84de278dfe
Each EGF pearl EMA / state slot resets to its Pearl-A sentinel at fold
boundary, mirroring sp13_aux_dir_acc_short_ema / long_ema entries.
Atomic refactor (feedback_no_partial_refactor): both halves land
together — registry entry + reset_named_state dispatch arm.
Reset slots (11 total, sentinel in parens):
- Q_DISAGREEMENT_SHORT/LONG_EMA (slots 383, 384) → 0.5
- K_AUX_ADAPTIVE (385) → K_BASE_AUX = 20.0
- K_Q_ADAPTIVE (386) → K_BASE_Q = 15.0
- BETA_RATE_LIMITER_ADAPTIVE (387) → BETA_BASE = 0.5
- AUX_DIR_ACC_VARIANCE_EMA, Q_DISAGREEMENT_VARIANCE_EMA,
ALPHA_GRAD_RAW_VARIANCE_EMA (388, 389, 390) → 0.0
(initial k = k_base, β = β_base via ISV-driven controllers)
- GATE1_OPEN_STATE (391) → 0.0 (closed)
- ALPHA_GRAD_SMOOTHED (393) → 0.0
- AUX_DIR_ACC_POST_OPEN_MIN (394) → 1.0 (no min observed)
ALPHA_GRAD_RAW (slot 392, recomputed every step from variance EMAs)
and GRADIENT_HACK_LOCKOUT_REMAINING (slot 395, decays at epoch
boundary) are NOT in the fold-reset registry; both naturally
re-initialise without explicit reset.
Also corrects the isv-slots.md SP14 table: slots 392 and 395 were
incorrectly marked FoldReset in the B.1 entry; corrected to reflect
their actual reset semantics (NOT reset / epoch-boundary decay).
Producer + consumer wiring lands in subsequent tasks (B.3-B.12);
this commit is additive infrastructure only — no behavior change.
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;