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authorYurenHao0426 <Blackhao0426@gmail.com>2026-07-22 02:19:35 -0500
committerYurenHao0426 <Blackhao0426@gmail.com>2026-07-22 02:19:35 -0500
commite189c8027c0ebb70d68eac630b6e50a23bb0ee0b (patch)
tree1103feefaf9f931b9e69413e2c8b3a240ca7a2e4 /RESULTS.md
parent7ae095f6e87977caece1d53380c2b395d69d6000 (diff)
results: freeze single-direction calibration
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@@ -158,6 +158,32 @@ slower than forward learning: once teaching and traffic are separated by neutral
tracking is beneficial, while a long warmup is not consistently useful. This is a frozen
validation choice for the multi-seed C1 experiment, not evidence that C1 has passed.
+## Causal-calibration query screen (validation only)
+
+This seed-0 development screen used CIFAR-10 d20/w64 for five epochs and a 5,000-example
+training-only validation split (hash `8328b206...515b`). All eight runs came from clean revision
+`1a22a99`; the test set was never evaluated. Gain retention is
+`(candidate - DFA) / (K16/e4 - DFA)` at the final validation endpoint.
+
+| method | directions K | every | val (%) | batch loss queries | query reduction | calibration work reduction | gain retention |
+|:-------|-------------:|------:|--------:|-------------------:|----------------:|---------------------------:|---------------:|
+| DFA | — | — | 36.66 | 0 | — | — | — |
+| learned NP | 16 | 4 | 37.46 | 14,080 | 1.0x | 1.0x | 1.000 |
+| **learned NP** | **1** | **4** | **38.08** | **880** | **16.0x** | **11.0x** | **1.775** |
+| learned NP | 2 | 8 | 37.32 | 880 | 16.0x | 13.2x | 0.825 |
+| learned NP | 4 | 16 | 35.98 | 880 | 16.0x | 14.7x | -0.850 |
+| learned NP | 1 | 8 | 36.70 | 440 | 32.0x | 22.0x | 0.050 |
+| learned NP | 1 | 16 | 36.52 | 220 | 64.0x | 44.0x | -0.175 |
+| learned NP | 2 | 32 | 36.78 | 220 | 64.0x | 52.8x | 0.150 |
+
+`K=1/every=4` is frozen for confirmation: it exceeds the C3 10x-query/90%-retention threshold in
+validation and is the only candidate that improves on the reference endpoint. The ablation also
+separates directions from update frequency: spending the same 880 queries as `K=2/every=8` or
+`K=4/every=16` is worse, so frequent noisy regression targets are more useful than precise sparse
+targets. GTX-1080 wall time remains approximately 15–23 seconds across the grid despite the 11x
+calibration-work change, demonstrating why the Pareto audit needs hardware-independent work in
+addition to wall time. C3 is not declared passed until frozen multi-seed confirmation.
+
## Legacy credit assignment / depth utility
Per-layer alignment cos(r_l, −∇_{h_l}L), early-third average, CIFAR no-BN residual:
| depth | DFA early-align | SDIL early-align |