foxhunt/docs/dqn-wire-up-audit.md

DQN v2 Wire-Up Audit

Status: Populated during Plan 1 Task 6 (A.5 orphan audit). Updated on every commit per Invariant 7.

Legend:

• Wired — consumed by production training + val path.
• Partial — consumed on one side only (training OR val, or forward OR backward).
• Orphan — built but no production consumer in the DQN path.
• Ghost — consumer path is stubbed or only loads the kernel without launching it.
• OUT-of-DQN-scope — has production consumers outside the DQN path (supervised, PPO, etc.); correct-as-is per Part E.

DQN-path definition: trainers/dqn/ (training loop + fused CUDA ops) + cuda_pipeline/ (GPU kernels wired into those trainers) + validation/ harness when evaluating DQN policy.

Task 2 / Task 3 Seed Rows (preserved)

Module / kernel	Consumer path	Classification	Notes	Action
trainers/dqn/state_reset_registry.rs	training_loop.rs::reset_named_state via fold_reset_entries() + soft_reset_entries()	Wired	A.1 primary implementation (Plan 1 Task 3)	—
training_loop.rs::reset_named_state	consumer of StateResetRegistry::fold_reset_entries + soft_reset_entries	Wired	A.1 Task 3 dispatch + D.5 SoftReset dispatch	—
D.5 SoftReset dispatch (isv_grad_balance_targets, isv_grad_scale_limit)	training_loop.rs::reset_named_state — writes bootstrap values (1.0 for targets, 2.0 for limit). EMA-based convergence from bootstrap over ~50 epochs via grad_balance_isv_update kernel's adaptive-rate EMA (α ∈ [0.01, 0.30], implicit decay_bars).	Wired	Plan 2 Task 4 D.5	—

DQN Core Training Modules

Module / kernel	Consumer path	Classification	Notes	Action
trainers/dqn/mod.rs	train_baseline_rl.rs, ml_training_service, hyperopt/adapters/dqn.rs	Wired	Entry point for DQN trainer	—
trainers/dqn/config.rs (DQNHyperparameters, DQNAgentType)	trainer/constructor.rs, fused_training.rs, smoke tests, hyperopt adapter	Wired	Core config struct	—
trainers/dqn/data_loading.rs	re-exported via dqn::mod; consumed by train_baseline_rl.rs	Wired	DBN + fxcache loading	—
trainers/dqn/early_stopping.rs (EarlyStopping)	trainer/mod.rs, trainer/constructor.rs	Wired	Patience-based stopping	—
trainers/dqn/features.rs	trainer/constructor.rs via extract_features_from_bars	Wired	Feature extraction for DQN	—
trainers/dqn/financials.rs	trainer/metrics.rs via compute_epoch_financials	Wired	Sharpe / drawdown per epoch	—
trainers/dqn/fused_training.rs (FusedTrainingCtx)	trainer/training_loop.rs, trainer/mod.rs	Wired	Core DQN fused-CUDA context	—
trainers/dqn/lr_scheduler.rs (LRScheduler)	trainer/mod.rs::lr_scheduler, trainer/constructor.rs	Wired	Learning-rate schedule	—
trainers/dqn/monitoring.rs (MonitoringSummary)	trainer/training_loop.rs, trainer/metrics.rs	Wired	Per-epoch action / Q monitoring	—
trainers/dqn/risk.rs	trainer/constructor.rs via DrawdownMonitor, HybridPositionLimiter	Wired	Risk controllers in training	—
trainers/dqn/statistics.rs (FeatureStatistics, QValueStats)	trainer/mod.rs, trainer/metrics.rs	Wired	Feature norm + Q stats	—
trainers/dqn/adversarial_self_play.rs (AdversarialSaboteur)	trainer/constructor.rs, trainer/mod.rs	Wired	Adversarial perturbation during training	—
trainers/dqn/expert_demos.rs (ExpertDemoGenerator)	trainer/training_loop.rs	Wired	Expert demo ratio injection	—
trainers/dqn/trainer/mod.rs (DQNTrainer)	train_baseline_rl.rs, service layer	Wired	Top-level trainer struct	—
trainers/dqn/trainer/constructor.rs	internal to DQNTrainer	Wired	Builder / init	—
trainers/dqn/trainer/action.rs	internal to DQNTrainer	Wired	Action selection helpers	—
trainers/dqn/trainer/enrichment.rs	internal to DQNTrainer	Wired	State enrichment	—
trainers/dqn/trainer/metrics.rs	training_loop.rs	Wired	Epoch-boundary metric compilation + backtest eval	—
trainers/dqn/trainer/state.rs	trainer/mod.rs	Wired	Mutable training state	—
trainers/dqn/trainer/training_loop.rs	called from DQNTrainer::train()	Wired	Main epoch loop	—
trainers/dqn/adaptive_monitor.rs	Read-only observer trait + harness (FireRateStats, DiagSnapshot, IsvBus<'a>); consumers added in Plan 1 Tasks 9-17 (atoms/gamma/kelly_cap/tau/epsilon/grad_balancer monitors)	Wired (consumers added in same plan)	C.6 GPU-drives-CPU-reads	—
trainers/dqn/monitors/grad_balancer_monitor.rs	Read-only observer for grad_balance_isv_update kernel output (ISV slots 31..35); consumers: HEALTH_DIAG + controller_activity smoke	Wired	Plan 1 Task 17	—
trainers/dqn/monitors/tau_monitor.rs	Read-only observer for tau_update kernel output (ISV slot 42); consumers: HEALTH_DIAG + controller_activity smoke	Wired	Plan 1 Task 13	—
cuda_pipeline/tau_update_kernel.cu	GpuDqnTrainer::launch_tau_update → FusedTrainingCtx::launch_tau_update → training_loop.rs epoch boundary	Wired	Plan 1 Task 13 — cold-path (per-epoch)	—
trainers/dqn/monitors/epsilon_monitor.rs	Read-only observer for epsilon_update kernel output (ISV slot 41); consumers: HEALTH_DIAG + controller_activity smoke	Wired	Plan 1 Task 14	—
cuda_pipeline/epsilon_update_kernel.cu	GpuDqnTrainer::launch_epsilon_update → FusedTrainingCtx::launch_epsilon_update → training_loop.rs epoch boundary	Wired	Plan 1 Task 14 — cold-path (per-epoch)	—
trainers/dqn/monitors/gamma_monitor.rs	Read-only per-branch observer; mean of ISV[43..47) = [GAMMA_DIR,MAG,ORD,URG]; consumers: HEALTH_DIAG + controller_activity smoke	Wired	Plan 2 Task 3 D.2	—
cuda_pipeline/per_branch_gamma_update_kernel.cu	GpuDqnTrainer::launch_per_branch_gamma_update → FusedTrainingCtx::launch_per_branch_gamma_update → training_loop.rs epoch boundary	Wired	Plan 2 Task 3 D.2 — cold-path (per-epoch); replaces scalar gamma_update_kernel.cu	—
trainers/dqn/monitors/kelly_cap_monitor.rs	Read-only observer for kelly_cap_update kernel output (ISV slot 47); consumers: HEALTH_DIAG + controller_activity smoke	Wired	Plan 1 Task 11	—
cuda_pipeline/kelly_cap_update_kernel.cu	GpuDqnTrainer::launch_kelly_cap_update → FusedTrainingCtx::launch_kelly_cap_update → training_loop.rs epoch boundary; takes portfolio_states dev ptr + n_envs	Wired	Plan 1 Task 11 — cold-path (per-epoch)	—
trainers/dqn/monitors/atoms_monitor.rs	Read-only observer for atoms_update kernel inputs (ISV v-range slots 23..31); consumers: HEALTH_DIAG + controller_activity smoke	Wired	Plan 1 Task 9 (test initial-value fixup post-land; local-smoke StateResetRegistry dispatch arms + hp_f32 helper added)	—
cuda_pipeline/atoms_update_kernel.cu	GpuDqnTrainer::launch_atoms_update → FusedTrainingCtx::launch_atoms_update → training_loop.rs epoch boundary; replaces per-branch recompute_atom_positions CPU loop	Wired	Plan 1 Task 9 — cold-path (per-epoch + SGD step)	—
cuda_pipeline/q_quantile_kernel.cu	GpuDqnTrainer::launch_q_quantile_reduce → FusedTrainingCtx::launch_q_quantile_reduce → training_loop.rs epoch boundary; reads q_out_buf [N, total_actions], writes ISV[50..58) P5/P95 EMAs per branch	Wired	Plan 2 Task 1 C.1 — cold-path (per-epoch); consumer: update_eval_v_range reads Q_P05/Q_P95 to set quantile-based half-width	—
trainers/dqn/monitors/reward_component_monitor.rs	Read-only observer for reward_component_ema kernel output (ISV slots 63..69); consumers: HEALTH_DIAG reward_split line + controller_activity smoke fire-rate tracking	Wired	Plan 3 Task 1 C.2	—
cuda_pipeline/reward_component_ema_kernel.cu	GpuExperienceCollector::launch_reward_component_ema_inplace → training_loop.rs (called after reward_contrib_fractions, before HEALTH_DIAG); reads reward_components_per_sample [N*L, 6], writes ISV[63..69) adaptive EMA (α=0.05)	Wired	Plan 3 Task 1 C.2 — hot-path (per-step); single-block 6-thread kernel	—
B.1 Flat opp-cost ISV scaling (experience_kernels.cu Flat branch)	experience_env_step kernel (Flat position=0, not segment_complete branch); writes rc[4] to reward_components_per_sample; consumed by reward_component_ema → ISV[67]	Wired	Plan 3 Task 2 B.1 — opp-cost multiplied by isv_signals_ptr[21] (Q_DIR_ABS_REF_INDEX, EMA of max(	Q_mean
cuda_pipeline/trade_rate_ema_kernel.cu	GpuExperienceCollector::launch_trade_attempt_rate_ema_inplace → training_loop.rs (called alongside launch_reward_component_ema_inplace each epoch); reads flat_to_pos_per_sample [N*L], reduces count on-GPU, writes ISV[TRADE_ATTEMPT_RATE_EMA_INDEX=71] adaptive EMA (α=α_base × (1+0.5×|sharpe|), α_base=0.05)	Wired	Plan 3 Task 3 B.2 — cold-path (per-epoch); single-block 1-thread reduction + EMA. No atomicAdd.	—
B.2 Flat→Positioned novelty bonus (experience_kernels.cu trade-lifecycle block)	experience_env_step kernel (entering_trade path, AFTER opp-cost branch, BEFORE drawdown penalty); writes rc[5] and adds shaping_scale × bonus to reward; consumed by reward_component_ema → ISV[68] and drives PopArt denominator via total_reward_per_sample	Wired	Plan 3 Task 3 B.2 — novelty = max(0, 1 − ISV[71]/max(1e-4, ISV[72])); bonus = conviction_core × vol_proxy × novelty; TRADE_TARGET_RATE frozen at epoch 5 from measured attempt-EMA (min 0.001). No tuned multiplier.	—
C.4 Temporal timing bonus on trade exit (experience_kernels.cu segment_complete block)	experience_env_step kernel (inside segment_complete && segment_hold_time > 0 block, AFTER Layer 2–9 credits land in reward); accumulates += into rc[5] and adds timing_bonus to reward; consumed by reward_component_ema → ISV[68] (shares the bonus slot with B.2 — different (i,t) slots, so += is idempotent)	Wired	Plan 3 Task 5 C.4 — bars_early = max(0, segment_hold_time − PS_PEAK_PNL_BAR); timing_bonus = shaping_scale × (bars_early / segment_hold_time) × |final_pnl| × conviction_core. Peak bar tracked in new portfolio-state slot PS_PEAK_PNL_BAR=38 (PS_STRIDE 38→39), snapshot alongside every MAX_PNL update, reset at every MAX_PNL reset site (entry/reverse/fold-reset). No tuned multiplier.	—
D.4a Persistence credit on profitable drawdown recovery (experience_kernels.cu segment_complete block)	experience_env_step kernel (inside segment_complete && segment_hold_time > 0 block, IMMEDIATELY AFTER C.4 timing bonus, gated on reward > 0 AND drawdown_depth > 1e-6); accumulates += into rc[5] and adds persist_bonus to reward; consumed by reward_component_ema → ISV[68] (shares the bonus slot with B.2 entry + C.4 exit — different (i,t) slots per trade, so += is idempotent across all three)	Wired	Plan 3 Task 6a D.4a — drawdown_depth = max(0, −PS_INTRA_TRADE_MIN_PNL); persist_bonus = shaping_scale × conviction_core × drawdown_depth × tanh(reward / max(1e-4, drawdown_depth)). MIN_PNL tracked in new portfolio-state slot PS_INTRA_TRADE_MIN_PNL=39 (PS_STRIDE 39→40), updated per-bar in the same block as MAX_PNL (fminf against pnl_pct), reset at every MAX_PNL reset site (entry/reverse/fold hard-reset/trade-complete soft-reset). Self-scaling tanh: saturates +1 when reward ≫ drawdown, ≈0 when reward ≈ drawdown. No tuned multiplier.	—
D.4b Regime-shift penalty for trades held past a regime flip (experience_kernels.cu segment_complete block)	experience_env_step kernel: detector inside the active-trade block (after MIN/MAX_PNL update) writes PS_REGIME_SHIFT_BAR on the FIRST bar where |isv[11] − PS_PLAN_ENTRY_REGIME| > clamp(0.25 × |clamp(sharpe, ±2)|, 0.05, 0.5); consumer inside segment_complete block IMMEDIATELY AFTER D.4a accumulates −= into rc[5] and subtracts penalty from reward; consumed by reward_component_ema → ISV[68] (cancellation within rc[5] vs B.2/C.4/D.4a is intentional — that's what ISV[68] REWARD_BONUS_EMA tracks). Consumer also resets PS_REGIME_SHIFT_BAR = 0 after use.	Wired	Plan 3 Task 6b D.4b — bars_late = max(0, saved_hold_time − PS_REGIME_SHIFT_BAR); penalty = shaping_scale × conviction_core × (bars_late / saved_hold_time) × ISV[Q_DIR_ABS_REF_INDEX=21] × |reward|. Shift-bar tracked in new portfolio-state slot PS_REGIME_SHIFT_BAR=40 (PS_STRIDE 40→41), reset at every MAX_PNL reset site (entry/reverse/fold hard-reset/trade-complete soft-reset) PLUS post-consumer (defensive). Self-scaling Q_DIR_ABS_REF coefficient (same B.1 pattern). Adaptive threshold tightens when |sharpe| is high. First-shift-only. No tuned multiplier.	—
D.4c Conviction consistency bonus on Flat→Positioned entry (experience_kernels.cu Flat branch + entering_trade block)	experience_env_step kernel: producer inside the Flat branch (!segment_complete && position≈0, before opp-cost) updates two PS slots PS_PRE_ENTRY_CONVICTION_EMA (mean) and PS_PRE_ENTRY_CONVICTION_VAR_EMA (variance) via Welford-style EMA at α=0.05 on conviction_core. Consumer inside entering_trade block IMMEDIATELY AFTER B.2 novelty bonus computes ratio = stddev/mean; when ratio < 0.2 accumulates += into rc[5] and adds bonus to reward; consumed by reward_component_ema → ISV[68] (shares the bonus slot with B.2/C.4/D.4a/D.4b at different (i,t) slots; += is idempotent). Both EMA slots reset at every trade-lifecycle reset site (entry/reverse/fold hard-reset/trade-complete soft-reset) PLUS post-consumer (defensive).	Wired	Plan 3 Task 6c D.4c — bonus = shaping_scale × vol_proxy × stability × conviction_core where vol_proxy ∈ [0.0001, 0.01], stability = clamp(0, 1, 1 − ratio/0.2), conviction_core ∈ [0,1]. Mirrors B.2 novelty-bonus structure exactly — ONE technically unbounded multiplicand (vol_proxy, bounded ≤ 0.01), all others bounded — per pearl_one_unbounded_signal_per_reward.md. Max bonus ≈ 0.01, same order as B.2. New portfolio-state slots PS_PRE_ENTRY_CONVICTION_EMA=41 and PS_PRE_ENTRY_CONVICTION_VAR_EMA=42; PS_STRIDE 41→43; PORTFOLIO_STRIDE 41→43 in all consumers (experience_kernels.cu, trade_stats_kernel.cu, gpu_experience_collector.rs, gpu_dqn_trainer.rs::launch_kelly_cap_update, ml-core::state_layout.rs). The 0.2 stability threshold is the single tuned constant retained for this first cut (commented; reviewer may demote to ISV-driven follow-up). α=0.05 matches Plan 3 Task 1 reward-EMA convention.	—
cuda_pipeline/plan_threshold_update_kernel.cu	GpuExperienceCollector::launch_plan_threshold_update_inplace → training_loop.rs (called alongside launch_reward_component_ema_inplace and launch_trade_attempt_rate_ema_inplace each epoch); reads readiness_per_sample [N*L] written by experience_env_step, reduces mean on-GPU, writes ISV[READINESS_EMA_INDEX=75] adaptive EMA (α=α_base × (1+0.5×|sharpe|), α_base=0.05), then derives ISV[PLAN_THRESHOLD_INDEX=49] = max(0.1, 0.5 × ema). Producer-only upgrade — consumer kernels (experience_kernels.cu 4 sites + backtest_plan_kernel.cu 1 site) unchanged.	Wired	Plan 3 Task 4 B.4 — cold-path (per-epoch); single-block 1-thread reduction + EMA. No atomicAdd.	—
trainers/dqn/monitors/plan_threshold_monitor.rs	Read-only observer for plan_threshold_update kernel output (ISV slots 49 + 75); consumers: HEALTH_DIAG plan_threshold.eff / plan_threshold.readiness_ema + controller_activity smoke fire-rate tracking	Wired	Plan 3 Task 4 B.4	—
cuda_pipeline/state_kl_divergence_kernel.cu	GpuExperienceCollector::launch_state_kl_inplace → training_loop.rs (called once per validation epoch, AFTER compute_validation_loss populates chunked_states_buf, BEFORE next training epoch's reward kernels read ISV[79]); reads train-state sample (states_out) and val-state batch (chunked_states_buf from gpu_evaluator.val_state_sample()), per-OFI-dim Gaussian moment-match KL summed across [SL_OFI_START, SL_OFI_START+SL_OFI_DIM); writes ISV[STATE_KL_TRAIN_VAL_EMA=78] (adaptive EMA, α_base=0.05) + ISV[STATE_KL_AMPLIFICATION=79] (kernel-internal trailing-self ratio → 1 + clamp(ratio−1, 0, 1) ∈ [1,2]). Single-block, one thread per OFI dim. No atomicAdd, no DtoH.	Wired	Plan 3 Task 7 C.3 — cold-path (per validation epoch). All α/threshold coefficients ISV-derived; no tuned constants per feedback_isv_for_adaptive_bounds.md.	—
C.3 B.1/B.2 KL-amp consumers (experience_kernels.cu Flat opp_cost + entering_trade B.2 bonus)	experience_env_step kernel: B.1 site multiplies reward = -shaping_scale × holding_cost_rate × conviction_core × vol_proxy_flat × q_abs_ref × kl_amp_b1; B.2 site multiplies bonus_scaled = shaping_scale × bonus × kl_amp_b2. Both consumers use fmaxf(1.0, isv_signals_ptr[ISV_STATE_KL_AMP_IDX]) to no-op against cold-start. ISV_STATE_KL_AMP_IDX=79 macro defined in state_layout.cuh.	Wired	Plan 3 Task 7 C.3 — bounded amp ∈ [1, 2] stacks safely with B.1's existing q_abs_ref unbounded multiplicand per pearl_one_unbounded_signal_per_reward.md.	—
trainers/dqn/monitors/state_kl_monitor.rs	Read-only observer for state_kl_moment_match kernel output (ISV slots 78 + 79); consumers: HEALTH_DIAG state_kl.train_val_ema / state_kl.amp + controller_activity smoke fire-rate tracking	Wired	Plan 3 Task 7 C.3	—
cuda_pipeline/scripted_policy_kernel.cu	GpuExperienceCollector::launch_timestep_loop (per-timestep dispatch when seed_phase_active_cache=true); 4 policies (UNIFORM 40% / MOMENTUM 20% / MEAN_REV 20% / VWAP_DEV 20%) deterministically mixed by i % 5. Per-thread one sample. Reads batch_states[i, MARKET_START] for current close + portfolio_states[i, PS_PREV_CLOSE] for prev close; writes batch_actions[i] (factored dir*27 + mag*9 + ord*3 + urg) and conviction_buf[i] ∈ [0, 1] — same outputs the network's experience_action_select would have written. Direction-flip thresholds (±0.0001f momentum/reversal, ±5bp VWAP band) are noise-floor cutoffs, not scaling coefficients.	Wired	Plan 3 Task 8 B.3 — GPU-only seeded warm-start. CPU only orchestrates per-epoch dispatch (cold-path read of ISV[SEED_STEPS_DONE/TARGET]); the action computation itself is 100% GPU. No CPU physics mirror — existing experience_env_step runs unchanged.	—
cuda_pipeline/seed_step_counter_update_kernel.cu	GpuExperienceCollector::launch_seed_step_counter_update_inplace → training_loop.rs (called alongside the other Plan 3 EMA producers each epoch); single-block single-thread cold-path. Increments ISV[SEED_STEPS_DONE_INDEX=83] by n_samples, capped at ISV[SEED_STEPS_TARGET_INDEX=82], and EMAs the derived max(0, 1 - DONE/TARGET) into ISV[SEED_FRAC_EMA_INDEX=84]. Adaptive α=α_base × (1+0.5×|clamp(sharpe,−2,2)|), α_base=0.05.	Wired	Plan 3 Task 8 B.3 — cold-path (per-epoch). No atomicAdd. SEED_FRAC_EMA cold-start 1.0 ensures Task 9's CQL ramp sees target=0 until the seed phase actually decays.	—
cuda_pipeline/cql_alpha_seed_update_kernel.cu	GpuExperienceCollector::launch_cql_alpha_seed_update_inplace → training_loop.rs (called alongside launch_seed_step_counter_update_inplace each epoch); single-block single-thread cold-path. EMAs ISV[CQL_ALPHA_INDEX=48] toward cql_alpha_final × max(0, 1 - ISV[SEED_FRAC_EMA_INDEX=84]) where cql_alpha_final = config.cql_alpha. Adaptive α matches Task 8 convention.	Wired	Plan 3 Task 9 C.5 — producer upgrade for ISV[CQL_ALPHA_INDEX=48]: SchemaContract → FoldReset. CQL gradient kernel consumer (already reading slot 48 per Plan 1 Task 12) unchanged.	—
trainers/dqn/monitors/seed_monitor.rs	Read-only observer for seed_step_counter_update kernel output (ISV slots 82 / 83 / 84); consumers: HEALTH_DIAG seed.steps_target / seed.steps_done / seed.frac_ema + controller_activity smoke fire-rate tracking	Wired	Plan 3 Task 8 B.3	—
trainers/dqn/monitors/cql_alpha_monitor.rs	Read-only observer for cql_alpha_seed_update kernel output (ISV slot 48 + dependency 84); consumers: HEALTH_DIAG cql_alpha.eff / cql_alpha.seed_frac + controller_activity smoke fire-rate tracking	Wired	Plan 3 Task 9 C.5	—

CUDA Pipeline — Rust Wrappers

Module / kernel	Consumer path	Classification	Notes	Action
cuda_pipeline/mod.rs (DqnGpuData, upload_slices)	trainer/constructor.rs, fused_training.rs	Wired	Data upload to GPU	—
cuda_pipeline/gpu_dqn_trainer.rs (GpuDqnTrainer)	fused_training.rs, trainer/mod.rs	Wired	Core GPU-side DQN weight / forward / grad ops	—
cuda_pipeline/fused_training.rs wrapper (FusedTrainingCtx)	trainer/training_loop.rs	Wired	Wires all GPU ops into epoch loop	—
cuda_pipeline/batched_forward.rs	gpu_dqn_trainer.rs — forward graph node	Wired	Batched SGEMM forward pass	—
cuda_pipeline/batched_backward.rs	gpu_dqn_trainer.rs — backward graph node	Wired	Batched SGEMM backward pass (KAN gate included)	—
cuda_pipeline/shared_cublas_handle.rs	fused_training.rs, gpu_iqn_head.rs, gpu_iql_trainer.rs, gpu_attention.rs, gpu_curiosity_trainer.rs (10 consumers)	Wired	Shared cuBLAS/cuBLASLt handle	—
cuda_pipeline/cublas_algo_deterministic.rs	gpu_dqn_trainer.rs, gpu_curiosity_trainer.rs, gpu_iqn_head.rs (7 consumers)	Wired	Deterministic cuBLASLt algo selection	—
cuda_pipeline/gpu_weights.rs	trainer/mod.rs, gpu_dqn_trainer.rs, hyperopt/adapters/dqn.rs (11 consumers)	Wired	Weight tensor layout constants	—
cuda_pipeline/gpu_action_selector.rs (GpuActionSelector)	trainer/mod.rs, fused_training.rs, gpu_backtest_evaluator.rs	Wired	Epsilon-greedy + routed action selection	—
cuda_pipeline/gpu_monitoring.rs (GpuMonitor)	fused_training.rs, trainer/metrics.rs, trainer/training_loop.rs	Wired	GPU-side monitoring_reduce launch	—
cuda_pipeline/gpu_training_guard.rs	gpu_dqn_trainer.rs, trainer/training_loop.rs	Wired	NaN / gradient anomaly guard	—
cuda_pipeline/gpu_backtest_evaluator.rs (GpuBacktestEvaluator)	trainer/metrics.rs (eval path)	Wired	Per-epoch GPU backtest evaluation	—
cuda_pipeline/gpu_experience_collector.rs	fused_training.rs via TradeStats, financials.rs	Wired	Per-trade stats from experience buffer	—
cuda_pipeline/gpu_curiosity_trainer.rs	fused_training.rs	Wired	Curiosity-driven intrinsic reward	—
cuda_pipeline/gpu_walk_forward.rs (GpuWalkForwardData)	trainer/mod.rs (lazy-init field)	Wired	GPU walk-forward data structure	—
cuda_pipeline/gpu_her.rs (GpuHer)	fused_training.rs	Wired	Hindsight Experience Replay	—
cuda_pipeline/gpu_iql_trainer.rs (GpuIqlTrainer)	fused_training.rs (dual IQL instances)	Wired	IQL value network training	—
cuda_pipeline/gpu_iqn_head.rs (GpuIqnHead)	fused_training.rs	Wired	IQN distributional head	—
cuda_pipeline/gpu_attention.rs (GpuAttention)	fused_training.rs	Wired	Self-attention layer in DQN trunk	—
cuda_pipeline/gpu_tlob.rs (GpuTlob)	fused_training.rs (training: forward pre-trunk, backward+Adam Phase 6); trainer/metrics.rs (val: set_tlob_from_training per-epoch weight sync + GpuBacktestEvaluator::submit_dqn_step_loop_cublas per-gather TLOB forward)	Wired	D.8 Plan 2 Task 6C — OFI[32]→TLOB[16] single-head SDP attention, Xavier random init, trained end-to-end. ISV[60]=TLOB_REGIME_FOCUS_EMA written at epoch boundary.	—
cuda_pipeline/decision_transformer.rs (DecisionTransformer)	trainer/training_loop.rs	Wired	Decision Transformer pre-training step	—
cuda_pipeline/learning_health.rs (LearningHealth)	fused_training.rs, trainer/training_loop.rs, trainer/constructor.rs, trainer/metrics.rs, meta_q_network.rs (11 consumers)	Wired	Health-band tracking for adaptive LR / early stopping	—
cuda_pipeline/q_snapshot.rs	cuda_pipeline/mod.rs, gpu_dqn_trainer.rs	Wired	Q-value snapshot for double-DQN target	—
cuda_pipeline/meta_q_network.rs (MetaQNetwork)	trainer/constructor.rs, trainer/mod.rs, trainer/training_loop.rs	Wired	Meta-learning Q-network	—
cuda_pipeline/q_value_provider.rs (QValueProvider trait)	fused_training.rs impl, trainer/metrics.rs, hyperopt/adapters/dqn.rs	Wired	Trait for on-device Q-value computation in val path	—
cuda_pipeline/signal_adapter.rs	hyperopt/adapters/dqn.rs, hyperopt/adapters/{ppo,mamba2,tft,kan,liquid,tlob,diffusion,tggn,xlstm}.rs	Wired	backtest_fitness scoring used by all hyperopt adapters	—
cuda_pipeline/multi_gpu.rs (MultiGpuConfig)	trainer/constructor.rs, trainer/mod.rs	Wired	Multi-GPU detection and layout	—
cuda_pipeline/gpu_ppo_collector.rs (GpuPpoExperienceCollector)	trainers/ppo.rs	OUT-of-DQN-scope	PPO-only consumer; correct-as-is	—
cuda_pipeline/gpu_statistics.rs (GpuStatistics)	declared pub in cuda_pipeline/mod.rs; no call site outside the file	Orphan	GpuStatistics::compute never called; BatchStatistics struct unused	Plan 2 D.2 audits + decides whether to wire into val path or delete
cuda_pipeline/cublaslt_debug.rs	mod cublaslt_debug (private) inside cuda_pipeline/mod.rs; test-only	OUT-of-DQN-scope	Private debug/test module; not a public feature	—

CUDA Kernels (.cu files)

Module / kernel	Consumer path	Classification	Notes	Action
epsilon_greedy_kernel.cu (epsilon_greedy_select, epsilon_greedy_routed)	gpu_action_selector.rs	Wired	Action selection kernel	—
monitoring_kernel.cu (monitoring_reduce)	gpu_monitoring.rs	Wired	12-bin action count + Q stats	—
c51_loss_kernel.cu	gpu_dqn_trainer.rs (C51 loss compute)	Wired	C51 distributional loss	—
c51_grad_kernel.cu	gpu_dqn_trainer.rs (C51 backward)	Wired	C51 gradient backward	—
mse_loss_kernel.cu	gpu_dqn_trainer.rs (MSE warmup loss)	Wired	MSE loss for warmup phase	—
mse_grad_kernel.cu	gpu_dqn_trainer.rs (MSE backward)	Wired	MSE gradient backward	—
iqn_dual_head_kernel.cu	gpu_iqn_head.rs	Wired	IQN dual-head quantile computation	—
iqn_cvar_kernel.cu	gpu_iqn_head.rs	Wired	IQN CVaR risk measure	—
iql_value_kernel.cu	gpu_iql_trainer.rs	Wired	IQL value network kernel	—
cql_grad_kernel.cu	gpu_dqn_trainer.rs (CQL backward)	Wired	Conservative Q-learning gradient	—
experience_kernels.cu	gpu_backtest_evaluator.rs (BACKTEST_DQN_CUBIN), gpu_experience_collector.rs	Wired	Experience buffer ops + DQN backtest forward	—
reward_shaping_kernel.cu	gpu_dqn_trainer.rs (reward shaping step)	Wired	Per-sample reward shaping	—
nstep_kernel.cu	gpu_dqn_trainer.rs (n-step return)	Wired	N-step Bellman target	—
backward_kernels.cu	gpu_dqn_trainer.rs (weight backward)	Wired	Weight gradient accumulation	—
bias_kernels.cu	gpu_dqn_trainer.rs (bias grad)	Wired	Bias gradient kernels	—
relu_mask_kernel.cu	gpu_dqn_trainer.rs (activation backward)	Wired	ReLU activation mask	—
ema_kernel.cu	gpu_dqn_trainer.rs (target-net soft update)	Wired	EMA target-network update	—
q_stats_kernel.cu	gpu_dqn_trainer.rs (Q-value stats for ISV)	Wired	Per-branch Q-value statistics	—
dqn_utility_kernels.cu	gpu_dqn_trainer.rs (multiple utility ops)	Wired	Misc DQN GPU ops (advantage std, PopArt, etc.)	—
graph_utility_kernels.cu	gpu_dqn_trainer.rs (CUDA graph utility ops)	Wired	Graph capture helper kernels	—
grad_decomp_kernel.cu	gpu_dqn_trainer.rs, called via fused_training.rs::grad_decomp_launch_c51	Wired	C51 gradient decomposition (Task 2.0 diagnostic)	—
branch_grad_balance_kernel.cu	gpu_dqn_trainer.rs, called via fused_training.rs::launch_branch_grad_balance	Wired	Per-branch gradient L2-norm balancing	—
mamba2_temporal_kernel.cu (mamba2_scan_projected_fwd, mamba2_scan_projected_bwd, isv_temporal_route, etc.)	gpu_dqn_trainer.rs::mamba2_forward + mamba2_backward, called in fused training loop (adam_grad child graph)	Wired (grad-check validated, Plan 2 Task 2)	Mamba2 selective SSM forward + backward (both paths implemented). D.1: kernel-level reference check + non-zero gradient propagation test confirm backward is not silently no-oping.	—
attention_kernel.cu	gpu_attention.rs	Wired	Scaled dot-product attention forward	—
attention_backward_kernel.cu	gpu_attention.rs, gpu_tlob.rs (reused for grad_norm+Adam kernels)	Wired	Attention backward pass	—
tlob_kernel.cu (tlob_sdp_forward, tlob_sdp_backward, tlob_write_states, tlob_read_grad_from_states)	gpu_tlob.rs	Wired	D.8 Plan 2 Task 6C — TLOB SDP forward/backward, state scatter/read kernels	—
training_guard_kernel.cu (training_guard_check_and_accumulate)	gpu_training_guard.rs	Wired	NaN / guard check kernel	—
backtest_env_kernel.cu (backtest_env_step)	gpu_backtest_evaluator.rs (ENV_CUBIN)	Wired	Backtest environment step	—
backtest_metrics_kernel.cu	gpu_backtest_evaluator.rs (METRICS_CUBIN)	Wired	Backtest metrics reduction	—
backtest_plan_kernel.cu (backtest_plan_diag_reduce, backtest_plan_state_isv)	gpu_backtest_evaluator.rs (BACKTEST_PLAN_CUBIN)	Wired	Plan-ISV diagnostic reduction in val. D.6 (Plan 2 Task 6A): backtest_plan_state_isv now writes pisv[6] = remaining_fraction; stride updated to SL_PORTFOLIO_PLAN_DIM=7.	—
backtest_forward_ppo_kernel.cu (backtest_forward_ppo_kernel)	gpu_backtest_evaluator.rs::evaluate_ppo (PPO_FORWARD_CUBIN)	OUT-of-DQN-scope	PPO-path backtest only; correct-as-is	—
backtest_forward_supervised_kernel.cu (signal_to_action_kernel)	gpu_backtest_evaluator.rs::evaluate_supervised (SUPERVISED_SIGNAL_CUBIN)	OUT-of-DQN-scope	Supervised-model backtest only; correct-as-is	—
signal_adapter_kernel.cu	signal_adapter.rs (loaded at construction)	Wired	Signal-to-action ISV bus adapter	—
statistics_kernel.cu (batch_statistics)	gpu_statistics.rs (loaded but GpuStatistics has no call sites outside the file)	Orphan	Kernel compiled, wrapper struct exists, but never instantiated by any consumer	Plan 2 D.2 — same resolution as gpu_statistics.rs
ppo_experience_kernel.cu	gpu_ppo_collector.rs	OUT-of-DQN-scope	PPO collector; correct-as-is	—
her_episode_kernel.cu	gpu_her.rs	Wired	HER episode boundary kernel	—
her_relabel_kernel.cu	gpu_her.rs	Wired	HER goal relabelling kernel	—
curiosity_training_kernel.cu	gpu_curiosity_trainer.rs	Wired	Curiosity intrinsic reward training	—
curiosity_inference_kernel.cu	gpu_curiosity_trainer.rs	Wired	Curiosity inference forward	—
ensemble_kernels.cu	batched_forward.rs / batched_backward.rs (ensemble ops in fused trainer)	Wired	Ensemble head combination	—
dt_kernels.cu	decision_transformer.rs (DT_CUBIN)	Wired	Decision Transformer GPU ops	—
trade_stats_kernel.cu	gpu_experience_collector.rs	Wired	Per-trade statistics accumulation	—
backtest_env_kernel.cu (backtest_env_step)	gpu_backtest_evaluator.rs	Wired	Already listed above	—
common_device_functions.cuh	prepended to all kernels at compile time by build.rs	Wired	Shared device helpers (BF16 wrappers, warp reduce)	—
state_layout.cuh	included by kernels that reference state buffer layout	Wired	Named index constants for state buffer	—
trade_physics.cuh	included by backtest_env_kernel.cu and others at compile time	Wired	Kelly / physics helpers shared across kernels	—

Model Modules — Supervised-Only (OUT-of-DQN-scope per Part E)

Module / kernel	Consumer path	Classification	Notes	Action
xlstm/mod.rs + xlstm/trainable.rs	ensemble/adapters/xlstm.rs, hyperopt/adapters/xlstm.rs	OUT-of-DQN-scope	Supervised consumers; DQN does not import this. Part E: keep.	—
kan/mod.rs + kan/trainable.rs	hyperopt/adapters/kan.rs only	OUT-of-DQN-scope	KAN splines in DQN trunk (batched_forward/backward) are inline in gpu_dqn_trainer.rs and do not import crate::kan. kan/ wraps ml-supervised KAN for hyperopt. Part E: keep.	—
tgnn/mod.rs + tgnn/trainable_adapter.rs	hyperopt/adapters/tggn.rs, risk/mod.rs	OUT-of-DQN-scope	TGNN for supervised + risk path	—
tlob/mod.rs + tlob/trainable_adapter.rs	hyperopt/adapters/tlob.rs, data_loaders/tlob_loader.rs	Partial / OUT-of-DQN-scope	TLOB transformer has hyperopt adapter; DQN trunk wiring scheduled for Plan 2 D.8	Plan 2 D.8 wires TLOB trunk into DQN
diffusion/mod.rs + diffusion/trainable.rs	hyperopt/adapters/diffusion.rs, trainers/dqn/fused_training.rs (data aug only)	Partial	Diffusion model used for data augmentation in fused training but lacks a gradient backward path through DQN trunk	Plan 2 tracks full integration
ppo/mod.rs + ppo/trainable_adapter.rs	trainers/ppo.rs, gpu_ppo_collector.rs	OUT-of-DQN-scope	PPO training path; not DQN	—

Temporal/Recurrent Modules — DQN Integration Status

Module / kernel	Consumer path	Classification	Notes	Action
mamba/mod.rs + mamba/trainable_adapter.rs	trainers/mamba2.rs, model_factory.rs, inference_validator.rs; Mamba2 temporal kernel wired fully in gpu_dqn_trainer.rs	Wired (DQN trunk) + OUT-of-DQN-scope (supervised trainers/mamba2.rs)	Forward and backward both implemented in mamba2_temporal_kernel.cu	—
liquid/mod.rs + liquid/adapter.rs	trainers/liquid.rs supervised path only	Deleted (DQN)	D.7 audit (2026-04-24): liquid_tau_rk4_step kernel was a mathematical identity — ODE f(x)=(1/tau)*(1-x) has fixed point x=1.0; initialised to 1.0, it never deviated. liquid_mod_buf always held [1.0,1.0,1.0,1.0], so velocity_mod in c51_grad_kernel multiplied spread_scale by 1.0 unconditionally. No ISV slot existed (violates §4.C.6). Kernel, buf, and call removed. LiquidTrainableAdapter (supervised path) unchanged.
tft/mod.rs + tft/trainable_adapter.rs	trainers/tft/trainer.rs has full forward + backward_step(); TrainableTFT::backward() returns error with explicit message that backward is via TFTTrainer::train()	Partial	TFT backward available via dedicated trainer; UnifiedTrainable::backward() slot returns informational error (not a stub — documented routing)	Plan 4 E.1/E.3 — DQN concept adoption; full backward path tracked in task #76

Data Pipeline Modules

Module / kernel	Consumer path	Classification	Notes	Action
fxcache.rs (discover_and_load, FxCacheData)	train_baseline_rl.rs, trainers/dqn/data_loading.rs	Wired	Primary training data format	—
feature_cache.rs	fxcache.rs (cache key), hyperopt/adapters/dqn.rs, smoke tests	Wired	Cache key generation for .fxcache auto-discovery	—
data_pipeline/ (DatasetManager, PreparedDataset)	lib.rs prelude re-export; integration/ modules	Wired	Dataset management abstraction	—
data_loaders/dbn_sequence_loader.rs	data_loaders/mod.rs; internally used by dbn_tick_adapter.rs	Wired	DBN sequence loading for supervised models	—
data_loaders/dbn_tick_adapter.rs	data_loaders/mod.rs	Wired	DBN tick-to-bar adapter	—
data_loaders/streaming_dbn_loader.rs	data_loaders/mod.rs; tests/streaming_pipeline_edge_cases.rs (test consumer)	Partial	Test-only consumer at tests/streaming_pipeline_edge_cases.rs; if streaming DBN load is genuinely needed for production (memory-efficient data loading), wire into production path in a follow-up task; otherwise schedule for deletion after test utility is moved to dbn_sequence_loader.	Reclassified Partial — test consumer confirmed
data_loaders/tlob_loader.rs (TLOBDataLoader)	data_loaders/mod.rs; no external call sites found	Orphan	TLOB data loader built but not called outside its own module and mod.rs	Plan 2 D.8 may wire; surface for user review
training/unified_trainer.rs (UnifiedTrainable trait)	diffusion/trainable.rs, tlob/trainable_adapter.rs, mamba/trainable_adapter.rs, tft/trainable_adapter.rs, hyperopt adapters	Wired	Unified training trait for supervised models	—
training/unified_data_loader.rs	—	—	Deleted (Task 6): zero production + zero test consumers confirmed. pub mod unified_data_loader removed from training.rs.	DELETED
training/orchestrator.rs (TrainingOrchestrator)	—	—	Deleted (Task 6): zero production + zero test consumers confirmed. pub mod orchestrator removed from training.rs.	DELETED
training_pipeline.rs (ProductionTrainingError)	lib.rs (two From impls: MLError + MLSafetyError); TrainingPipeline struct has no external instantiation	Partial	ProductionTrainingError used via From impl in lib.rs; TrainingPipeline struct itself may be dead — follow-up to extract error enum and delete struct separately.	Reclassified Partial — error type wired, struct possibly unused
training_profile.rs (DqnTrainingProfile)	train_baseline_rl.rs, hyperopt/adapters/dqn.rs, smoke tests	Wired	TOML-backed hyperparameter profiles	—
walk_forward.rs (FoldRange, WalkForwardConfig)	train_baseline_rl.rs, validation/harness.rs	Wired	Walk-forward fold management	—

Evaluation / Validation Modules

Module / kernel	Consumer path	Classification	Notes	Action
validation/mod.rs (ValidatableStrategy)	validation/harness.rs, hyperopt/adapters/dqn.rs	Wired	Validation trait + entry points	—
validation/harness.rs	hyperopt/adapters/dqn.rs, trainer/metrics.rs	Wired	Walk-forward validation harness	—
validation/financial.rs	validation/harness.rs	Wired	Financial metric computation for val	—
validation/regime_analysis.rs	validation/harness.rs	Wired	Regime-conditional eval	—
validation/ppo_adapter.rs	validation/harness.rs	OUT-of-DQN-scope	PPO-specific validation path	—
evaluation/mod.rs	hyperopt/adapters/dqn.rs, trainer/metrics.rs, gpu_backtest_evaluator.rs	Wired	DQN evaluation engine	—
backtesting/ (GpuBacktestEvaluator, BarrierBacktester)	trainer/metrics.rs, hyperopt/adapters/*	Wired	Backtesting framework	—

Supporting Infrastructure Modules

Module / kernel	Consumer path	Classification	Notes	Action
checkpoint/mod.rs	model_registry.rs	Wired	Checkpoint save/load	—
inference.rs	multiple service consumers (67 files by grep)	Wired	Inference entry points	—
inference_validator.rs (InferenceValidator)	—	—	Deleted (Task 6): zero production + zero test consumers confirmed. No pub mod declaration found in any file.	DELETED
model_factory.rs	mamba path, lib.rs	Wired	Model construction factory	—
model_loader_integration.rs (ModelLoaderTrait)	—	—	Deleted (Task 6): zero production + zero test consumers confirmed. No pub mod declaration found in any file.	DELETED
model_registry.rs	lib.rs, various consumers (3 by grep)	Wired	Model lifecycle registry	—
registry/mod.rs	consumed by integration and service layer (10 consumers)	Wired	Operational model lifecycle	—
hyperopt/ (campaign, adapters)	trainer/training_loop.rs, hyperopt smoke test	Wired	Bayesian hyperparameter optimisation	—
ensemble/	integration path, service layer	Wired	Multi-model ensemble	—
integration/	service layer	Wired	Service integration layer	—
flash_attention/mod.rs (FlashAttention3)	transformers/hft_transformer.rs, trainers/tft/config.rs, benchmark/tft_benchmark.rs	OUT-of-DQN-scope	TFT / transformer path only; DQN uses gpu_attention.rs	—
features/ (FeatureVector, extract_ml_features)	train_baseline_rl.rs, DQN trainer	Wired	Feature extraction	—
microstructure/	trainers/dqn/features.rs, OFI pipeline	Wired	VPIN, Kyle lambda, order-flow imbalance	—
regime/mod.rs	DQN features, data pipeline (50 consumers)	Wired	Regime detection + signal	—
regime_detection/mod.rs	lib.rs declaration only; zero consumers of the facade; zero direct uses of ml_regime_detection:: anywhere else	Orphan	Thin facade re-exporting ml-regime-detection crate. Zero consumers of the facade, zero direct uses of ml_regime_detection:: anywhere. The underlying ml-regime-detection/ workspace crate exists but appears unused. Deleting a whole workspace crate exceeds Task 6 scope — scheduled for a separate crate-cleanup task. Potentially superseded by ml_regime/ crate (50 consumers).	CRATE-LEVEL-FOLLOWUP
risk/mod.rs (crate-level)	DQN trainer constructor via DrawdownMonitor, KellyCriterionOptimizer	Wired	Risk management in training	—
preprocessing.rs	features/, data pipeline	Wired	Log-return normalisation, outlier clipping	—
labeling/mod.rs	features/, supervised training	Wired	Triple-barrier labelling	—
security/mod.rs	lib.rs (1 consumer)	Partial	ML security / prediction validation; no DQN consumer found	Surface for user review
stress_testing/mod.rs	dqn/stress_testing.rs, ppo/stress_testing.rs	Wired	Stress-testing framework	—
paper_trading/mod.rs	lib.rs declaration; tests/paper_trading_integration_test.rs (test consumer)	Partial	Test-only consumer at tests/paper_trading_integration_test.rs. Production trading-service uses its own paper_trading_executor (distinct module); this facade exists only for ML crate tests.	Reclassified Partial — test consumer confirmed
observability/mod.rs	integration/performance_monitor.rs	Wired	ML observability hooks	—
deployment/	lib.rs, integration layer	Wired	Model deployment + A/B testing	—
universe/mod.rs	lib.rs, integration layer (5 consumers)	Wired	Asset universe management	—
asset_selection/mod.rs	lib.rs, integration layer (referenced in universe path)	Wired	Asset selection logic	—
batch_processing.rs	lib.rs, integration/ (multiple consumers)	Wired	Batch ML operations	—
bridge.rs	lib.rs, trading-ML bridge	Wired	ML-Financial type bridge	—
data_loader.rs	lib.rs, training service	Wired	Legacy data loader shim	—
data_validation/mod.rs	lib.rs, data pipeline	Wired	Input validation for data pipeline	—
explainability/mod.rs	lib.rs only (1 consumer)	Partial	SHAP / attribution; no DQN call site found	Surface for user review
benchmarks.rs	lib.rs, benchmark harness	OUT-of-DQN-scope	Benchmark entry point; not production training	—
benchmark/	benchmark harness	OUT-of-DQN-scope	GPU / model benchmarks; not production DQN path	—
portfolio_transformer.rs	—	—	Deleted (Task 6): zero production + zero test consumers confirmed. pub mod portfolio_transformer removed from lib.rs.	DELETED

Summary

Updated after Task 6 cleanup (2026-04-24): 5 confirmed-orphan files deleted, 3 Orphan rows reclassified Partial, 1 Orphan reclassified with crate-level follow-up action. Plan 1 Task 8 (revised, 2026-04-24): adaptive_controller.rs renamed → adaptive_monitor.rs; AdaptiveController replaced with read-only AdaptiveMonitor per spec §4.C.6 (GPU drives, CPU reads).

Plan 3 Task 1 C.2 (2026-04-24): reward_component_ema_kernel.cu + RewardComponentMonitor added. 6 ISV reward-EMA slots [63..69) allocated; fingerprint shifted [61..63) → [69..71); ISV_TOTAL_DIM 63 → 71. experience_kernels.cu extended with reward_components_per_sample [N*L, 6] output parameter. 2 new Wired rows.

Plan 3 Task 3 B.2 (2026-04-24): trade_rate_ema_kernel.cu added. 2 new ISV slots [71] TRADE_ATTEMPT_RATE_EMA and [72] TRADE_TARGET_RATE; fingerprint shifted [69..71) → [73..75); ISV_TOTAL_DIM 71 → 75. experience_kernels.cu gains flat_to_pos_per_sample [N*L] output + 2 ISV slot-idx scalar parameters; a novelty-scaled bonus (conviction × vol_proxy × novelty) is added to reward and captured in rc[5] at every Flat→Positioned transition. TRADE_TARGET_RATE frozen in training_loop.rs at epoch 5 from measured attempt-EMA (min 0.001). 2 new Wired rows.

Plan 3 Task 5 C.4 (2026-04-24): Temporal timing bonus on trade exit. No new ISV slot — accumulates into rc[5] bonus slot via += (B.2 at entry, C.4 at exit: different (i,t) slots). New portfolio-state slot PS_PEAK_PNL_BAR=38; PS_STRIDE 38 → 39; PORTFOLIO_STRIDE 38 → 39 in all consumers (experience_kernels.cu, trade_stats_kernel.cu, gpu_experience_collector.rs, gpu_dqn_trainer.rs::launch_kelly_cap_update, ml-core::state_layout.rs). Peak bar snapshotted alongside every MAX_PNL update, reset at every MAX_PNL reset site (entry, reverse, fold hard-reset, trade-complete soft-reset). timing_bonus = shaping_scale × (bars_early / hold_time) × |final_pnl| × conviction_core where bars_early = max(0, segment_hold_time − PS_PEAK_PNL_BAR). No tuned multiplier. 1 new Wired row.

Plan 3 Task 6a D.4a (2026-04-24): Persistence credit on profitable drawdown recovery. No new ISV slot — accumulates into rc[5] bonus slot via += (now shared by B.2 entry, C.4 exit-timing, D.4a exit-persistence; all three fire at different (i,t) slots per trade, so += is idempotent). New portfolio-state slot PS_INTRA_TRADE_MIN_PNL=39; PS_STRIDE 39 → 40; PORTFOLIO_STRIDE 39 → 40 in all consumers (experience_kernels.cu, trade_stats_kernel.cu, gpu_experience_collector.rs, gpu_dqn_trainer.rs::launch_kelly_cap_update, ml-core::state_layout.rs). MIN_PNL tracked per-bar in the same block as MAX_PNL (fminf against pnl_pct), reset at every MAX_PNL reset site (entry, reverse, fold hard-reset, trade-complete soft-reset). persist_bonus = shaping_scale × conviction_core × drawdown_depth × tanh(reward / max(1e-4, drawdown_depth)) where drawdown_depth = max(0, −PS_INTRA_TRADE_MIN_PNL); gated on reward > 0 && drawdown_depth > 1e-6. Self-scaling tanh ratio: saturates +1 when reward ≫ drawdown (full credit for riding through), ≈0 when reward ≈ drawdown (trivial recovery). No tuned multiplier. 1 new Wired row.

Plan 3 Task 4 B.4 (2026-04-24): plan_threshold_update_kernel.cu + PlanThresholdMonitor added. PRODUCER-ONLY upgrade for ISV[PLAN_THRESHOLD_INDEX=49]: cold-start 0.5 constructor write preserved, but per-epoch GPU kernel now overwrites the slot with max(0.1, 0.5 × ISV[READINESS_EMA_INDEX=75]). New ISV slot [75] READINESS_EMA (cold-start 1.0 so the derived threshold matches the legacy 0.5 default until the EMA adapts). Fingerprint shifted [73..75) → [76..78); ISV_TOTAL_DIM 75 → 78. experience_kernels.cu gains readiness_per_sample [N*L] output parameter; the kernel writes the broadcast readiness_ptr[0] value into every reached (i,t) slot, race-free. isv_plan_threshold reset category flipped SchemaContract → FoldReset; isv_readiness_ema registered as FoldReset. Consumer kernels (4 sites in experience_kernels.cu + 1 in backtest_plan_kernel.cu) unchanged. 2 new Wired rows.

Plan 3 Task 6b D.4b (2026-04-24): Regime-shift penalty — punish trades held past a detected regime flip. No new ISV slot — subtracts from rc[5] bonus slot via −= (cancels against B.2/C.4/D.4a positive contributions within rc[5]; that cross-flow is exactly what ISV[68] REWARD_BONUS_EMA is meant to track). New portfolio-state slot PS_REGIME_SHIFT_BAR=40; PS_STRIDE 40 → 41; PORTFOLIO_STRIDE 40 → 41 in all consumers (experience_kernels.cu, trade_stats_kernel.cu, gpu_experience_collector.rs, gpu_dqn_trainer.rs::launch_kelly_cap_update, ml-core::state_layout.rs). New CUDA-side ISV macros ISV_Q_DIR_ABS_REF_IDX=21 and ISV_SHARPE_EMA_IDX=22 added to state_layout.cuh (mirror authoritative Q_DIR_ABS_REF_INDEX / SHARPE_EMA_INDEX in Rust trainer). Detector inside the active-trade block (after MIN/MAX_PNL update) writes PS_REGIME_SHIFT_BAR = hold_time on the FIRST bar where |isv[11] − PS_PLAN_ENTRY_REGIME| > clamp(0.25 × |clamp(sharpe, −2, 2)|, 0.05, 0.5); first-shift-only (short-circuits on non-zero PS_REGIME_SHIFT_BAR). Consumer in segment_complete block IMMEDIATELY AFTER D.4a: bars_late = max(0, saved_hold_time − PS_REGIME_SHIFT_BAR); penalty = shaping_scale × conviction_core × (bars_late / saved_hold_time) × ISV[21] × |reward|; reward −= penalty; rc[5] −= penalty; then defensively resets PS_REGIME_SHIFT_BAR = 0 (in addition to the 5 lifecycle resets at entry/reverse/fold hard-reset/trade-complete soft-reset). Adaptive threshold tightens when |sharpe| is high (confident trading warrants early detection); loosens under noisy training. Q_DIR_ABS_REF self-scaling matches B.1 opp-cost pattern. No tuned multiplier. 1 new Wired row.

Plan 1 Tasks 12/15/16 + pre-allocation (2026-04-24): No new modules added. Changes are ISV slot allocation + consumer migration only. Task 15 confirmed no-op (IQL_BRANCH_SCALE_FLOOR_INDEX already serves conviction-floor role). Tasks 12 and 16 migrate cql_alpha and plan-threshold consumers from config fields / hardcoded literals to ISV slots. 8 new ISV slots allocated ([39..47)); fingerprint tail moves from [37..39) to [47..49); ISV_TOTAL_DIM 39 → 49. GpuDqnTrainConfig gains total_epochs field (written to TOTAL_EPOCHS_INDEX at construction). write_isv_signal_at bound extended from ISV_DIM to ISV_TOTAL_DIM to allow writes beyond slot 22.

Plan 2 Task 6B D.3 (2026-04-24): IQL value head widened from 1 to 2 outputs (V_short + V_long). v_out_buf shape [B] → [B*2]. gemm_fwd_v M=1→2, gemm_bwd_dw3 M=1→2, gemm_bwd_dh2 K=1→2. W3 param block [H*1] → [H*2], b3 [1] → [2]. total_params += H+1. iql_expectile_loss kernel extended with num_heads argument. 4 consumer kernels in iql_value_kernel.cu updated to read v_out[b*2+0] + v_out[b*2+1]. Checkpoint compat break — retrain required.

Classification	Count
Wired	84
Partial	10
Orphan (held for follow-up)	3
Ghost	0
OUT-of-DQN-scope	17
Total	112

The 3 remaining Orphan rows are:

• cuda_pipeline/gpu_statistics.rs + statistics_kernel.cu — held for Plan 2 D.2 wire-or-delete decision.
• data_loaders/tlob_loader.rs — held for Plan 2 D.8 TLOB trunk wiring.
• regime_detection/mod.rs — crate-level cleanup task (whole workspace crate, exceeds Task 6 scope).

Orphan Resolution Index

Orphan	Resolution	Status
cuda_pipeline/gpu_statistics.rs + statistics_kernel.cu	Wire into val path or delete if superseded by gpu_monitoring.rs	Plan 2 D.2 — held
data_loaders/streaming_dbn_loader.rs	Reclassified Partial: test consumer at tests/streaming_pipeline_edge_cases.rs	RECLASSIFIED
data_loaders/tlob_loader.rs	Plan 2 D.8 wires TLOB trunk — this loader may be needed then	Plan 2 D.8 — held
training/unified_data_loader.rs	Deleted (zero consumers)	DELETED
training/orchestrator.rs	Deleted (zero consumers)	DELETED
training_pipeline.rs	Reclassified Partial: ProductionTrainingError used via From impl in lib.rs	RECLASSIFIED
inference_validator.rs	Deleted (zero consumers)	DELETED
model_loader_integration.rs	Deleted (zero consumers)	DELETED
paper_trading/mod.rs	Reclassified Partial: test consumer at tests/paper_trading_integration_test.rs	RECLASSIFIED
portfolio_transformer.rs	Deleted (zero consumers)	DELETED
regime_detection/mod.rs	Crate-level follow-up scheduled (separate crate-cleanup task)	CRATE-LEVEL-FOLLOWUP