CtrlWAM: Controllable World Action Models with Aligned Intent and Foresight

arXiv:2610.00859 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/NVlabs/alpasim

Project page: https://ctrl-wam.github.io

Terminology

• Consistency — 17× in this paper · explained in 3 episodes
• Actor — 11× in this paper · explained in 3 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• World Model — 9× in this paper · explained in 7 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Ablation — 7× in this paper · explained in 1 episode
• Transformer — 6× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Action Decoder — 3× in this paper · explained in 1 episode
• Flow Matching — 3× in this paper · explained in 8 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Agent Interactions — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Cosmos 3: Omnimodal World Models for Physical AI
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising
• GAIA-1: A Generative World Model for Autonomous Driving
• Unified Video Action Model
• Flow Matching for Generative Modeling
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality
• Wan: Open and Advanced Large-Scale Video Generative Models
• World Action Models are Zero-shot Policies
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• EWMBench: Evaluating Scene, Motion, and Semantic Quality in Embodied World Models
• Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

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