Guangxiang Technology, in collaboration with Professor Li Shengbo's research team from Tsinghua University, has introduced the inaugural physics-native world model, Phi-WM 1.0 ActEffect. This innovative model diverges from the conventional method of deploying world models alongside robots, intervening solely during the training phase. It empowers the robot to generate three action plans and forecast the consequences of executing each plan, refining strategies by juxtaposing predictions with actual outcomes. Post-training, the model is no longer integrated into the deployment process, and robots no longer need to reason about future scenarios while executing tasks. Test results demonstrate the model's exceptional performance across various benchmarks, and ablation experiments have confirmed the efficacy of its training methodology. This approach diminishes latency, computational demands, and expenses in industrial deployment, catering to the requirements for extensive deployment in settings such as automotive manufacturing. Presently, related technologies have undergone validation in certain real-world situations, yet further testing is imperative during sustained operation on production lines.