Successor-closed distributional afterstate values rank siblings as well as fair D4

The successor-closed corpus pipeline works exactly as designed (11,379 roots, 616,048 sibling labels, 100% action completeness, all CHECK tests pass), but the frozen label-stability floor failed: with K=8 aligned scenarios and an H40 phase-greedy-D1 continuation, the split-half Spearman correlation of within-root action rankings is 0.246, far below the 0.50 floor. Per the preregistered gate the outcome is inconclusive: the scenario-mean H40 outcome ranking is too noisy at K=8 to certify any model's ranking. Diagnosis: within-action scenario std is ~21k points while the median between-action gap is ~7.1k, so the target ranking is noise-dominated; noise math predicts K=64 (split-half 32v32) lifts stability to roughly 0.7. For the record, against this noisy target the model trailed fair D4 (top-1 0.247 vs 0.330 pooled; pairwise 0.571 vs 0.637), and D4 beat D1 (0.330 vs 0.254), consistent with the historical ranking of those searches. No gameplay tier was opened.

K=64 improved label stability from 0.246 to 0.446 mean split-half Spearman on fresh held-out origins, but still below the frozen 0.5 floor, so the preregistered verdict is again inconclusive. Quantile calibration passed (0.807 coverage of the nominal 76.5% interval). Against the still-noisy target the model trailed fair D4 consistently in both half-folds (pooled top-1 0.342 vs 0.433; pairwise 0.637 vs 0.697; regret 0.303 vs 0.232) while beating its D1 continuation teacher (top-1 0.342 vs 0.299). Training stopped at epoch 15 of 20 on the GPU time budget with the ranking loss still decreasing. No gameplay tier was opened.

With K=256 aligned scenarios the label-stability floor passed decisively (decisive-root split-half Spearman 0.818, unconditioned 0.638, both >= 0.5), so for the first time in this line the ranking target is stable enough to certify a verdict. The verdict is a valid negative for the direct-play configuration: the distributional afterstate ranker trailed exact fair D4 on every preregistered comparison in both fresh held-out half-folds (pooled top-1 0.424 vs 0.499; pairwise 0.685 vs 0.737; regret 0.241 vs 0.178). On the 411 decisive roots (spread > 20k) the gap narrowed but persisted (top-1 0.526 vs 0.584; regret 0.082 vs 0.064). The model clearly beat its D1 continuation teacher (top-1 0.424 vs 0.319) and its quantile head is calibrated (0.864 coverage). Conclusion: a compact action-free afterstate network trained on successor-closed H40 D1-continuation labels is a viable, calibrated long-horizon evaluator, but it is NOT a viable direct replacement for 4-ply expectimax; any future use must be as a companion signal inside search, and the same ranking gate applies. No gameplay tier was opened.

The calibrated top-two near-tie override of fair D4 by the frozen iteration-3 afterstate model FAILED its frozen gate by a narrow margin, with genuinely positive signal. On 2,689 fresh held-out roots, 1,030 (38.3%) were near-tied at the frozen 500-point threshold and the override fired on 37.0% of those. On eligible roots the override reduced mean normalized regret versus unchanged D4 in both half-folds (half1 0.2484 vs 0.2835, +0.0351; half2 0.2343 vs 0.2418, +0.0075; pooled +0.0214), but the frozen criterion required at least +0.01 in EACH half-fold, and half2 fell short. On the 84 decisive eligible roots the improvement was larger (+0.0426: 0.0880 vs 0.1306). Label stability passed (decisive Spearman 0.814), model quantile calibration passed (0.861), the gate is deterministic (byte-identical repeated reports), and the corpus is successor-closed. Interpretation: the conservative-override MECHANISM works as designed (fires often, calibrated, positive in both halves) and this is the first preregistered held-out test in this repository where a learned model's intervention improved on fair D4 at all; the CHECKPOINT's ranking quality is the bottleneck, consistent with the model being undertrained (11 of 20 epochs) and trained on a weak D1-continuation teacher. Per the frozen failure action, no further override variants of this same checkpoint are opened; a retry requires new evidence such as a fully trained or stronger-teacher model under the same frozen rule.

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.

The stronger-teacher hypothesis fails as tested. A successor-closed corpus relabeled with a fair-D2 (five-sample) continuation teacher at K=64 (2.88M rows over 6,535 roots; partial at the generator's 4h default wall stop) trained a model that, on the fresh 0x5da70500 ranking gate against the fixed D1-continuation H40 target, reached top-1 0.3365 - far below fair D4's 0.5020, below the D1-teacher model's 0.4245 from iteration 3, and only at exact-D1's own 0.3339. The frozen criterion (top-1 >= 0.4616 on each half, i.e. closing half the iteration-3 gap to D4) failed by a wide margin in both halves (0.342, 0.331). The frozen override gate on fresh 0x5da70600 roots also failed (eligible-root regret: half1 -0.0089, half2 +0.0110, pooled +0.0012). IMPORTANT CONFOUND, disclosed: the D2-teacher model was evaluated against D1-continuation outcomes (frozen for comparability with iteration 3), so part of its regression may reflect the teacher/target mismatch rather than teacher quality alone. Read narrowly, the result says a stronger-teacher corpus did not produce a better ranker of the fixed public-continuation outcome, and the afterstate line's ranking deficit is robust to the teacher choice within the tested configurations.