Restore 45-action factored space via Branching DQN (Tavakoli 2018),
outputting 11 Q-values (5+3+3) instead of 45. This was reduced to 5
exposure-only actions during debugging and was never intended as permanent.
- Enable use_branching: true by default in DQNConfig and DQNHyperparameters
- Add branching paths to select_action_with_confidence and select_action_inference
- Update agent.rs select_action_factored for branching-aware selection
- Expand CountBonus to per-branch tracking with bonuses_branched()
- Add order_type + urgency distribution tracking in monitoring
- Add DQN_ORDER_ACTIONS=3, DQN_URGENCY_ACTIONS=3, DQN_TOTAL_ACTIONS=45 to CUDA header
- Fix 7 pre-existing clippy doc_markdown errors in regime_conditional.rs
- Fix pre-existing cognitive_complexity in replay_buffer_type.rs (extract helpers)
- Fix flaky GPU test OOM under parallel execution (CPU fallback + test VRAM safety)
- Delete unused flash_attention submodules (block_sparse, causal_masking, etc.)
- Add GPU hot-path guard scripts and ensemble/hyperopt adapter improvements
Tests: ml-dqn 416/0, ml 905/0, clippy 0 errors on both crates
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add backticks to type names in doc comments (doc_markdown)
- Mark eligible functions as const fn (missing_const_for_fn)
No behavior changes.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
module_name_repetitions: PpoConfig in ppo module is conventional ML naming.
Renaming breaks every import across the workspace.
integer_division: Basis point calculations, batch size math, combinatorial
formulas — truncation is intentional. Float conversion would introduce bugs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Remove unused struct fields, dead methods, unreachable code across
ml-dqn, ml-supervised, ml-features, ml-ppo, ml-checkpoint, ml-labeling,
ml-observability, and ml-universe. Gate test-only infra behind #[cfg(test)].
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Move UnifiedTrainable trait, TrainingMetrics, CheckpointMetadata, and
checkpoint helpers from ml to ml-core (zero new dependencies — ml-core
already had candle-core + serde_json)
- Wrap Mamba2SSM in Mamba2TrainableAdapter to satisfy orphan rule (trait
in ml-core, type in ml-supervised — all other 9 models already used
wrapper pattern)
- Make Mamba2SSM::validate() pub for cross-crate adapter access
- Delete 5 permanently disabled deployment modules (cfg(any()) — never
compiled): registry, hot_swap, validation, monitoring, endpoints
(-5,842 lines)
- Delete 2 undeclared dead files in training/: dqn_trainer.rs,
transformer_trainer.rs (-138 lines)
- Fix pre-existing compute_loss test shape mismatch in mamba adapter
UnifiedTrainable in ml-core unblocks future trainers/ extraction (17.8K
lines) since model-specific trainers can now depend on ml-core for the
trait without pulling in the full ml monolith.
14 files changed, +128 -6,497 (net -6,369 lines)
Tests: 274 ml-core + 948 ml = 1,222 passed, 0 failed
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Move TFT, Mamba-2, Liquid, TGGN, TLOB, KAN, xLSTM, and Diffusion model
implementations to ml-supervised. Bridge files (UnifiedTrainable adapters,
Checkpointable impls) stay in ml. Delete AsyncDataLoader (replaced by
StreamingDbnLoader + simple .chunks() batching). Remove empty ml-infra
scaffold — the remaining ml modules are too tightly coupled for clean
extraction, so ml stays as the orchestration facade.
- ml-supervised: 234 tests, 0 failures
- ml: 1687 tests, 0 failures
- Workspace: 0 compilation errors
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add 5 empty sub-crates (ml-core, ml-dqn, ml-ppo, ml-supervised, ml-infra)
to workspace. Modules will be moved from monolithic ml crate in subsequent
tasks.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
