In research on mobile agents driven by large multimodal models, constructing an internal world model constitutes an important foundation for enabling long-horizon reasoning and planning in graphical user interface (GUI) environments. However, the dynamic and nondeterministic nature of open environments often causes agents to produce state-transition hallucinations, namely, mismatches between internal predictions and actual environmental feedback, which can in turn lead to planning failures and cascading errors. Existing self-reflection mechanisms are often constrained by the model's own cognitive blind spots and lack the capacity for autonomous evolution over prolonged interactions. To address these limitations, this paper proposes a dual-layer cognitive architecture for agents based on the decoupling of System-1 and System-2. System-1 serves as the intuitive execution layer and uses a parameterized world model for rapid state rollouts and decision-tree planning; System-2 serves as the supervisory cognitive layer, providing cognitive oversight through real-time verification of state-transition consistency. When the dynamics residual exceeds a threshold, System-2 intervenes and uses a counterfactual causal attribution mechanism to identify the root cause of the failure, guiding the agent to perform state backtracking and dynamic replanning. Furthermore, by constructing an episodic causal memory bank, the architecture creates a closed-loop data flywheel that drives the continual self-calibration of world model parameters during interaction. Experiments on the multi-domain, cross-application GUI task benchmark developed in this work and in a cross-domain transfer setting show that the architecture can reduce error cascades and facilitate the partial internalization of causal knowledge. As the interaction scale increases, the frequency of System-2 interventions decreases while the independent task success rate increases, providing preliminary evidence of continual self-calibration within the scope of the tested GUI tasks.