Ring Contextual Propagation (RCP) Framework - Complete Implementation

All components of the Ring Contextual Propagation (RCP) Framework have been successfully implemented and thoroughly tested. The RCP Framework implements a sophisticated system for modeling and propagating contextual information within hierarchical conversation structures: #### 1. **3D Coordinate System** (`coordinate_system.py`) - **Purpose**: Assigns spatial coordinates (x, y, z) to each message - **Coordinates**: - `x`: Depth level in conversation tree - `y`: Sibling order among messages at same level - `z`: Homogeneity relationships between sibling messages - **Features**: - Multiple homogeneity calculation methods (similarity-based, count-based, hybrid) - Coordinate normalization and validation - Confidence scoring for coordinate quality - Support for embeddings-based similarity computation #### 2. **Ring Structure** (`ring_structure.py`) - **Purpose**: Organizes messages in circular topology while preserving hierarchical relationships - **Construction Methods**: - Hierarchical preserving (maintains conversation tree structure) - Temporal-based (chronological ordering) - Similarity-based (content similarity clustering) - Hybrid (weighted combination of factors) - **Features**: - Ring connectivity validation - Neighbor traversal and path finding - Ring distance computation - Connection strength analysis #### 3. **Contextual Attention Mechanism** (`attention_mechanism.py`) - **Purpose**: Computes attention weights based on coordinate distances - **Formula**: `w(mᵢ, mⱼ) = softmax(ψ(cᵢ, cⱼ))` - **Distance Function**: `ψ(cᵢ, cⱼ) = α|xᵢ - xⱼ| + β|yᵢ - yⱼ| + γ|zᵢ - zⱼ|` - **Features**: - Learnable coordinate weights (α, β, γ) - Semantic similarity integration - Temporal decay factors - Multi-scale and adaptive attention variants - Comprehensive attention pattern analysis