Li Auto’s Foundation Model team has released MachEmbodied-Dex-1.0, a unified World Action Tactile Model that jointly learns future video, tactile states and robot actions. ME-Dex-1.0 elevates tactile sensing from an auxiliary condition to a future world state that the model must predict. It uses a unified tactile representation to align heterogeneous sensors, while a three-expert architecture connects predicted visual changes and contact dynamics to action generation. An Agentic Tactile Data Engine expands paired visual-tactile-action data through simulation. Results: • RoboTwin: 78.9% Clean-only success rate, +7.6 points vs. the strongest baseline • DexJoCo: #1# on 7 of 11 dexterous manipulation tasks • ManiFeel: plug-insertion success rate jumped from 58% → 88%