WALL-WM: Carving World Action Modeling at the Event Joints
- Published
- Source
- arXiv
- Paper number
- 351
- Field
- Robotics
- arXiv ID
- 2606.01955
Key points
- It shifts the paradigm from chunk-centric optimization to event-grounded optimization, aligning natural boundaries across language, video, and action.
- It builds a data ecosystem made up of event captions and cluster-balanced sampling.
- It uses dual inference modes: Event mode for variable-length event execution and Unified mode for fixed chunks with Staircase Decoding.
- It provides a scalable training recipe built on large-scale pretraining infrastructure with the Muon optimizer.
- It demonstrates strong generalization across language, scene, and task settings.
- It outperforms the existing WAM on both physical video prediction and executable control.
Paper links
External research summaries. These are not HDATF publications or measured product results.