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DLM Integration Pipeline: IRCP, RCP, and TPO

This document defines the complete pipeline for integrating **IRCP** (Inverse Ring Contextual Propagation), **RCP** (Ring Contextual Propagation), and **TPO** (Topological Preference Optimization) directly within the **DLM** (Divergent Language Matrix) framework.

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# DLM Integration Pipeline: IRCP, RCP, and TPO ## Comprehensive Architecture and Process Definition ## Table of Contents 1. [Executive Summary](#executive-summary) 2. [Current State Analysis](#current-state-analysis) 3. [Architecture Overview](#architecture-overview) 4. [Detailed Pipeline Definition](#detailed-pipeline-definition) 5. [Component Responsibilities](#component-responsibilities) 6. [Data Flow Diagrams](#data-flow-diagrams) 7. [Integration Points](#integration-points) 8. [Implementation Roadmap](#implementation-roadmap) This document defines the complete pipeline for integrating **IRCP** (Inverse Ring Contextual Propagation), **RCP** (Ring Contextual Propagation), and **TPO** (Topological Preference Optimization) directly within the **DLM** (Divergent Language Matrix) framework. ### Key Principles: - **IRCP**: Inverse pattern learning (P(u|v)) - How individuals construct responses - **RCP**: Forward context propagation (dC/dt = A(C)C) - How context flows - **TPO**: Preference optimization (Q(P)) - Which paths are optimal - **DLM**: Unified coordinate system and embedding framework ### Integration Goal: Create a unified system where DLM serves as the foundation, with IRCP, RCP, and TPO providing specialized capabilities for: 1. Coordinate calculation 2. Embedding generation 3. Similarity computation 4. Context propagation 5. Preference generation

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