200f05fcef
Root cause from train-v8ztm 10-ep validation (commit1396b62ec): HEALTH_DIAG showed alpha_smoothed=0.0002 (vs ~0.5 expected steady-state), gate1=closed, var_aux:var_q ratio 290:1 — symptoms of an EGF producer chain firing < 1% as often as the consumer. The original B.11 wire-up (commit857722e77) placed `launch_sp14_q_disagreement_update`, `launch_sp14_alpha_grad_compute`, and the prerequisite `launch_sp13_aux_dir_metrics` in `process_epoch_boundary` — which runs ONCE per epoch (single call site at training_loop.rs:780, called from the per-epoch loop, not from the per-step loop in `run_training_steps_slices`). The captured backward consumer `launch_sp14_scale_wire_col` (inside launch_cublas_backward_to, replays every training step via parent graph) reads ISV[ALPHA_GRAD_SMOOTHED=393] per step, but the producer was firing only at epoch boundary — every step inside the epoch observed (steps_per_epoch − 1)-step-stale alpha values, with the EMA chain barely accumulating past sentinel between rare per-epoch updates. The plan §2550 explicitly specifies per-step cadence; the existing wire violated the plan. Fix (atomic, graph-capture-safe): - MOVED launch_sp13_aux_dir_metrics, launch_sp14_q_disagreement_update, launch_sp14_alpha_grad_compute from process_epoch_boundary into fused_training.rs:submit_aux_ops, immediately after populate_q_out. submit_aux_ops captures into the aux_child sub-graph, so each parent-graph replay re-fires the full producer chain — restoring per-step cadence. - launch_sp13_aux_dir_metrics had to migrate alongside the SP14 launches: alpha_grad_compute_kernel consumes its outputs (ISV[373/374]); leaving sp13 per-epoch while moving SP14 per-step would re-introduce the same staleness bug for aux_dir_acc reads (atomic dependency migration per feedback_no_partial_refactor). - Per-epoch launch_sp14_gradient_hack_detect circuit breaker stays in process_epoch_boundary — its lockout decrement IS one-per-epoch by design. - Forward consumers (6 launch_sp14_dir_concat_qaux sites) and backward consumers (2 launch_sp14_scale_wire_col sites) unchanged — they read the same ISV[393], but now see live per-step values instead of per-epoch staleness. Verification: - cargo check -p ml --tests --all-targets clean (no errors, no new warnings). - All 6 SP14 oracle GPU tests pass (alpha_grad_adaptive_beta, alpha_grad_schmitt_hysteresis, dir_concat_qaux_correct, gradient_hack_circuit_breaker_fires, q_disagreement_all_hold_no_contribution, q_disagreement_k4_k2_mapping). - HEALTH_DIAG validation pending L40S re-dispatch — expect alpha_smoothed to track real EGF-driven values (~0.5 in steady state). Invariants: - pearl_no_host_branches_in_captured_graph (kernels are pure GPU state machines using launch_builder + pre-loaded CudaFunction; no per-call load_cubin) - feedback_no_partial_refactor (sp13 + 2 SP14 launches migrated atomically) - feedback_wire_everything_up (all 3 producers now production hot-path, re-fire on every parent-graph replay) - feedback_isv_for_adaptive_bounds (no warmup_gate parameter — variance- driven k_aux/k_q in alpha_grad_compute_kernel handles cold-start adaptively, perc0fc28e45) Refs: train-v8ztm trajectory analysis 2026-05-07T15:59:49 HEALTH_DIAG[10] showed dir_entropy=0.6545 kill-fast breach with model converging to 64% Hold + 84% Quarter magnitude — exactly the pathology B.11 was designed to prevent by routing aux's directional signal into Q. 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;