# Detailed Mechanism

1. **Collection of Empirical Data:** When Strant executes a task (e.g., predicting stock prices, analyzing cash flow, or evaluating real estate values), the entire process and its outcomes are recorded. This recorded information comprises: \+ Input Data: The data utilized to generate the prediction. \+ Predicted Result: The result generated by Strant AI, for example: "Stock A will increase by 5% next week." \+ Actual Result: The actual outcome, for example: Stock A only increased by 3%, resulting in a discrepancy. 2. **Evaluation and Comparison of Results:** The actual results are compared with the predicted results to assess the level of accuracy: \+ Discrepancy Identification: Strant AI calculates the magnitude of the difference between the prediction and the actual outcome. \+ Identification of Unaccounted Factors: If the AI predicts stock prices based on news and charts but does not account for an unexpected economic event, the system will register this omission. 3. **Feedback and Retraining Loop:** The actual results and newly identified factors are fed back into the system: * **Adjustment of Weights within Fusion Modules:** If a Data Learning (DL) Module produces an inaccurate prediction, its weight will be reduced in future similar tasks. * **Retraining of DL Modules:** The modules are updated to incorporate the new empirical data, thereby enhancing their analytical capabilities.