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…tion for attention, fitness, NEAT, bayesian, graph traversal; add delegation tests
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Stabilize core by extracting initialization responsibilities into a small Orchestrator facade.\n\nChanges:\n- Add src/training/core/orchestrator.py exposing idempotent initialization methods for: components, losing-streak systems, real-time learning, attention+communication, fitness evolution, NEAT architect, Bayesian inference, graph traversal.\n- Delegate existing ContinuousLearningLoop initialization points to the Orchestrator when present.\n- Add lightweight tests ests/test_orchestrator_delegation.py (fake modules injected) to validate delegation paths.\n\nTesting:\n- Focused test suite (tests/) passes locally (4 tests).\n\nNotes for reviewers:\n- This is an incremental refactor: behavior preserved, small surface area, safe delegation.\n