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Week 3: Training Pipeline Integration - Progress Summary

Week 3 focuses on integrating the training pipeline components, building on Week 2's core modules (DLMConfig, DLMCoordinate, Logging). The goal is to create a complete training pipeline that uses unified data loading, IRCP training, evaluation metrics, and coordinate explainability.

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Week 3 focuses on integrating the training pipeline components, building on Week 2's core modules (DLMConfig, DLMCoordinate, Logging). The goal is to create a complete training pipeline that uses unified data loading, IRCP training, evaluation metrics, and coordinate explainability. **Key Deliverables:** - [packages/dlm/core/data_loader.py](packages/dlm/core/data_loader.py) - 560+ lines - `DLMDataLoader` - Main loader class - `ConversationNode` - Message representation with coordinates/embeddings - `ConversationGraph` - Tree structure with traversal methods **Features:** - ✅ Batch loading (coordinates and embeddings) - ✅ Caching system for performance - ✅ IRCP database schema compatibility - ✅ Integration with DLMConfig and DLMCoordinate - ✅ Context manager support - ✅ Tree traversal (get_children, get_ancestors, get_depth) **Testing:** - 10/10 tests passing - Comprehensive test coverage in [packages/dlm/tests/verify_week3_phase1.py](packages/dlm/tests/verify_week3_phase1.py) **Documentation:** - [PHASE_3_1_DATA_LOADING.md](PHASE_3_1_DATA_LOADING.md) - Complete tracking document

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