ef08611d3f
Phase 3.5.4 (e0e0abfb2) landed the trigger + warm-up tracker but
deferred the actual weight reset (kernel accepted advantage_head_weights
+ n_weights but no-op'd via (void) cast). Wave 4.2 lands the real
reset.
When DD_PERSISTENCE exceeds threshold AND not yet fired this fold,
the kernel now resets the last 10% of advantage-head weights to
Kaiming-He init: Normal(0, sqrt(2/fan_in)) sampled via cuRAND
curand_normal() with per-thread state initialized from a host-passed
seed (Option A: per-call curand_init(seed, tid, 0, &state) for full
determinism — bit-identical samples for identical (seed, tid) pairs).
Single kernel, two phases:
1. Every thread independently re-evaluates fire_now from the same
ISV reads (DD_PERSISTENCE / threshold / fired_flag); the trigger
condition is a pure function of these reads so all threads
converge without cross-block synchronisation. Block 0 / thread 0
also runs the trigger + warm-bars decrement (single-thread ISV
write path).
2. If fire_now: each tid < reset_count writes Kaiming-He sample to
advantage_head_weights[reset_start + tid]. Per-thread independent
write, no atomic, no reduction (feedback_no_atomicadd clean).
New launcher params: fan_in (i32), seed (u64). Grid:
((n_weights/10 + 255) / 256).max(1) x [256, 1, 1]. The .max(1) floor
ensures block 0 always exists even when n_weights/10 == 0.
cuRAND device functions (curand_init, curand_normal) are inlined into
the cubin by nvcc from <curand_kernel.h> in the standard CUDA toolkit
include path — no host-side cuRAND linker dependency required, no
build.rs link change needed.
Oracle test plasticity_injection_kernel_resets_last_10pct_kaiming_he
verifies: (a) ISV[fired] flipped 0->1, (b) ISV[warm] = m_warm - 1,
(c) first 90% bit-identical to 1.0, (d) last 10% all moved off 1.0,
(e) sample mean |mean| < 0.05, (f) sample std within +-20% of
sqrt(2/fan_in), (g) determinism re-check produces bit-identical
samples for identical seed.
3 existing trigger tests migrated to the new launcher signature; the
debounced + no-fire variants additionally assert weight-stability
(early-out path skips the reset region).
Atomic per feedback_no_partial_refactor: kernel + launcher + 4 tests
+ audit doc + build.rs comment + 2 docstrings land together.
Eliminates Phase 3.5.4 deferred consumer per feedback_wire_everything_up
(the (void) casts are gone).
Action-selection consumer wiring (Phase 3.5.4.c) remains the separate
follow-up per the established Phase 3.5.X pattern.
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;