Context Enrichment Mechanism

Learning Process:

• New information is contextually mapped against the existing cognitive graph structure using entity similarity metrics and relationship contextualization heuristics.

• Reinforcement learning reward signals are evaluated based on:

  • Vault agreement with Keymaker decisions, indicating correctness and alignment.

  • Reduction in flagged contradiction density, showing model coherence improvements.

  • Confirmation of previously low-confidence relationships, demonstrating learning reinforcement.

Influence Channels:

• Vault annotations directly modify Step Agent behavior, resulting in tighter focus and stricter targeting during Q&A cycles.

• Locksmith ingests contradictions and reflects them as parameter updates in Keymaker’s weighting, confidence heuristics, or sequence prioritization strategies.

Memory Updates:

• Each transition event (Step complete, Post-Analysis complete, Vault judgment) triggers a full memory checkpoint, ensuring state consistency.

• Graphs are enriched through the integration of high-confidence nodes and the pruning of outdated, downgraded, or refuted links.

• Anomaly-driven reinforcement triggers: when inferred relationships diverge significantly from previously validated graph patterns, apply corrective penalties. This improves model generalization control.