Part 3: The Simulation - A Proof of Concept in Action

To validate the core principles of the GDS learning paradigm, a controlled experiment was designed and executed. The goal was not merely to test if the code ran, but to determine if the model could exhibit autonomous, non-trivial learning behavior.

Step 1: Initial Reasoning

The Reasoner is asked to find the cheapest path from `king` to `power`. The direct path has a cost of 1.0, while the path through `crown` has a total cost of 1.6 (0.8 + 0.8). As expected, the model chooses the most direct and obvious route.

Step 2: Internal Evaluation & Learning

An internal heuristic evaluates the path through `crown` as being semantically richer. This triggers a learning event. A strong reinforcement (negative cost delta) is applied to the `king -> crown` and `crown -> power` edges, and a penalty (positive cost delta) is applied to the direct `king -> power` edge.

Step 3: Verification & New Path

When the query is run again, the Reasoner factors in the new deltas from the Context Overlay. The path through `crown` is now the new cheapest path, and the model changes its preference, demonstrating a full, autonomous learning loop.