ResultFull training of the K=256 afterstate model, then the frozen top-two override rule on fresh origins

Full training does not rescue the afterstate model; it overfits. The model was trained to 22 epochs on the 2M-row K=256 subsample (44M row-updates, 2x iteration 3, cosine schedule completed, ranking loss 0.584 vs iteration 3's 0.630). On the SAME held-out roots as iteration 3 (corpus-C, a labeled diagnostic reuse), the fully-trained model ranks WORSE than iteration 3's 11-epoch checkpoint (top-1 0.361 vs 0.424, pairwise 0.658 vs 0.685, regret 0.281 vs 0.241) - training loss improved while held-out ranking degraded, a textbook overfitting signature against the D1-continuation H40 labels. The frozen override gate on fresh corpus-E (2,867 roots, 1,106 near-tie eligible, 41% override rate) then FAILED: eligible-root regret half1 0.2289 vs D4 0.2517 (+0.0228) but half2 0.2778 vs 0.2481 (-0.0297, the override is actively harmful there), pooled +0.0018. Stability (0.824), calibration (0.800), determinism (byte-identical) all passed. Conclusion: the model's limitation is not training completeness but generalization to held-out roots under a weak D1 teacher; the direct-override use of this model family is closed per the frozen failure action.