Part 4: Learning Paradigm

The GDS learning paradigm is fundamentally different from the backpropagation and gradient descent methods that power traditional Large Language Models. It is a form of autonomous, Hebbian-style learning that modifies the geometry of the conceptual space in response to experience.

Core Principles

  1. No Backpropagation: The model does not compute gradients across a massive neural network. Learning is a local, lightweight process.
  2. Learning by Modifying Costs: Instead of adjusting neuron weights, GDS learns by adjusting the “cost” of traversing specific edges in the semantic graph. This is done by writing small delta values to the dynamic Context Overlay.
  3. Reinforcement and Penalization: Paths that lead to successful or “coherent” outcomes are reinforced (their edges receive a negative delta, making them cheaper and more attractive to the Reasoner). Paths that are evaluated as poor alternatives are penalized (their edges receive a positive delta, making them more expensive).
  4. Internal Evaluation: The model does not strictly require external, supervised labels to learn. As demonstrated in our simulation, it can employ internal heuristics (such as a “coherence score” based on concept mass) to decide which paths are “better” and thus worthy of reinforcement.
  5. Stability and Explainability: Because learning only affects the overlay, the foundational knowledge graph remains stable. The changes are auditable (one can inspect the deltas in the overlay) and their effect is directly observable in the Reasoner’s behavior and cost calculations.

Case Study: The Simulation

Our simulation provided a perfect, concrete example of this paradigm in action:

  1. Initial State: The Reasoner initially chose the cheapest, most obvious path: king -> power.
  2. Internal Evaluation: An internal metric, the “coherence score” (sum of concept masses), evaluated the alternative path king -> crown -> power as being semantically richer, despite its higher initial cost.
  3. Autonomous Learning: This internal evaluation triggered a learning event. The learn_edges function was called to apply a strong negative delta (reinforcement) to the king -> crown and crown -> power edges, and a positive delta (penalty) to the king -> power edge.
  4. Behavioral Change: When the query was run again, the Reasoner, factoring in the new deltas from the Overlay, found that the path through crown was now the new cheapest path.

This demonstrates a complete, autonomous loop of Reason -> Evaluate -> Self-Reinforce -> Reason Differently. The model improved its reasoning based on its own internal principles, a process far more akin to neuroplasticity than to traditional model training.

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