10e647c141
C.8 (ISV-driven aux trunk Adam β1/β2/ε/LR/grad-clip) was already complete in C.5a
commit c90de9859 — all 5 ISV reads and fold-boundary StateResetRegistry defaults were
wired atomically with the Adam launcher. No new code required; noted in audit doc.
C.9 adds `aux_trunk_learns_synthetic_uptrend` to aux_trunk_oracle_tests.rs:
- B=16, ENC=32, H1=32, H2=16, SH2=32, H_HEAD=32, K=2, 100 steps
- Backward kernel invocations corrected to match actual signatures:
- aux_trunk_bwd_dh_pre(d_logits, w3, w2, h_aux1, h_aux2, dh_pre2, dh_pre1, B, H1, H2, SH2)
shmem = H2 floats (sh_dh2_pre cache), NOT (H1+H2+SH2)
- aux_trunk_bwd_dW_reduce called 3×: dW3/dW2/dW1 each with (A, B_grad, dW_out, B, Krows, Jcols)
- aux_trunk_bwd_db_reduce called 3×: db3/db2/db1 each with (B_grad, db_out, B, Jcols)
- Head params trained via host-side SGD (test orchestration only; reads mapped-pinned partials)
- Trunk params trained via dqn_adam_update_kernel (GPU Adam)
- Pass gate: CE loss < 0.1 AND dir_acc > 0.95 after 100 steps
Near-random baseline (ln(2)≈0.693) = broken gradient chain, L40S dispatch blocked
Memory pearl pearl_separate_aux_trunk_when_shared_starves.md added and indexed.
Co-Authored-By: Claude Sonnet 4.6 <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;