Local trace at base_lambda=0.1 showed the linear ratio
{1, 0.3, 0.1, 0.03, 0.005} = HORIZONS[0]/HORIZONS[h] gave h6000 a
200x stronger target-undershoot signal than h30. The controller
slammed h6000 with smoothness gradient (peak λ[h6000]≈60) while h30
saw nearly none. h6000 val_auc collapsed to 0.514 (near-random)
while h100/h300 improved.
Replace with sqrt(HORIZONS[0]/HORIZONS[h]) = {1, 0.548, 0.316, 0.173,
0.0707}. Caps the differential at ~14x. Verified locally at base=0.1:
- h6000 val_auc recovered to 0.599 (vs 0.514 collapse, vs 0.621 no-smooth)
- jitter ratio h6000/h30 = 0.51 (meaningful differentiation, was 0.84 unforced)
- λ[h6000] equilibrium = 0.7-3 (was 4-60 with linear ratio at same base)
The design target (h6000 jitter = 7% of h30) is less aggressive than
linear (was 0.5%) but produces a tractable training equilibrium that
preserves h6000 predictive capacity. Both 500-step local AND the full
40k-step L40S retrain will tell us where it lands at scale.
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