical Flow as a Unified Action Representation for World Action Models
• ComPhy: Compositional Physical Reasoning of Objects and Events from Videos
• DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
• World Models
• LTX-Video: Realtime Video Latent Diffusion
• LTX-2: Efficient Joint Audio-Visual Foundation Model
• Dream to Control: Learning Behaviors by Latent Imagination
• Mastering Atari with Discrete World Models
• Mastering Diverse Domains through World Models
• Vid2World: Crafting Video Diffusion Models to Interactive World Models
• Galaxea Open-World Dataset and G0 Dual-System VLA Model
• WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL

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