Enforce benchmark result and data contracts - #2
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Stacked on #1. This focused correctness PR verifies dataset size and SHA-256 before training, rejects dropped/extra/non-finite predictions, preserves final partial batches, makes metrics strict, records clean source and hardware provenance, distinguishes smoke from full runs, binds Optuna studies to model revisions, and writes content-addressed result bundles atomically. Tests cover corrupt data, truncated model output, non-finite metrics, and partial batches.