641aa0dfde
Per train-multi-seed-hjzss validation: SP16 T1+T2 chain was structurally
landed but BEHAVIORALLY INERT in 5-epoch smoke (bit-identical to pfh9n
baseline through epoch 3). Root cause: hardcoded `alpha = 0.05f` in both
producer kernels violates feedback_isv_for_adaptive_bounds AND prevents
convergence in short runs (~60 epochs needed from cold start).
Fix per pearl_wiener_optimal_adaptive_alpha:
α = diff_var / (diff_var + sample_var + ε)
Where sample_var = running variance of target signal (Welford accumulator)
and diff_var = running variance of consecutive one-step differences.
Cold-start: target jumps 1.0 → 6.4 → 7.0 → high diff_var → α ≈ 0.6+
→ near-bootstrap responsiveness in epochs 1-3
Steady-state: signal stabilizes → diff_var drops → α decays naturally
→ smoothing emerges without hardcoded constant
Adds 12 new ISV slots (6 per producer):
- HCS_TARGET_MEAN/M2, HCS_DIFF_MEAN/M2, HCS_PREV_TARGET, HCS_SAMPLE_COUNT
- MHT_TARGET_MEAN/M2, MHT_DIFF_MEAN/M2, MHT_PREV_TARGET, MHT_SAMPLE_COUNT
ISV_TOTAL_DIM 462 → 474.
Both kernels migrated atomically. Pearl-A bootstrap preserved (sentinel
on prev_blended triggers REPLACE; cold-start α=1.0 when N<3 samples).
Defensive bounds [WELFORD_ALPHA_MIN=0.01, WELFORD_ALPHA_MAX=0.95] on the
Wiener-derived α to guard against denormal/underflow corner cases.
HEALTH_DIAG[N] emit extended with `alpha=...` and `sample_count=...` for
direct trajectory observation in validation smoke.
Behavioral tests verify:
- α high during signal jumps (>0.3 at epoch 3 post-cold-start)
- α low in steady state (mean tail α<0.4 under converging signal)
- Pearl-A bootstrap fires on first observation (Welford state advances
regardless of REPLACE branch)
- α stays within [WELFORD_ALPHA_MIN, WELFORD_ALPHA_MAX] over 50 epochs
(post-cold-start; cold-start α=1.0 by design)
- No 0.05f hardcoded literal remains in blend math (regression-locked
via host-only string scan)
5 GPU + host tests pass: sp16_phase3_alpha_high_during_signal_jump,
alpha_low_in_steady_state, pearl_a_bootstrap_first_obs,
alpha_naturally_bounded, no_hardcoded_alpha. sp14 + sp15 oracle suites
unchanged (34 GPU tests + 4 host tests).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;