Add a divergence report (count + fraction outside tolerance, per-feature breakdown, worst offenders) so we can tell a few branch-flip elements from a pervasive regression. The CI tolerance gate failed with max|d|=0.85 / maxrel=345 — far beyond FP rounding — so we need to see WHICH feature elements diverge structurally on the Azure runner. |
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|---|---|---|
| .. | ||
| cir_verify_helper.py | ||
| expected_calibration_features.sha256 | ||
| expected_cir_features.sha256 | ||
| expected_features.sha256 | ||
| expected_features_reference.npz | ||
| generate_reference_signal.py | ||
| sample_csi_data.json | ||
| sample_csi_meta.json | ||
| verify.py | ||