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@@ -806,6 +806,28 @@ task-scalar queries did not calibrate it. WM-2 therefore opens. The result is credited entirely to an inherited weight-estimation baseline; it cannot raise the SDIL novelty score by itself. +The conditional WM-2 task screen is much stronger than fixed HFA but narrowly +fails its frozen advancement gate: + +| method | validation accuracy | early alignment | all-layer alignment | estimated MACs | +|:--|--:|--:|--:|--:| +| WM, hidden LR 0.03 | 62.68% | 0.827404 | 0.825811 | 2.4252e13 | +| WM, hidden LR 0.10 | **64.04%** | **0.939256** | **0.916222** | 2.4252e13 | +| matched BP | 74.94% | n/a | n/a | 2.4331e13 | + +Both WM runs are finite, use zero task-loss queries for feedback learning, and +cost `0.9968x` matched BP MACs. Selected WM improves fixed HFA by 20.52 points, +but misses 65% by 0.96 points and the within-10-point BP window by 0.90 points. +Thus four check families pass--finite, alignment, query, and cost--while both +accuracy checks fail. WM-3 is closed without extra mirror tuning. + +This is an informative optimization negative: a hidden teaching cosine of +`0.939` and feedback/forward cosine `0.936` still leave a 10.90-point short- +horizon accuracy gap. Alignment is necessary here but not sufficient; local +gain errors and the remaining compounding angular errors can materially change +the trajectory. It would be misleading to present the capture result alone as +BP-equivalent learning. + ## How to run `experiments/run.py --mode {bp,fa,dfa,sdil} --dataset {mnist,fmnist,cifar10} --depth D --residual {0,1} --act {tanh,gelu,silu,relu}` Batteries: `experiments/run_v2.sh <ds> "<depths>" <res> <act> "<seeds>" <ep> <pfx>`. |