0b9ea77dc4
Per Class A audit-fix Batch 4-B (final 2 of 4 deferred items from
P1-wiring/P1-producer). Completes the 8-commit WR-plateau intervention
chain. Validation deferred to next L40S smoke.
Item 3: plan_threshold adaptive floor (Design Y - inline producer)
- NEW slot PLAN_THRESHOLD_FLOOR_ADAPTIVE_INDEX=459
- Writes slow-EMA shadow of `0.5 * readiness_ema` from inside the
existing update kernel (no new file, no new launch).
- Pearl-A first-observation bootstrap (sentinel 0.1 matches pre-fix
hardcoded value for bit-identical cold-start) + Welford alpha=0.005
slow EMA.
- Bilateral clamp [0.05, 0.50] (probability units) per
pearl_symmetric_clamp_audit.
- Consumer reads isv[459] as the floor in the same launch's final
fmaxf; cold-start sentinel REPLACES with threshold_target so the
pre-fix `fmaxf(0.1, 0.5*ema)` semantic is preserved bit-identical
for any readiness EMA above 0.20.
Item 4: MIN_HOLD_TEMPERATURE -> ISV-driven (driving signal: dir_acc skill)
- NEW slot MIN_HOLD_TEMPERATURE_ADAPTIVE_INDEX=460
- NEW kernel min_hold_temperature_update_kernel.cu (single-thread
cold-path, per-epoch boundary launch).
- Driving signal: dir_acc skill = clamp((short_ema - 0.5)/0.5, 0, 1)
from ISV[AUX_DIR_ACC_SHORT_EMA_INDEX=373]. When committing skillfully
(high dir_acc) -> temp HIGH (permissive). When at random baseline
(~0.5) -> temp LOW (sharp commitment pressure). Substituted for
the audit-spec's `dir_entropy_deficit` because no dir_entropy ISV
slot exists - dir_acc skill is the closest semantically-equivalent
signal that preserves the spec intent.
- Pearl-A bootstrap (sentinel 50.0 matches the deleted
MIN_HOLD_TEMPERATURE_START=50 anchor) + alpha=0.05 mid-cadence EMA.
- Bounds [5, 50] (matches the deleted schedule range).
- Decouples temperature from epoch number - the old schedule pinned
LOW temp (sharp) at end of training, exactly when a WR-plateaued
model needed forgiveness to escape.
- DELETED: state_layout.cuh::MIN_HOLD_TEMPERATURE_{START, END, DECAY}
#defines + training_loop.rs::min_hold_temperature_for_epoch helper
function (kept docstring tombstone explaining the deletion). Both
call sites migrated to the new ISV reader. Per
feedback_no_legacy_aliases + feedback_no_partial_refactor.
ISV_TOTAL_DIM: 459 -> 461.
Cumulative WR-plateau fix series (final commit, #8):
- 8f218cab2 (Class C bug 1 + P0-B)
- 316db416b (P0-C MIN_HOLD_TARGET)
- 394de7d43 (P0-A REWARD_POS/NEG_CAP)
- c4b6d6ef2 (P1 wiring var_floor)
- 657972a4b (P0-A downstream DD penalty + MIN_HOLD_PENALTY_MAX)
- 87d597d5d (P1 producer Bayesian priors)
- 7e9a8f6ef (Batch 4-A DD saturation floor + legacy DELETE)
- this commit (Batch 4-B plan_threshold floor + MIN_HOLD_TEMPERATURE)
Verification:
cargo check -p ml --tests --all-targets PASS
cargo test sp14 (lib) 16/16 PASS (slot layout locks)
cargo test sp15 (lib) 2/2 PASS (no regression)
cargo test state_reset_registry (lib) 4/4 PASS (dispatch coverage)
cargo test sp14_oracle_tests (GPU) 24/24 PASS (8 new + 16 pre)
cargo test sp15_phase1_oracle_tests (GPU) 36/36 PASS (no regression)
Per feedback_isv_for_adaptive_bounds + feedback_no_partial_refactor +
feedback_no_legacy_aliases.
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;