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authorYurenHao0426 <Blackhao0426@gmail.com>2026-06-09 15:30:20 -0500
committerYurenHao0426 <Blackhao0426@gmail.com>2026-06-09 15:30:20 -0500
commit84fa70be406248e660488ddc4c56ca5ea0034189 (patch)
tree828e47ecd89991c2b446a0d0a6f5c030c7348e12 /notes/36_evidence_ledger.md
parentb246cc878c3db48e6d993a6a293176ee9be97e77 (diff)
Add teacher-task test-gap sweep and figure
SGD teacher sweep (widths 8-96, 24 FA runs/width): train gap soft and monotone (0.249 -> 0.0069) while the test gap changes sign — FA generalizes better than BP at intermediate width (-0.120 +/- 0.013 at w=32) and settles slightly positive once both fit. Scopes the paper's optimization-gap claims and preempts the generalization question. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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@@ -146,9 +146,12 @@ what they are; (31) and (25) deserve explicit mention in the paper.
- MNIST e0 + estimator: `outputs/real_data_validation_mnist/` (script
`scripts/real_data_validation.py`, self-test verifies kernels vs autograd
at 1e-15).
-- Teacher-task test gap (SGD, widths 8..96): `outputs/teacher_test_gap_sgd_T8000/`
- — addresses "what about generalization": train-gap story plus a descriptive
- test-MSE companion. Random-label sweeps measure optimization gap by design.
+- Teacher-task test gap (note 39, SGD, widths 8..96, 24 FA runs/width):
+ train gap soft and monotone (0.249 -> 0.0069) while the **test gap changes
+ sign** — +0.177 (w=8), **-0.120 +/- 0.013 (w=32, FA generalizes better)**,
+ +0.045 (w=96). The optimization gap is the invariant cost; its test-side
+ consequence is task-dependent — exactly why the theory is built on the
+ former. `outputs/teacher_test_gap_sgd_T8000/teacher_train_vs_test_gap.png`.
## 6. Figure plan (sources verified on disk)