The language model serves as the foundation, processing inputs and generating a structured data representation that captures all relevant causal relationships.
The structured data, initially generated, is iteratively refined through subsequent interactions, adding depth and accuracy to the model.
While the model may simplify certain aspects of reality to manage complexity, these simplifications are carefully considered to ensure they do not undermine the overall accuracy of the simulation.
At each iteration, the data structure is evaluated for consistency and completeness, ensuring that it captures all necessary information.
Edge cases are analyzed and refined to enhance the robustness of the model, ensuring it can handle a wide range of scenarios.
The simulation is validated by testing it across various scenarios, confirming that it accurately represents causal relationships.