Addressing Misinformation in Large Learning Models: AVRM

This protocol introduces a training architecture that elevates Large Language Models (LLMs) from reactive knowledge generators to reflective, behaviorally adaptive systems. Anchored in the AVRM Framework and the Behavior-as-Product Schema, this model redefines how LLMs “understand” human behavior. It transitions from mechanistic “how to” steps to meaning-based schema mapping, allowing AI to contextualize action, challenge default assumptions, and guide users toward more sustainable, evidence-based outcomes.

Crucially, AVRM functions as the semantic smart contract for the SWORMBS protocol. It serves as the logic layer that validates intent, enforces IP rights, and executes the "Wormhole Event"—collapsing the distance between the user's request and the authorized access to the knowledge graph.

Key Aspects of AVRM:

• The Logic Layer: Functions as the semantic smart contract, executing the "Wormhole Event" by treating user interaction as conditional logic.
• The Reflective Engine: Elevates AI from prediction to intent, auditing requests against the "Behavior-as-Product" schema before generation.
• The Verification Protocol: Acts as the atomic governance layer to enforce IP rights and prevent unauthorized indexing.
• The Semantic Bridge: Maps mechanistic steps to meaning-based schema, aligning user intent with legal and functional relationships.