The current λ controller boosts horizons by BCE-EMA z-score, which conflates
three distinct causes of high BCE — only one of which (under-trained
horizon) benefits from boosting. The other two (intrinsically harder
horizon, calibration drift) are unaffected by gradient-magnitude lifts.
Empirical motivation (gm67g fold-0, this branch's 3-fold run):
when λ_h6000 saturated at 2.0, h6000's next-epoch AUC went UP 2/4
times and DOWN 2/4 times. 50% hit rate ⇒ the BCE saturation signal
is misaligned with the optimization objective.
This spec replaces BCE-z-score with AUC-regret:
best_auc[h] = running max of per-horizon validation AUC
regret[h] = max(0, best_auc[h] - current_auc[h])
regret_max_ema = EMA of max_h regret[h]
λ[h] = clamp(1.0, 2.0, 1 + regret[h] / regret_max_ema)
Properties:
- Aligned with the objective (AUC, not BCE)
- Naturally bounded (regret ∈ [0, 1])
- "At personal best" → λ=1.0 (no wasted boost)
- Auto-saturation by design (max-regret horizon → ceiling)
- Cold-start clean (e0: best=current, regret=0, uniform λ)
- Zero hardcoded magic beyond bootstrap epsilons
Implementation surface ~250 LOC:
- Split horizon_lambda kernel: horizon_loss_ema (per-step) +
horizon_lambda (per-epoch, AUC-regret math)
- Trainer state: drop z_max_ema, add best_auc + regret_max_ema
- Per-epoch entry point: trainer.update_lambda_from_auc()
- Extend isv snapshot log line with best_auc_h* + regret_h*
Test plan:
- Local 9/9 perception_overfit
- New synthetic-AUC unit test (controller correctness)
- Cluster 5-epoch smoke + 3-fold A/B vs Option-2 baseline (004b662c8)
- Success: mean_auc lifts ≥ +0.005 AND median sat→next-AUC delta positive
Awaiting user review before plan handoff.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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