✅ Complete IRCP Implementation Summary

All placeholders have been removed and the complete SentenceTransformer-based IRCP system is ready for local training using `all-MiniLM-L6-v2`. ### ✅ **Fixed All Placeholders** 1. **main.py**: Complete `generate_response` method with coordinate-guided response generation 2. **base_models.py**: Complete abstract methods with full implementations 3. **measure_theory.py**: Complete abstract methods with mathematical rigor 4. **All modules**: No remaining `TODO`, `FIXME`, or placeholder implementations ### ✅ **Created Complete SentenceTransformer Model** - **File**: `ircp/models/sentence_transformer_icp.py` - **Features**: - Uses `sentence-transformers/all-MiniLM-L6-v2` as base encoder - Custom IRCP heads for coordinate prediction - Inverse attention mechanism - Ring topology integration - Measure-preserving transformations - Multi-component loss function - Complete response generation ### ✅ **Model Registry Integration** - Automatically registered as `"sentence_transformer_icp"` - Accessible through `ModelRegistry.get_model()` - Full compatibility with existing IRCP framework ### ✅ **Complete Training Pipeline** - **File**: `train_sentence_transformer_ircp.py` - **Features**: - Automatic dependency installation - Model validation and testing - Comprehensive configuration - Progress tracking and visualization - Checkpoint management - Evaluation integration