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parameterize dual-axis hard-case weighting for low-risk experiments\n\nConstraint: Keep the training pipeline behavior stable while exposing humming_like and confused controls through config only\nRejected: Add a brand-new sampler framework first | The smallest useful step is config-driven control on the existing dataset weighting path\nConfidence: high\nScope-risk: narrow\nDirective: Run weight-search experiments through training.sample_type_weights and training.pair_type_weights before attempting broader training-stack refactors\nTested: py_compile passed, train.py dry-run on synthetic_v2 passed, and custom SongPairDataset weighting instantiation produced expected hard_weight output\nNot-tested: End-to-end retraining and metric improvements from new dual-axis weight combinations
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