diff --git a/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/btc_vol.py b/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/btc_vol.py
index c53afefc07..bd6b204d34 100644
--- a/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/btc_vol.py
+++ b/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/btc_vol.py
@@ -212,6 +212,11 @@ def aggregate_verdicts_recentered(rows: list[dict]) -> list[dict]:
     The re-centered DM measures the variance differential (biases annihilated
     by centering), so this is the **precision** jambe that the §C amended
     bareme (#11010) requires for the BEATS verdict.
+
+    The uncentered sanity leg (`dm_uncentered_vs_har_raw_p_median`, #14390) is
+    aggregated alongside: it reproduces the #11011 keeper measure, and a leg
+    that exists only as per-row fields can never blush (the #14362 defect was
+    exactly a silent absence on the one surface where it would have been read).
     """
     from collections import defaultdict
 
@@ -228,11 +233,13 @@ def aggregate_verdicts_recentered(rows: list[dict]) -> list[dict]:
         har_vars_debiased = np.array([r["har_variance_debiased"] for r in sub])
         har_biases = np.array([r["har_bias_oos"] for r in sub])
         dm_pvals = np.array([r["dm_centered_pvalue"] for r in sub])
+        dm_unc_pvals = np.array([r["dm_uncentered_vs_har_raw_pvalue"] for r in sub])
         verdicts = [r["dm_centered_verdict"] for r in sub]
 
         edge = float(np.mean(reductions))
         sigma = float(np.std(reductions)) if len(reductions) > 1 else 0.0
         dm_p_med = float(np.median(dm_pvals))
+        dm_unc_p_med = float(np.median(dm_unc_pvals))
 
         # Variance ratio: var_DL / var_HAR_debiased < 1 means DLinear is more precise.
         var_ratio = float(np.mean(dl_vars) / np.mean(har_vars_debiased)) if np.mean(har_vars_debiased) > 0 else float("nan")
@@ -253,6 +260,7 @@ def aggregate_verdicts_recentered(rows: list[dict]) -> list[dict]:
             "edge_reduction_pct": edge,
             "edge_std_pct": sigma,
             "dm_centered_p_median": dm_p_med,
+            "dm_uncentered_vs_har_raw_p_median": dm_unc_p_med,
             "var_ratio_dl_over_har_debiased": var_ratio,
             "har_bias_share_of_mse_debiased": bias_share,
             "mean_dl_mse": float(np.mean([r["dlinear_mse_logrv"] for r in sub])),
diff --git a/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/results/m4_dlinear_vol_btc_sc_debiased_recentered.json b/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/results/m4_dlinear_vol_btc_sc_debiased_recentered.json
index 55cb7978af..504a5ff38a 100644
--- a/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/results/m4_dlinear_vol_btc_sc_debiased_recentered.json
+++ b/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/results/m4_dlinear_vol_btc_sc_debiased_recentered.json
@@ -8,23 +8,23 @@
       "decompose": false,
       "har_mse_logrv_raw": 0.8876766414630621,
       "har_mse_logrv_debiased": 0.8363349953763609,
-      "har_bias_oos": -0.2265869503892514,
-      "har_bias_sq_raw": 0.05134164608670107,
+      "har_bias_oos": -0.22658695038925147,
+      "har_bias_sq_raw": 0.05134164608670111,
       "har_variance_raw": 0.8363349953763609,
-      "har_bias_sq_debiased": 1.413373028026784e-31,
+      "har_bias_sq_debiased": 6.535436881595849e-32,
       "har_variance_debiased": 0.8363349953763609,
-      "dlinear_mse_logrv": 0.7500402773390882,
-      "dlinear_bias_sq": 1.3780518952009237e-05,
-      "dlinear_variance": 0.7500264968201361,
-      "mse_reduction_pct_vs_debiased_har": 10.318200065087444,
-      "dm_centered_stat": -6.228784907758055,
+      "dlinear_mse_logrv": 0.7500402759916452,
+      "dlinear_bias_sq": 1.3780499221591187e-05,
+      "dlinear_variance": 0.7500264954924235,
+      "mse_reduction_pct_vs_debiased_har": 10.318200226200268,
+      "dm_centered_stat": -6.228784955833262,
       "dm_centered_pvalue": 5.77933256806773e-10,
       "dm_centered_verdict": "BEATS baseline",
-      "dm_centered_mean_loss_diff": -0.08630849855622481,
-      "dm_uncentered_vs_har_raw_stat": -9.308815739868166,
+      "dm_centered_mean_loss_diff": -0.08630849988393753,
+      "dm_uncentered_vs_har_raw_stat": -9.308815767716679,
       "dm_uncentered_vs_har_raw_pvalue": 0.0,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.13763636412397387,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.13763636547141703,
       "n_predictions": 1890,
       "n_rv_days": 2278
     },
@@ -36,23 +36,23 @@
       "decompose": false,
       "har_mse_logrv_raw": 0.8876766414630621,
       "har_mse_logrv_debiased": 0.8363349953763609,
-      "har_bias_oos": -0.2265869503892514,
-      "har_bias_sq_raw": 0.05134164608670107,
+      "har_bias_oos": -0.22658695038925147,
+      "har_bias_sq_raw": 0.05134164608670111,
       "har_variance_raw": 0.8363349953763609,
-      "har_bias_sq_debiased": 1.413373028026784e-31,
+      "har_bias_sq_debiased": 6.535436881595849e-32,
       "har_variance_debiased": 0.8363349953763609,
-      "dlinear_mse_logrv": 0.7534806623520199,
-      "dlinear_bias_sq": 1.9665559248522274e-05,
-      "dlinear_variance": 0.7534609967927715,
-      "mse_reduction_pct_vs_debiased_har": 9.906835596070628,
-      "dm_centered_stat": -5.901210747033868,
-      "dm_centered_pvalue": 4.266757391846454e-09,
+      "dlinear_mse_logrv": 0.7534806566182599,
+      "dlinear_bias_sq": 1.96655395674364e-05,
+      "dlinear_variance": 0.7534609910786926,
+      "mse_reduction_pct_vs_debiased_har": 9.906836281652371,
+      "dm_centered_stat": -5.901210998180161,
+      "dm_centered_pvalue": 4.266750952552911e-09,
       "dm_centered_verdict": "BEATS baseline",
-      "dm_centered_mean_loss_diff": -0.0828739985835896,
-      "dm_uncentered_vs_har_raw_stat": -9.150634683670352,
+      "dm_centered_mean_loss_diff": -0.08287400429766853,
+      "dm_uncentered_vs_har_raw_stat": -9.150634866565728,
       "dm_uncentered_vs_har_raw_pvalue": 0.0,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.13419597911104217,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.1341959848448022,
       "n_predictions": 1890,
       "n_rv_days": 2278
     },
@@ -64,23 +64,23 @@
       "decompose": false,
       "har_mse_logrv_raw": 0.8876766414630621,
       "har_mse_logrv_debiased": 0.8363349953763609,
-      "har_bias_oos": -0.2265869503892514,
-      "har_bias_sq_raw": 0.05134164608670107,
+      "har_bias_oos": -0.22658695038925147,
+      "har_bias_sq_raw": 0.05134164608670111,
       "har_variance_raw": 0.8363349953763609,
-      "har_bias_sq_debiased": 1.413373028026784e-31,
+      "har_bias_sq_debiased": 6.535436881595849e-32,
       "har_variance_debiased": 0.8363349953763609,
-      "dlinear_mse_logrv": 0.7516759086489415,
-      "dlinear_bias_sq": 3.686069799782585e-05,
-      "dlinear_variance": 0.7516390479509438,
-      "mse_reduction_pct_vs_debiased_har": 10.122628754680028,
-      "dm_centered_stat": -6.099898908625079,
-      "dm_centered_pvalue": 1.2840908336642087e-09,
+      "dlinear_mse_logrv": 0.7516759112622348,
+      "dlinear_bias_sq": 3.686066472460918e-05,
+      "dlinear_variance": 0.7516390505975102,
+      "mse_reduction_pct_vs_debiased_har": 10.122628442210353,
+      "dm_centered_stat": -6.099898532645422,
+      "dm_centered_pvalue": 1.2840939422886777e-09,
       "dm_centered_verdict": "BEATS baseline",
-      "dm_centered_mean_loss_diff": -0.0846959474254172,
-      "dm_uncentered_vs_har_raw_stat": -9.262350386794445,
+      "dm_centered_mean_loss_diff": -0.08469594477885087,
+      "dm_uncentered_vs_har_raw_stat": -9.262349857973373,
       "dm_uncentered_vs_har_raw_pvalue": 0.0,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.13600073281412045,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.13600073020082734,
       "n_predictions": 1890,
       "n_rv_days": 2278
     },
@@ -92,23 +92,23 @@
       "decompose": false,
       "har_mse_logrv_raw": 0.8876766414630621,
       "har_mse_logrv_debiased": 0.8363349953763609,
-      "har_bias_oos": -0.2265869503892514,
-      "har_bias_sq_raw": 0.05134164608670107,
+      "har_bias_oos": -0.22658695038925147,
+      "har_bias_sq_raw": 0.05134164608670111,
       "har_variance_raw": 0.8363349953763609,
-      "har_bias_sq_debiased": 1.413373028026784e-31,
+      "har_bias_sq_debiased": 6.535436881595849e-32,
       "har_variance_debiased": 0.8363349953763609,
-      "dlinear_mse_logrv": 0.752382840589742,
-      "dlinear_bias_sq": 6.717596338348179e-05,
-      "dlinear_variance": 0.7523156646263586,
-      "mse_reduction_pct_vs_debiased_har": 10.038101388886567,
-      "dm_centered_stat": -6.044090732273397,
-      "dm_centered_pvalue": 1.8058008421917293e-09,
+      "dlinear_mse_logrv": 0.752382839345394,
+      "dlinear_bias_sq": 6.717598719471965e-05,
+      "dlinear_variance": 0.7523156633581991,
+      "mse_reduction_pct_vs_debiased_har": 10.038101537672409,
+      "dm_centered_stat": -6.044090646126228,
+      "dm_centered_pvalue": 1.805801952414754e-09,
       "dm_centered_verdict": "BEATS baseline",
-      "dm_centered_mean_loss_diff": -0.08401933075000244,
-      "dm_uncentered_vs_har_raw_stat": -9.333346721250683,
+      "dm_centered_mean_loss_diff": -0.08401933201816193,
+      "dm_uncentered_vs_har_raw_stat": -9.333346648451142,
       "dm_uncentered_vs_har_raw_pvalue": 0.0,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.13529380087332005,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.13529380211766828,
       "n_predictions": 1890,
       "n_rv_days": 2278
     },
@@ -118,25 +118,25 @@
       "seed": 0,
       "seq_len": 22,
       "decompose": false,
-      "har_mse_logrv_raw": 0.5219741587277243,
-      "har_mse_logrv_debiased": 0.404202867593261,
-      "har_bias_oos": -0.34317822065868814,
-      "har_bias_sq_raw": 0.11777129113446325,
-      "har_variance_raw": 0.404202867593261,
-      "har_bias_sq_debiased": 1.5615786875681597e-31,
-      "har_variance_debiased": 0.404202867593261,
-      "dlinear_mse_logrv": 0.3736949329946485,
-      "dlinear_bias_sq": 2.7489317423759556e-05,
-      "dlinear_variance": 0.3736674436772248,
-      "mse_reduction_pct_vs_debiased_har": 7.547678911895267,
-      "dm_centered_stat": -4.049040174742201,
-      "dm_centered_pvalue": 5.352813553383129e-05,
+      "har_mse_logrv_raw": 0.5219741587277245,
+      "har_mse_logrv_debiased": 0.4042028675932612,
+      "har_bias_oos": -0.34317822065868825,
+      "har_bias_sq_raw": 0.11777129113446332,
+      "har_variance_raw": 0.40420286759326113,
+      "har_bias_sq_debiased": 4.8568331222071824e-32,
+      "har_variance_debiased": 0.40420286759326113,
+      "dlinear_mse_logrv": 0.3736949335562163,
+      "dlinear_bias_sq": 2.7489304039379204e-05,
+      "dlinear_variance": 0.3736674442521769,
+      "mse_reduction_pct_vs_debiased_har": 7.54767877296314,
+      "dm_centered_stat": -4.049040043379695,
+      "dm_centered_pvalue": 5.352816531289939e-05,
       "dm_centered_verdict": "BEATS baseline",
-      "dm_centered_mean_loss_diff": -0.030535423916036263,
-      "dm_uncentered_vs_har_raw_stat": -6.4982458593752215,
-      "dm_uncentered_vs_har_raw_pvalue": 1.0392997573660523e-10,
+      "dm_centered_mean_loss_diff": -0.030535423341084222,
+      "dm_uncentered_vs_har_raw_stat": -6.4982458667403,
+      "dm_uncentered_vs_har_raw_pvalue": 1.0393019778121015e-10,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.14827922573307575,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.14827922517150813,
       "n_predictions": 1870,
       "n_rv_days": 2278
     },
@@ -146,25 +146,25 @@
       "seed": 7,
       "seq_len": 22,
       "decompose": false,
-      "har_mse_logrv_raw": 0.5219741587277243,
-      "har_mse_logrv_debiased": 0.404202867593261,
-      "har_bias_oos": -0.34317822065868814,
-      "har_bias_sq_raw": 0.11777129113446325,
-      "har_variance_raw": 0.404202867593261,
-      "har_bias_sq_debiased": 1.5615786875681597e-31,
-      "har_variance_debiased": 0.404202867593261,
-      "dlinear_mse_logrv": 0.3747704573335785,
-      "dlinear_bias_sq": 2.5898740393859288e-05,
-      "dlinear_variance": 0.3747445585931846,
-      "mse_reduction_pct_vs_debiased_har": 7.281593630181664,
-      "dm_centered_stat": -3.8833086283328817,
-      "dm_centered_pvalue": 0.00010662841412600876,
+      "har_mse_logrv_raw": 0.5219741587277245,
+      "har_mse_logrv_debiased": 0.4042028675932612,
+      "har_bias_oos": -0.34317822065868825,
+      "har_bias_sq_raw": 0.11777129113446332,
+      "har_variance_raw": 0.40420286759326113,
+      "har_bias_sq_debiased": 4.8568331222071824e-32,
+      "har_variance_debiased": 0.40420286759326113,
+      "dlinear_mse_logrv": 0.37477045743488374,
+      "dlinear_bias_sq": 2.5898735165782497e-05,
+      "dlinear_variance": 0.37474455869971796,
+      "mse_reduction_pct_vs_debiased_har": 7.281593605118732,
+      "dm_centered_stat": -3.8833085637806812,
+      "dm_centered_pvalue": 0.000106628442218204,
       "dm_centered_verdict": "BEATS baseline",
-      "dm_centered_mean_loss_diff": -0.029458309000076354,
-      "dm_uncentered_vs_har_raw_stat": -6.410523125545738,
+      "dm_centered_mean_loss_diff": -0.029458308893543145,
+      "dm_uncentered_vs_har_raw_stat": -6.410523136645976,
       "dm_uncentered_vs_har_raw_pvalue": 1.8314505467742492e-10,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.14720370139414576,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.14720370129284066,
       "n_predictions": 1870,
       "n_rv_days": 2278
     },
@@ -174,25 +174,25 @@
       "seed": 42,
       "seq_len": 22,
       "decompose": false,
-      "har_mse_logrv_raw": 0.5219741587277243,
-      "har_mse_logrv_debiased": 0.404202867593261,
-      "har_bias_oos": -0.34317822065868814,
-      "har_bias_sq_raw": 0.11777129113446325,
-      "har_variance_raw": 0.404202867593261,
-      "har_bias_sq_debiased": 1.5615786875681597e-31,
-      "har_variance_debiased": 0.404202867593261,
-      "dlinear_mse_logrv": 0.37625328531902474,
-      "dlinear_bias_sq": 4.137209157807155e-05,
-      "dlinear_variance": 0.37621191322744674,
-      "mse_reduction_pct_vs_debiased_har": 6.914741214147253,
-      "dm_centered_stat": -3.7842228030691065,
-      "dm_centered_pvalue": 0.00015904809598477065,
+      "har_mse_logrv_raw": 0.5219741587277245,
+      "har_mse_logrv_debiased": 0.4042028675932612,
+      "har_bias_oos": -0.34317822065868825,
+      "har_bias_sq_raw": 0.11777129113446332,
+      "har_variance_raw": 0.40420286759326113,
+      "har_bias_sq_debiased": 4.8568331222071824e-32,
+      "har_variance_debiased": 0.40420286759326113,
+      "dlinear_mse_logrv": 0.376253282882971,
+      "dlinear_bias_sq": 4.137209933984475e-05,
+      "dlinear_variance": 0.37621191078363114,
+      "mse_reduction_pct_vs_debiased_har": 6.914741816828259,
+      "dm_centered_stat": -3.7842233173471698,
+      "dm_centered_pvalue": 0.00015904776965869338,
       "dm_centered_verdict": "BEATS baseline",
-      "dm_centered_mean_loss_diff": -0.02799095436581433,
-      "dm_uncentered_vs_har_raw_stat": -6.394581498492657,
+      "dm_centered_mean_loss_diff": -0.027990956809629992,
+      "dm_uncentered_vs_har_raw_stat": -6.394581561922246,
       "dm_uncentered_vs_har_raw_pvalue": 2.0285328972136085e-10,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.14572087340869952,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.14572087584475343,
       "n_predictions": 1870,
       "n_rv_days": 2278
     },
@@ -202,25 +202,25 @@
       "seed": 99,
       "seq_len": 22,
       "decompose": false,
-      "har_mse_logrv_raw": 0.5219741587277243,
-      "har_mse_logrv_debiased": 0.404202867593261,
-      "har_bias_oos": -0.34317822065868814,
-      "har_bias_sq_raw": 0.11777129113446325,
-      "har_variance_raw": 0.404202867593261,
-      "har_bias_sq_debiased": 1.5615786875681597e-31,
-      "har_variance_debiased": 0.404202867593261,
-      "dlinear_mse_logrv": 0.3742861044528717,
-      "dlinear_bias_sq": 3.5460793748660084e-05,
-      "dlinear_variance": 0.3742506436591231,
-      "mse_reduction_pct_vs_debiased_har": 7.401422785177713,
-      "dm_centered_stat": -3.934064343605695,
-      "dm_centered_pvalue": 8.657436804493379e-05,
+      "har_mse_logrv_raw": 0.5219741587277245,
+      "har_mse_logrv_debiased": 0.4042028675932612,
+      "har_bias_oos": -0.34317822065868825,
+      "har_bias_sq_raw": 0.11777129113446332,
+      "har_variance_raw": 0.40420286759326113,
+      "har_bias_sq_debiased": 4.8568331222071824e-32,
+      "har_variance_debiased": 0.40420286759326113,
+      "dlinear_mse_logrv": 0.3742861021680439,
+      "dlinear_bias_sq": 3.546079923907866e-05,
+      "dlinear_variance": 0.3742506413688048,
+      "mse_reduction_pct_vs_debiased_har": 7.401423350445333,
+      "dm_centered_stat": -3.9340646295525485,
+      "dm_centered_pvalue": 8.65742658435753e-05,
       "dm_centered_verdict": "BEATS baseline",
-      "dm_centered_mean_loss_diff": -0.029952223934137993,
-      "dm_uncentered_vs_har_raw_stat": -6.504799754772119,
+      "dm_centered_mean_loss_diff": -0.029952226224456306,
+      "dm_uncentered_vs_har_raw_stat": -6.504799847439706,
       "dm_uncentered_vs_har_raw_pvalue": 9.959455482544399e-11,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.1476880542748526,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.14768805655968054,
       "n_predictions": 1870,
       "n_rv_days": 2278
     },
@@ -230,25 +230,25 @@
       "seed": 0,
       "seq_len": 22,
       "decompose": false,
-      "har_mse_logrv_raw": 0.5706841531937791,
-      "har_mse_logrv_debiased": 0.3679851864421157,
-      "har_bias_oos": -0.45022101989096786,
-      "har_bias_sq_raw": 0.20269896675166327,
-      "har_variance_raw": 0.3679851864421157,
-      "har_bias_sq_debiased": 1.0026153804241113e-32,
-      "har_variance_debiased": 0.3679851864421157,
-      "dlinear_mse_logrv": 0.35453501263625026,
-      "dlinear_bias_sq": 1.0367793001096496e-05,
-      "dlinear_variance": 0.35452464484324914,
-      "mse_reduction_pct_vs_debiased_har": 3.6550856668740277,
-      "dm_centered_stat": -1.6292025580658112,
-      "dm_centered_pvalue": 0.10344094996441178,
+      "har_mse_logrv_raw": 0.5706841531937793,
+      "har_mse_logrv_debiased": 0.3679851864421159,
+      "har_bias_oos": -0.4502210198909679,
+      "har_bias_sq_raw": 0.20269896675166332,
+      "har_variance_raw": 0.3679851864421159,
+      "har_bias_sq_debiased": 0.0,
+      "har_variance_debiased": 0.3679851864421159,
+      "dlinear_mse_logrv": 0.35453501270384535,
+      "dlinear_bias_sq": 1.0367794207915697e-05,
+      "dlinear_variance": 0.35452464490963737,
+      "mse_reduction_pct_vs_debiased_har": 3.6550856485051137,
+      "dm_centered_stat": -1.629202545808664,
+      "dm_centered_pvalue": 0.10344095255858243,
       "dm_centered_verdict": "INCONCLUSIVE",
-      "dm_centered_mean_loss_diff": -0.013460541598866624,
-      "dm_uncentered_vs_har_raw_stat": -5.94917859880759,
-      "dm_uncentered_vs_har_raw_pvalue": 3.2169920061164703e-09,
+      "dm_centered_mean_loss_diff": -0.013460541532478484,
+      "dm_uncentered_vs_har_raw_stat": -5.949178586266273,
+      "dm_uncentered_vs_har_raw_pvalue": 3.2169922281610752e-09,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.21614914055752882,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.21614914048993392,
       "n_predictions": 1845,
       "n_rv_days": 2278
     },
@@ -258,25 +258,25 @@
       "seed": 7,
       "seq_len": 22,
       "decompose": false,
-      "har_mse_logrv_raw": 0.5706841531937791,
-      "har_mse_logrv_debiased": 0.3679851864421157,
-      "har_bias_oos": -0.45022101989096786,
-      "har_bias_sq_raw": 0.20269896675166327,
-      "har_variance_raw": 0.3679851864421157,
-      "har_bias_sq_debiased": 1.0026153804241113e-32,
-      "har_variance_debiased": 0.3679851864421157,
-      "dlinear_mse_logrv": 0.3550310821814059,
-      "dlinear_bias_sq": 2.0108051038759022e-06,
-      "dlinear_variance": 0.35502907137630213,
-      "mse_reduction_pct_vs_debiased_har": 3.520278733488501,
-      "dm_centered_stat": -1.5657678314136847,
-      "dm_centered_pvalue": 0.11757456059303473,
+      "har_mse_logrv_raw": 0.5706841531937793,
+      "har_mse_logrv_debiased": 0.3679851864421159,
+      "har_bias_oos": -0.4502210198909679,
+      "har_bias_sq_raw": 0.20269896675166332,
+      "har_variance_raw": 0.3679851864421159,
+      "har_bias_sq_debiased": 0.0,
+      "har_variance_debiased": 0.3679851864421159,
+      "dlinear_mse_logrv": 0.3550310836965655,
+      "dlinear_bias_sq": 2.010805036203793e-06,
+      "dlinear_variance": 0.3550290728915293,
+      "mse_reduction_pct_vs_debiased_har": 3.5202783217438296,
+      "dm_centered_stat": -1.5657676573123986,
+      "dm_centered_pvalue": 0.11757460136708375,
       "dm_centered_verdict": "INCONCLUSIVE",
-      "dm_centered_mean_loss_diff": -0.012956115065813675,
-      "dm_uncentered_vs_har_raw_stat": -5.921302353064981,
-      "dm_uncentered_vs_har_raw_pvalue": 3.8006469083029515e-09,
+      "dm_centered_mean_loss_diff": -0.012956113550586642,
+      "dm_uncentered_vs_har_raw_stat": -5.921302338184295,
+      "dm_uncentered_vs_har_raw_pvalue": 3.800647352392161e-09,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.21565307101237308,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.21565306949721383,
       "n_predictions": 1845,
       "n_rv_days": 2278
     },
@@ -286,25 +286,25 @@
       "seed": 42,
       "seq_len": 22,
       "decompose": false,
-      "har_mse_logrv_raw": 0.5706841531937791,
-      "har_mse_logrv_debiased": 0.3679851864421157,
-      "har_bias_oos": -0.45022101989096786,
-      "har_bias_sq_raw": 0.20269896675166327,
-      "har_variance_raw": 0.3679851864421157,
-      "har_bias_sq_debiased": 1.0026153804241113e-32,
-      "har_variance_debiased": 0.3679851864421157,
-      "dlinear_mse_logrv": 0.353231017866015,
-      "dlinear_bias_sq": 1.2116360650931165e-06,
-      "dlinear_variance": 0.35322980622994987,
-      "mse_reduction_pct_vs_debiased_har": 4.009446336346347,
-      "dm_centered_stat": -1.8015203320683721,
-      "dm_centered_pvalue": 0.0717841959895027,
+      "har_mse_logrv_raw": 0.5706841531937793,
+      "har_mse_logrv_debiased": 0.3679851864421159,
+      "har_bias_oos": -0.4502210198909679,
+      "har_bias_sq_raw": 0.20269896675166332,
+      "har_variance_raw": 0.3679851864421159,
+      "har_bias_sq_debiased": 0.0,
+      "har_variance_debiased": 0.3679851864421159,
+      "dlinear_mse_logrv": 0.3532310174356302,
+      "dlinear_bias_sq": 1.2116371197366201e-06,
+      "dlinear_variance": 0.35322980579851043,
+      "mse_reduction_pct_vs_debiased_har": 4.009446453303514,
+      "dm_centered_stat": -1.8015204023592464,
+      "dm_centered_pvalue": 0.07178418491634075,
       "dm_centered_verdict": "INCONCLUSIVE",
-      "dm_centered_mean_loss_diff": -0.014755380212165842,
-      "dm_uncentered_vs_har_raw_stat": -6.014850417141578,
-      "dm_uncentered_vs_har_raw_pvalue": 2.1659258830908357e-09,
+      "dm_centered_mean_loss_diff": -0.014755380643605435,
+      "dm_uncentered_vs_har_raw_stat": -6.014850424246173,
+      "dm_uncentered_vs_har_raw_pvalue": 2.1659256610462307e-09,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.21745313532776406,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.21745313575814906,
       "n_predictions": 1845,
       "n_rv_days": 2278
     },
@@ -314,25 +314,25 @@
       "seed": 99,
       "seq_len": 22,
       "decompose": false,
-      "har_mse_logrv_raw": 0.5706841531937791,
-      "har_mse_logrv_debiased": 0.3679851864421157,
-      "har_bias_oos": -0.45022101989096786,
-      "har_bias_sq_raw": 0.20269896675166327,
-      "har_variance_raw": 0.3679851864421157,
-      "har_bias_sq_debiased": 1.0026153804241113e-32,
-      "har_variance_debiased": 0.3679851864421157,
-      "dlinear_mse_logrv": 0.3547817507641396,
-      "dlinear_bias_sq": 4.446275826344222e-07,
-      "dlinear_variance": 0.35478130613655695,
-      "mse_reduction_pct_vs_debiased_har": 3.5880345634655084,
-      "dm_centered_stat": -1.6420369908879489,
-      "dm_centered_pvalue": 0.10075284985975763,
+      "har_mse_logrv_raw": 0.5706841531937793,
+      "har_mse_logrv_debiased": 0.3679851864421159,
+      "har_bias_oos": -0.4502210198909679,
+      "har_bias_sq_raw": 0.20269896675166332,
+      "har_variance_raw": 0.3679851864421159,
+      "har_bias_sq_debiased": 0.0,
+      "har_variance_debiased": 0.3679851864421159,
+      "dlinear_mse_logrv": 0.3547817510562591,
+      "dlinear_bias_sq": 4.446253648082128e-07,
+      "dlinear_variance": 0.3547813064308943,
+      "mse_reduction_pct_vs_debiased_har": 3.588034484082074,
+      "dm_centered_stat": -1.642036967772643,
+      "dm_centered_pvalue": 0.10075285465047168,
       "dm_centered_verdict": "INCONCLUSIVE",
-      "dm_centered_mean_loss_diff": -0.013203880305558816,
-      "dm_uncentered_vs_har_raw_stat": -5.963643000538417,
-      "dm_uncentered_vs_har_raw_pvalue": 2.9495581532756887e-09,
+      "dm_centered_mean_loss_diff": -0.013203880011221601,
+      "dm_uncentered_vs_har_raw_stat": -5.9636430462563395,
+      "dm_uncentered_vs_har_raw_pvalue": 2.949557265097269e-09,
       "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
-      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.21590240242963946,
+      "dm_uncentered_vs_har_raw_mean_loss_diff": -0.21590240213752013,
       "n_predictions": 1845,
       "n_rv_days": 2278
     }
@@ -341,12 +341,13 @@
     {
       "horizon": 1,
       "n_seeds": 4,
-      "edge_reduction_pct": 10.096441451181168,
-      "edge_std_pct": 0.1493457405376121,
-      "dm_centered_p_median": 1.544945837927969e-09,
-      "var_ratio_dl_over_har_debiased": 0.8989944886967286,
-      "har_bias_share_of_mse_debiased": 0.05783823037415984,
-      "mean_dl_mse": 0.7518949222324479,
+      "edge_reduction_pct": 10.09644162193385,
+      "edge_std_pct": 0.14934555451809678,
+      "dm_centered_p_median": 1.5449479473517158e-09,
+      "dm_uncentered_vs_har_raw_p_median": 0.0,
+      "var_ratio_dl_over_har_debiased": 0.8989944870038112,
+      "har_bias_share_of_mse_debiased": 0.05783823037415989,
+      "mean_dl_mse": 0.7518949208043835,
       "mean_har_mse_raw": 0.8876766414630621,
       "mean_har_mse_debiased": 0.8363349953763609,
       "n_beaten": 0,
@@ -357,14 +358,15 @@
     {
       "horizon": 5,
       "n_seeds": 4,
-      "edge_reduction_pct": 7.286359135350475,
-      "edge_std_pct": 0.23433430101241232,
-      "dm_centered_p_median": 9.660139108547128e-05,
-      "var_ratio_dl_over_har_debiased": 0.9270558668235739,
-      "har_bias_share_of_mse_debiased": 0.22562666975990264,
-      "mean_dl_mse": 0.37475119502503085,
-      "mean_har_mse_raw": 0.5219741587277243,
-      "mean_har_mse_debiased": 0.404202867593261,
+      "edge_reduction_pct": 7.286359386338866,
+      "edge_std_pct": 0.23433409285739365,
+      "dm_centered_p_median": 9.660135403088965e-05,
+      "dm_uncentered_vs_har_raw_p_median": 1.4353762622931754e-10,
+      "var_ratio_dl_over_har_debiased": 0.9270558643170052,
+      "har_bias_share_of_mse_debiased": 0.2256266697599027,
+      "mean_dl_mse": 0.37475119401052875,
+      "mean_har_mse_raw": 0.5219741587277245,
+      "mean_har_mse_debiased": 0.4042028675932612,
       "n_beaten": 0,
       "n_beats": 4,
       "n_inconclusive": 0,
@@ -373,21 +375,22 @@
     {
       "horizon": 10,
       "n_seeds": 4,
-      "edge_reduction_pct": 3.6932113250435963,
-      "edge_std_pct": 0.18869683395762699,
-      "dm_centered_p_median": 0.1020968999120847,
-      "var_ratio_dl_over_har_debiased": 0.9630583518129214,
-      "har_bias_share_of_mse_debiased": 0.35518590382662285,
-      "mean_dl_mse": 0.35439471586195265,
-      "mean_har_mse_raw": 0.5706841531937791,
-      "mean_har_mse_debiased": 0.3679851864421157,
+      "edge_reduction_pct": 3.693211226908633,
+      "edge_std_pct": 0.18869698928571157,
+      "dm_centered_p_median": 0.10209690360452706,
+      "dm_uncentered_vs_har_raw_p_median": 3.083274746629172e-09,
+      "var_ratio_dl_over_har_debiased": 0.9630583527942871,
+      "har_bias_share_of_mse_debiased": 0.3551859038266228,
+      "mean_dl_mse": 0.35439471622307506,
+      "mean_har_mse_raw": 0.5706841531937793,
+      "mean_har_mse_debiased": 0.3679851864421159,
       "n_beaten": 0,
       "n_beats": 0,
       "n_inconclusive": 4,
       "verdict_sc": "INCONCLUSIVE"
     }
   ],
-  "elapsed_s": 1776.391629934311,
+  "elapsed_s": 1571.1744956970215,
   "config": {
     "horizons": [
       1,
diff --git a/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/tests/test_btc_vol.py b/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/tests/test_btc_vol.py
index b0ffb864ae..3e1959d45b 100644
--- a/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/tests/test_btc_vol.py
+++ b/MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/tests/test_btc_vol.py
@@ -26,6 +26,7 @@
 )
 from btc_vol import (  # noqa: E402
     _dm_uncentered_mse,
+    aggregate_verdicts_recentered,
 )
 
 
@@ -224,4 +225,73 @@ def test_uncentered_sentinels(self):
         assert (
             _dm_uncentered_mse(np.zeros(5), np.zeros(5), horizon=1)["dm_verdict"]
             == "INSUFFICIENT_DATA"
-        )
\ No newline at end of file
+        )
+
+
+def _agg_row(h: int, seed: int, dm_centered_p: float, dm_unc_p: float) -> dict:
+    """Minimal row accepted by `aggregate_verdicts_recentered` (#14390)."""
+    return {
+        "coin": "BTC",
+        "horizon": h,
+        "seed": seed,
+        "mse_reduction_pct_vs_debiased_har": 10.0,
+        "dlinear_variance": 0.8,
+        "har_variance_debiased": 1.0,
+        "har_bias_oos": 0.05,
+        "dlinear_mse_logrv": 0.75,
+        "har_mse_logrv_raw": 0.89,
+        "har_mse_logrv_debiased": 0.84,
+        "dm_centered_pvalue": dm_centered_p,
+        "dm_centered_verdict": "BEATS baseline",
+        "dm_uncentered_vs_har_raw_pvalue": dm_unc_p,
+        "dm_uncentered_vs_har_raw_verdict": "BEATS baseline",
+    }
+
+
+class TestAggregateVerdictsRecentered:
+    """#14390: the uncentered sanity leg must live in `aggregated`, not only per row.
+
+    The #14362 defect was exactly a silent absence -- the sanity leg existed
+    per row but nothing summarized or confronted it. These tests fail if the
+    aggregated field disappears again.
+    """
+
+    def test_uncentered_p_median_present_in_every_aggregate(self):
+        rows = [
+            _agg_row(1, 0, 1e-9, 0.0),
+            _agg_row(1, 7, 2e-9, 0.0),
+            _agg_row(5, 0, 3e-9, 1.0e-10),
+            _agg_row(5, 7, 4e-9, 2.0e-10),
+        ]
+        agg = aggregate_verdicts_recentered(rows)
+        assert len(agg) == 2
+        for entry in agg:
+            assert "dm_uncentered_vs_har_raw_p_median" in entry
+
+    def test_uncentered_p_median_is_the_same_seeds_median(self):
+        # median([0.0, 0.0]) = 0.0 ; median([1e-10, 2e-10]) = 1.5e-10
+        rows = [
+            _agg_row(1, 0, 1e-9, 0.0),
+            _agg_row(1, 7, 2e-9, 0.0),
+            _agg_row(5, 0, 3e-9, 1.0e-10),
+            _agg_row(5, 7, 4e-9, 2.0e-10),
+        ]
+        agg = {e["horizon"]: e for e in aggregate_verdicts_recentered(rows)}
+        assert agg[1]["dm_uncentered_vs_har_raw_p_median"] == pytest.approx(0.0, abs=0.0)
+        assert agg[5]["dm_uncentered_vs_har_raw_p_median"] == pytest.approx(1.5e-10)
+        # Same seeds as the centered median: recomputed independently below.
+        assert agg[1]["dm_centered_p_median"] == pytest.approx(1.5e-9)
+
+    def test_legs_cannot_silently_collapse_at_the_aggregate_surface(self):
+        # If the two legs ever return the same statistic again (#14362), the
+        # aggregated medians coincide -- the fixture keeps them distinct so a
+        # future collapse flips this assertion.
+        rows = [_agg_row(10, s, 1e-9 + s * 1e-12, 1e-8 + s * 1e-10) for s in (0, 7, 42, 99)]
+        agg = aggregate_verdicts_recentered(rows)[0]
+        assert agg["dm_centered_p_median"] != agg["dm_uncentered_vs_har_raw_p_median"]
+
+    def test_field_position_is_next_to_centered_median(self):
+        rows = [_agg_row(1, s, 1e-9, 1e-7) for s in (0, 7)]
+        entry = aggregate_verdicts_recentered(rows)[0]
+        keys = list(entry)
+        assert keys.index("dm_uncentered_vs_har_raw_p_median") == keys.index("dm_centered_p_median") + 1