# Dual Prop state-bias screen

The central result is positive, but the complete pre-registered gate fails.

In the author Dual Prop miniCNN, neuron-specific activity-dependent bias makes
the raw local update nonfinite in epoch 1 at every tested bias ratio.  The
innovation rule stays finite for all 20 epochs and remains close to both clean
Dual Prop and an oracle that subtracts the generated bias exactly.

| Bias / clean teaching RMS | Raw | Innovation | Oracle |
|---:|---:|---:|---:|
| 0.25 | 9.40, nonfinite | 71.10 | 70.58 |
| 1 | 9.40, nonfinite | 70.18 | 69.80 |
| 4 | 9.40, nonfinite | 68.96 | 69.86 |

Values are final validation accuracy in percent.  Clean Dual Prop is 69.66%.
The largest innovation-to-clean gap is 1.44 points and the largest
innovation-to-oracle gap is 0.90 points.  The maximum bias remaining after
subtraction is `8.96e-8` of the raw bias, and the predictor sees zero task
instruction observations.

The complete B1 gate is `fail` for two reasons unrelated to the recovery
comparison: clean ends 0.34 points below the frozen 70% threshold, and the
common-bias endpoint differs from clean by 2.28 points even though its teaching
difference error is exactly zero.  The latter shows that a 0.2-point single-run
accuracy tolerance is not a reliable identity check on these GPU kernels.
Because the complete gate fails, B2 is not opened automatically.  A new
confirmation protocol must be frozen before running more seeds.