2b91f587b7
Adds FoldReset registry coverage for all 22 SP18 ISV slots [483..505)
landed in PP.2 (10 D-leg + 12 B-leg), in lockstep with matching match
arms in `reset_named_state` per the existing
`every_fold_and_soft_reset_entry_has_dispatch_arm` contract test (which
catches the "add registry entry, forget dispatch arm → runtime panic
at fold boundary" pattern surfaced twice historically — SP5 #281, SP7
T7 commit 6e479c55c).
Sentinels match `sp14_isv_slots.rs` constants (single source of truth):
D-leg POS/NEG caps → 5.0 / -10.0 (SP14 P0-A REWARD_*_CAP_ADAPTIVE pattern)
D-leg HRC Welford → 0.0 (SENTINEL_WELFORD_ZERO)
D-leg DIAG/FIRE → 0.0 (Pearl-A direct-replace)
B-leg HEALTH_DIAG → 0.0 (Pearl-A)
B-leg POPART_RESET → 1.0 (one-shot per B-DD11 — see below)
B-leg TDB Welford → 0.0 (SENTINEL_WELFORD_ZERO)
B-leg RESERVED → 0.0
Slot 497 POPART_RESET_FLAG semantics: each fold start, FoldReset writes
1.0; on the first epoch of that fold the Phase 5 PopArt-reset consumer
reads 1.0, resets PopArt slot 63 EMA to identity normalization, and
writes 0.0 back — one-shot per fold. Cheap insurance against the 1+
epoch PopArt adaptation lag when switching `q_next` source from
rewards-distributed to Q-distributed in the Phase 5 atomic refactor.
New lock test `sp18_fold_reset_entries_present` asserts all 22 entries
exist with FoldReset category and a description containing "SP18 D-leg"
or "SP18 B-leg" prefix marker. Existing
`every_fold_and_soft_reset_entry_has_dispatch_arm` catches drift in
either direction (registry adds without dispatch arms, or dispatch arms
without registry entries).
Audit doc updated per Invariant 7 with PP.3 entry + full sentinel
mapping table.
Plan: docs/superpowers/plans/2026-05-08-sp18-reward-shape-hold-attractor.md
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;