LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction
Large-scale vision--language--action (VLA) policies have advanced generalist robot control, yet most remain stimulus-to-action black boxes: actions are exposed, but their explanatory state is not. They provide no native account of three explanatory signals: task progress, the next semantic transition, or local command reliability. Prior work shows that progress and event structure aid long-horizon control and that uncertainty supports monitoring; however, such capabilities are typically added or extracted only after action pretraining. The field therefore lacks a VLA foundation model whose explanatory state is jointly pretrained with control. Drawing on biological sensorimotor organizatio...
| Authors | Jin Lou, Zhiyuan Jing, Xupeng Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 August 2026 |
| Last updated at source | 8 October 2026 |
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