Supersedes the 2026-05-19 deployability spec (commit 07d5de504). The
prior spec assumed CfcTrunk::save_checkpoint was the producer-side
wiring point — discovered at execution time that alpha_train trains via
PerceptionTrainer (full v2: VSN + Mamba2 ×2 + LN ×2 + attn-pool + CfC +
heads), not the simpler CfcTrunk. The existing LOB backtester loads
CheckpointV1 envelopes that only know about CfC weights, so there is no
producer for a checkpoint containing the full v2 model.
New scope: one bigger spec covering refactor + deployability end-to-end.
Phase 1 (X0–X19, code commits): grow CfcTrunk to own the full v2
inference graph; restructure PerceptionTrainer to wrap a trunk + add
training-only state (grads, AdamW). Discipline: bit-equivalence golden
fixture (X0) gates every refactor commit (X1–X11). CheckpointV2
envelope (X12) replaces V1. Verdict emitter (X17) reuses the tiered
classification (Pass-robust / Pass-nominal / Fail-inconclusive / Fail /
Fail-degenerate) from the superseded spec.
Phase 2 (Argo runtime): production training → smoke gate → threshold
pre-registration → 560-cell deployability sweep → verdict + memory
update.
Hard gate before Phase 2: post-refactor fold-0 smoke must reproduce
recorded 3-fold A/B numbers (best_mean_auc 0.7529, best_h6000 0.7639,
both within ±0.010 absolute) from project_ml_alpha_v2_ab_verdict
memory. Prior spec marked SUPERSEDED in its header, kept in history as
audit trail.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
User revisions to design spec from this session:
1. §2.1 — split anchor into realistic (200 ms, 1 tick) and stress (400 ms,
1.5 tick). Realistic remains the hard verdict gate; stress grades the Pass
into Pass-robust vs Pass-nominal.
2. §2.2 — expand metrics from Sharpe-only to four: annualized daily Sharpe
and max-drawdown are hard gates (median across windows > 1.0 and < 20%
respectively); Sortino and profit factor are diagnostics. Per-window
summary.json schema extended.
3. §2.6 — verdict emitter rewrites to two-anchor logic, tiered output:
Pass-robust / Pass-nominal / Fail-inconclusive / Fail / Fail-degenerate.
4. §3.3 — added max-dd computation and zero-trade-window failure modes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Closes the wiring gap between the existing real-LOB backtest system (C1–C19 on
this branch) and the v2 ml-alpha model. alpha_train.rs currently never calls
save_checkpoint, so the LOB harness/sweep/aggregate machinery has never been
pointed at a real trained model.
Design defines a single-pass falsifiable deployability gate: produce a
production checkpoint (cv-n-folds=1, cv-train-window=4 → train on 2024
quarters, val on 2025-Q1, hold out 2025-Q2..2026-Q1), pre-register one
threshold on the W0 val window, then evaluate median Sharpe across 4
held-out walk-forward quarters at the realistic Scaleway→IBKR anchor (200ms
RTT, 1-tick all-in cost). Pass iff median > 1.0; inconclusive in [0.8, 1.0]
counts as fail; smoke gate halts the full sweep on any wiring failure.
Approved through brainstorming. Awaiting user spec-review before plan
handoff.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The motivating "50% saturation hit-rate" observation came from gm67g
fold 0 (Option-2 config, commit 004b662c8 — itself a -0.003 mean_auc
regression vs ISV-σ at 410ab6b0e). Re-running the same diagnostic on
the actual production baseline (ISV-σ 3 folds: rxm5t/r57lx/x24d6,
logs retrieved from MinIO argo-logs bucket) on 215 horizon-epoch
observations:
saturated λ→AUC up: 8/13 = 61.5% median Δauc = +0.0025
non-saturated→AUC up: 96/202 = 47.5% median Δauc = -0.0012
difference: +14pp in favor of the controller working
The BCE-z-score controller is empirically correlated with the
optimization target. No evidence it's misaligned with AUC. Spec
premise falsified.
Spec retained in tree as historical record. The diagnostic
methodology itself (saturation→Δauc analysis on archived MinIO logs)
is the durable artifact and is documented in the supersede block.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The current λ controller boosts horizons by BCE-EMA z-score, which conflates
three distinct causes of high BCE — only one of which (under-trained
horizon) benefits from boosting. The other two (intrinsically harder
horizon, calibration drift) are unaffected by gradient-magnitude lifts.
Empirical motivation (gm67g fold-0, this branch's 3-fold run):
when λ_h6000 saturated at 2.0, h6000's next-epoch AUC went UP 2/4
times and DOWN 2/4 times. 50% hit rate ⇒ the BCE saturation signal
is misaligned with the optimization objective.
This spec replaces BCE-z-score with AUC-regret:
best_auc[h] = running max of per-horizon validation AUC
regret[h] = max(0, best_auc[h] - current_auc[h])
regret_max_ema = EMA of max_h regret[h]
λ[h] = clamp(1.0, 2.0, 1 + regret[h] / regret_max_ema)
Properties:
- Aligned with the objective (AUC, not BCE)
- Naturally bounded (regret ∈ [0, 1])
- "At personal best" → λ=1.0 (no wasted boost)
- Auto-saturation by design (max-regret horizon → ceiling)
- Cold-start clean (e0: best=current, regret=0, uniform λ)
- Zero hardcoded magic beyond bootstrap epsilons
Implementation surface ~250 LOC:
- Split horizon_lambda kernel: horizon_loss_ema (per-step) +
horizon_lambda (per-epoch, AUC-regret math)
- Trainer state: drop z_max_ema, add best_auc + regret_max_ema
- Per-epoch entry point: trainer.update_lambda_from_auc()
- Extend isv snapshot log line with best_auc_h* + regret_h*
Test plan:
- Local 9/9 perception_overfit
- New synthetic-AUC unit test (controller correctness)
- Cluster 5-epoch smoke + 3-fold A/B vs Option-2 baseline (004b662c8)
- Success: mean_auc lifts ≥ +0.005 AND median sat→next-AUC delta positive
Awaiting user review before plan handoff.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Integrated design spec for the post-A/B redesign: Kendall sigma BCE
(A), L2 anchor on horizon tokens + shared Q (B), horizon-token
K-prepend replacing per-horizon Q_h (C), regime-aware MoE gate (D),
and inverted-axis attention pass (E). Bundled per
pearl_no_deferrals_for_complementary_fixes — all five axes have
orthogonal architectural scope and independent kill criteria.
Spec drops the C21-C25 per-horizon Q_h path (falsified by sweep
2026-05-18: mean_auc -0.019 vs single-Q baseline) and migrates the
existing init buffers into the new horizon-tokens prefix.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Captures the brainstormed "alternative attention pool variants"
follow-on from the original real-LOB integration brainstorm (Axis 1,
deferred from the LOB workstream as a separate model-side spec).
Design:
Replace shared learned query Q[HIDDEN_DIM] with per-horizon queries
Q_h[N_HORIZONS, HIDDEN_DIM]. Per-horizon softmax + context vectors
feed multi-horizon heads directly (PATH A) — each horizon attends
to a different part of the K=6000 LN_b output sequence. CfC k=0
state is initialised by the MEAN of per-horizon contexts so the
K-loop recurrence + state amplification (per
pearl_state_amplifies_short_horizon_into_long_horizon) survives.
Heads consume per-horizon context concat CfC h_K (residual) with a
default 75/25 weight split.
Falsifiable claim (§0): A/B-tested win means h6000 mean_auc lifts by
≥ +0.01 absolute OR per-horizon distribution shifts toward short
horizons (h1000, h300) with no net h6000 loss. The 3-fold variance
band on the current architecture (mean_auc 0.7749 ± 0.024) means a
+0.01 lift is within noise — a meaningful effect needs ≥ +0.024 or
qualitative distributional shift.
Two new kernels (per_horizon_attention_pool_fwd + _bwd) + signature
extension on multi_horizon_heads_{fwd,bwd}. Variant-toggle config flag
(SharedQuery vs PerHorizonQuery) keeps the existing path fully
functional; new variant is opt-in. CheckpointV1 → V2 with explicit
discriminant + optional q_h field; V1 files load as SharedQuery, new
V2 training writes the discriminant.
Three validation rings:
1. Per-(b,h) numgrad parity at K=16
2. One-epoch smoke (no NaN, loss decreases)
3. 30-epoch × 3-fold A/B (#204) — decision gate per §0 falsifiable claim
Implementation explicitly deferred. The decision to invest depends on
(a) GPU time budget (~3-6 hrs on L40S × 5 GPUs for the A/B), (b)
whether per-horizon cost-frontier sweeps (#202 follow-ups) surface
viable horizons beyond h6000 that would benefit from per-horizon
specialisation, and (c) the 3-fold variance noise floor making the
expected effect size visible.
Next step when ready: invoke superpowers:writing-plans against this
spec for the ~6-8 commit implementation plan.
Closes the "good to have" question from the recent brainstorm with a
concrete decision framework rather than ad-hoc implementation.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Revises all 12 issues from the self-review pass:
1. (HIGH) Soften bit-equivalence claim — single-sample helper and
batched kernel at B=1 are different CUDA kernels; FP order may
differ. Acceptance is relative_eq ≤ 1e-6, not bit-exact.
2. (HIGH) Explicit asymmetry: only cfc_step has a single-sample GPU
oracle. GRN/VSN/attn bwd rely on smoke + chain-rule preservation.
feedback_no_cpu_test_fallbacks.md forbids a CPU reference oracle.
3. (HIGH) Realistic targets — 3× floor, 5× stretch. Drops 15× claim
which was Amdahl-bounded under any reasonable assumption.
4. (MED) AdamW-after-reducer invariant stated explicitly: final grad
buffer is OVERWRITE by reducer, meaningful only after reducer ran.
5. (MED) New B=32 smoke test (stacked_trainer_loss_shrinks_at_batch_32)
actually exercises the cross-batch reduction path; existing
perception_overfit suite is all B=1.
6. (MED) Rollout commit 1 bundles reduce_axis0 + first consumer
(cfc_step refactor) to avoid feedback_wire_everything_up.md
orphan-kernel anti-pattern.
7. (LOW) Drop "merge to ml-alpha-phase-a" — user already chose direct
commits to that branch; clarify in Rollout.
8. (LOW) Add explicit scratch-sizing formula:
scratch_bytes ≈ B × Σ(param tensor sizes per kernel).
9. (LOW) Remove resolved open question (memset_zeros ordering).
10. (STRUCT) Post-refactor bottleneck analysis section — names the
next L40S floor (Mamba2 scan, launch latency, cuBLAS).
11. (STRUCT) cudaFuncSetAttribute note — refactored cfc_bwd's per-
block shared mem drops to 1 KiB; no attribute change needed.
12. (STRUCT) Explicit Rollback section — atomic-commit-per-kernel +
revert strategy; rollback baseline is the spec commit
(54aa69c10) on ml-alpha-phase-a.
Plus locks the target pool to L40S — speedup must be attributable to
the refactor, not to a hardware bump. H100 / BF16 / larger batch
become candidates for a follow-up spec once the L40S floor is known.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Documents the design for fixing the single-SM bottleneck in five
backward/K-loop kernels: cfc_step (fwd+bwd), GRN bwd, VSN bwd, and
attention_pool bwd. All currently use grid=(1,1,1) with an internal
n_batch loop — on L40S (142 SMs) with B=32 this puts <1% of the GPU
to work in the K-loop critical path.
Architecture: block-per-batch (grid=(B,1,1)) for the kernel body, plus
per-batch grad scratch buffers reduced via a single parameterised
reduce_axis0 kernel (block tree-reduce, no atomicAdd per
feedback_no_atomicadd.md). Same pattern as the existing LayerNorm bwd
reducer — CUDA-Graph-safe, debuggable, and consistent with foxhunt's
no-cooperative-groups discipline.
Target: ≥3× epoch wall speedup (stretch 8-15×). Makes 3-fold CV
tractable (10.5h → 2-3h) and unblocks decision-stride / state-dim
sweeps that compound the gain.
Acceptance gates: (a) all 8 perception_overfit smokes still converge,
(b) new B=1 bit-equivalence test asserts the refactored batched bwd
kernel at B=1 matches the existing single-sample helper byte-for-byte,
(c) cluster A/B vs t6z89 baseline shows AUC trajectory within ±0.005
and epoch wall ≥3× faster.
Atomic refactor per kernel — one commit per kernel covering the
kernel rewrite, scratch buffer alloc, reducer launch wiring, and
smoke. No "_legacy" parallel kernels per feedback_no_legacy_aliases.md
+ feedback_no_partial_refactor.md.
Open implementation-plan decisions: exact memset_zeros ordering inside
the captured graph, batch-vs-per-tensor reducer launches, optimal
block_dim for reduce_axis0 itself.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per user direction "no gating, this is the new default": the stacked
Mamba2 -> CfC -> heads design is THE production architecture. There's
no competing-baseline comparison to run. Validation reduces to normal
training metrics (per-horizon val AUC, train loss curve, sanity floor
of >0.5 AUC).
Deletions:
- crates/ml-alpha/src/gate/cfc_vs_mamba2.rs (gate verdict logic)
- crates/ml-alpha/src/gate/mod.rs
- crates/ml-alpha/examples/alpha_gate.rs (gate runner binary)
Renames:
- crates/ml-alpha/src/gate/auc.rs -> crates/ml-alpha/src/eval/auc.rs
- lib.rs: pub mod gate -> pub mod eval (gate implied comparison;
eval doesn't)
Spec amendments:
- Drop the "Gate baseline strategy" amendment (committed earlier
this session)
- Reframe the stacked-architecture amendment as a "decision" not a
"gate"; production path is unambiguous
- Reframe Section 4 "Validation gate: CfC must meet Mamba2" -> just
"Validation: per-horizon val AUC" with the >0.5 sanity floor
Doc cleanups: stale "Mamba2 gate baseline" mentions in build.rs and
pinned_mem.rs replaced with neutral wording. The Argo template
comment about "downstream gate consumption" becomes "for monitoring".
Test status: all 26+ ml-alpha tests pass. AUC tests (6/6) still pass
under the eval:: namespace.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Defines the Mamba2-only baseline for the stacked-vs-baseline gate
verdict as an ablation of the SAME PerceptionTrainer (a --bypass-cfc
flag), not a separate model. Apples-to-apples; same data window,
same hyperparameters, same code path. The only difference is whether
the CfC step is in the loop.
Three ablation options evaluated:
1. --bypass-cfc flag (recommended): Mamba2 -> heads directly
2. --mamba2-state-dim 2 (crippled Mamba2, CfC stays)
3. Frozen CfC initialized to identity (no code branch needed)
Option 1 wins on clarity: it answers "is CfC additive on top of
Mamba2" unambiguously, with the same Mamba2 capacity and same
training regime in both arms.
Concrete next-session work documented (1-2 hours):
- PerceptionTrainerConfig.bypass_cfc: bool + step() branch
- alpha_train --bypass-cfc CLI flag
- alpha-perception-template.yaml workflow parameter + bash branch
- submit both runs, fetch summaries, alpha_gate, commit verdict
gate_verdict logic unchanged — the cfc/mamba2 naming in the report
becomes stacked/bypass at the binding layer; the verdict math is
generic.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Mid-execution architecture revision: Mamba2 stays as a sequence
encoder; CfC becomes the layer on top (replacing the Phase 1d.3 MLP
stacker). Gate becomes 'stacked AUC >= Mamba2-only stacker AUC at
every horizon' — proves the CfC layer is additive, rather than CfC
alone beating Mamba2 alone.
Plan 1 kernels (cfc_step, heads, projection, BCE, AdamW, Graph A)
are unchanged. Only CfcTrunk's forward path gains a Mamba2 prefix
that consumes the snapshot stream and emits a 128-dim h_mamba which
CfC reads. The Mamba2 kernel (mamba2_alpha_kernel.cubin) is already
in the build.
Option B (parallel + fused dual-stream) is documented as the Plan 2
fallback if the stacked gate fails.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two documentation deliverables produced while T14 backtest runs:
1. Plan update (specs/2026-05-15-phase-e-4-a-temporal-foundation.md):
adds 'Execution Status' section reflecting actual T1-T14
progression. T5 deferred (real MBP-10 peek), T9 skipped (GRN
moved to E.4.B per integration notes), T10 partial (new C51
grad-input kernel landed but Mamba2 backward wiring deferred),
T14 in flight. Documents the 4 execution learnings:
research-first saved a week of duplicate kernel work; cheap
falsification experiments (Path 2, Path 3) avoided expensive
investments; C51 borrow was the largest single Sharpe-lift in
the session; GpuTensor/CudaSlice interop friction is the real
integration cost.
2. T10 patch sketch (specs/2026-05-15-t10-mamba2-backward-from-h-enriched.md):
ready-to-apply patch for ml-alpha::Mamba2Block adding a new
public method backward_from_h_enriched(cache, d_h_enriched).
Bypasses the W_out projection backward, accepts the
[B, hidden_dim] gradient from C51's grad-input kernel directly,
zero-initialises dw_out/db_out (AdamW step on zero grad is a
no-op with correct moment decay — effectively freezes W_out
params which is correct semantics since Phase E never uses
them). Includes the smoke binary wiring snippet that consumes
the new method via launch_alpha_c51_grad_input → Mamba2
backward → AdamW step. Application gated on T14 backtest
validation — if frozen Mamba2 already lifts Sharpe, T10
becomes optimisation rather than prerequisite.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Design doc (specs/): TFT-style architecture for Phase E execution
policy — sliding window → Mamba2 SSM → GRN trunk → MoE regime gate
→ C51 head → Thompson selector. Two core pillars added per user:
A) Full L1-L10 LOB depth input via hybrid MBP-10 peek
B) ISV-continual-learning: controllers fire at training AND
inference; Q-net weights frozen at inference but effective
policy adapts via ISV modulation
Plan doc (plans/): 14-task implementation plan for E.4.A foundation
(window buffer + L1-L10 depth + Mamba2 forward+backward + ISV-eval
controllers). Falsification gates: smoke R_mean improvement ≥ 50%,
backtest cost=0 Sharpe ≥ +8 (no regression vs C51-flat +10.41),
half-tick Sharpe ≥ -8 (closes 5pt+ of 10pt gap to Phase 1d.4
baseline -4.0).
TGGN (foxhunt Temporal Graph Gated Network) explicitly deferred to
Phase E.5+: existing CPU graph implementation + GPU adapter at
ml-supervised/src/tgnn/ — marginal benefit for single-instrument ES
futures vs the TFT-Mamba2 stack; revisit for multi-instrument
extension or production HFT inference layer.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Findings:
- Production Mamba2 (gpu_dqn_trainer) is coupled to SH2=256 trunk +
ofi_embed + ISV[8] temporal routing — not portable to Phase E.
- ml-alpha::mamba2_block::Mamba2Block is from-scratch, fully
configurable (in_dim/hidden_dim/state_dim/seq_len), GPU-pure with
forward_train/backward/AdamW. Used in Phase 1d.2 to lift AUC 0.50
to 0.66. ml-alpha is already a workspace dep of ml.
- GRN skipped for E.4.A — Mamba2 output goes straight to C51 head.
Reintroduce GRN in E.4.B if Sharpe gates don't pass.
- Controller-at-inference: kernel has no training-mode branches;
Wiener state preserved across episodes/cost cells for natural
live-deployment simulation.
Revises Tasks 8-10 of the plan: use Mamba2Block API instead of
writing custom kernels. Only new CUDA needed: alpha_c51_grad_input
(C51 gradient w.r.t. input features, for Mamba2 backward chain).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Atomic Phase 1.4 commit per `feedback_no_partial_refactor`. Wires the
SP20 fused-producer chain (Stats → Aggregate → EMAs → Controllers) into
GpuExperienceCollector's per-rollout-step path with one new aggregation
kernel + Phase 1.2 EMA kernel-signature refactor + production wire-up
+ tests + audit/spec/plan amendments, all in one commit.
## Path C decision rationale
The Phase 1.2 EMA kernel originally took 9 scalar value-args. The Phase
1.4 wire-up site (per-rollout-step in GpuExperienceCollector) needs to
feed per-env GPU-resident trade signals (trade_close_per_sample,
step_ret_per_sample, hold_at_exit_per_sample, packed factored
actions_out) into the kernel — forbidden via host sync
(`feedback_cpu_is_read_only`) and via memcpy_dtoh/htod
(`feedback_no_htod_htoh_only_mapped_pinned`). Path C refactors the EMA
kernel to take a device struct pointer (`SP20EmaInputs* ema_inputs`) +
adds an aggregation kernel that writes the struct on the GPU. Phase 1.2
had zero production consumers yet, so the sig change + Phase 1.4 wire-up
land atomically.
## What this commit contains
1. **EMA kernel signature refactor** (Phase 1.2 → Path C):
`sp20_emas_compute_kernel.cu` swaps 9 scalar args for a single
`const SP20EmaInputs* __restrict__ ema_inputs` device pointer.
Math semantics bit-identical. Rust `EmaInputs` mirrored as
`#[repr(C)]` byte-for-byte; new `pack_inputs_into_f32_view` helper
for tests + collectors that fill the struct via a mapped-pinned
f32-aliased buffer.
2. **New aggregation kernel** (`sp20_aggregate_inputs_kernel.cu`):
Per-env arrays (sliced to current rollout step) + sp20_stats outputs
(p50/std) + aux_dir_acc_reduce_kernel output [0] → SP20EmaInputs
struct. Block tree-reduce (4 stripes × bdim × 4 bytes shmem); no
atomicAdd. Aggregation rules per spec §4.5:
- is_close = OR over envs
- is_win = (≥ 0.5 of closed envs were wins)
- trade_duration = round(mean(hold_at_exit) over closed envs)
- action_is_hold = strict majority (count*2 > n_envs) HOLD
- alpha = 0.0 (Phase 2 forward ref — reward kernel)
- per_bar_hold_reward = 0.0 (Phase 3.2 forward ref — Hold-cost dual)
- aux_logits_p50/std/dir_acc forwarded from upstream
3. **Production wire-up in GpuExperienceCollector**:
4 kernel handles + 4 mapped-pinned buffers (struct fields, alloc,
init); per-rollout-step launch sequence after env_step (step 5c):
`Stats → Aggregate → EMAs → Controllers`. All on the same stream;
stream-implicit producer→consumer ordering. Gated on
`isv_signals_dev_ptr != 0 && trainer_params_ptr != 0` (matches the
existing SP14-β EGF chain pattern).
4. **Fold-boundary reset**:
3 new RegistryEntry records (sp20_ema_inputs_buf,
sp20_emas_internal_buf, sp20_emas_obs_count_buf) + 13 new dispatch
arms in training_loop.rs (10 SP20 ISV slot resets + 3 buffer resets).
Closes the pre-existing `every_fold_and_soft_reset_entry_has_dispatch_arm`
regression (was failing on fresh check after the SP20 ISV slot
registrations landed in commit 4249ebc96).
5. **HEALTH_DIAG emit**:
Per-epoch `HEALTH_DIAG[N]: sp20_isv [loss_cap=… alpha_ema=…
wr_ema=… hold_cost_scale=… target_hold_pct=… hold_pct_ema=…
hold_reward_ema=… n_step=… aux_conf_threshold=… aux_gate_temp=…]`
right after the existing q_disagreement_diag emit.
6. **Tests** (5 test files, all green on RTX 3050 Ti sm_86):
- sp20_emas_compute_test.rs (4 GPU oracle): updated to use the
post-Path-C buffer-arg API (mapped-pinned f32-aliased struct).
- sp20_aggregate_inputs_test.rs (NEW, 6 GPU oracle): aggregation
rule coverage including the Phase 2 / Phase 3.2 placeholder
contract.
- sp20_phase1_4_wireup_test.rs (NEW, 2 GPU oracle): end-to-end
4-kernel chain integration test.
- sp20_stats_compute_test.rs (4 GPU oracle): unchanged, regression.
- sp20_controllers_compute_test.rs (7 GPU oracle): unchanged,
regression.
- 18 lib unit tests across the SP20 launchers.
7. **Phase 2 / Phase 3.2 forward references**:
The `alpha` (Phase 2) and `per_bar_hold_reward` (Phase 3.2) fields
are emitted as 0.0 placeholders and documented at:
- `sp20_aggregate_inputs_kernel.cu:46-52` (in-kernel docstring)
- `sp20_aggregate_inputs.rs:46-49` (launcher docstring)
- `dqn-wire-up-audit.md` "Phase 2 / Phase 3.2 forward references"
These are NOT stubs — Phase 2 / 3.2 will replace the kernel's 0.0
writes with real signals atomically with their respective producers.
The original Task 2.3 (host-side aggregation) is **subsumed** by
the GPU-side aggregation kernel.
## Hard rules
- `feedback_no_partial_refactor` — kernel sig change + aggregation
kernel + production wire-up + tests + audit/spec/plan amendments
in one atomic commit
- `feedback_no_atomicadd` — aggregation kernel uses block tree-reduce
- `feedback_no_cpu_compute_strict` — every aggregation lives on GPU
- `feedback_no_htod_htoh_only_mapped_pinned` — every Phase 1.4 buffer
is mapped-pinned with `cuMemHostAlloc(DEVICEMAP)` reachable via
`dev_ptr`; no memcpy_dtoh/htod
- `pearl_first_observation_bootstrap` — counter-based bootstrap
preserved; placeholder writes (0.0) keep the ALPHA / HOLD_REWARD
EMAs at 0.0 sentinel until Phase 2 / 3.2 wire real producers
- `pearl_no_host_branches_in_captured_graph` — every kernel is single-
block; no host branches; safe to capture in the per-step CUDA Graph
- `pearl_tests_must_prove_not_lock_observations` — integration test
asserts invariants (slots populate, bounds respected) rather than
locked observed values
## Verification
- `cargo check -p ml --features cuda` clean
- 18 lib unit tests for SP20 launchers pass
- 23 GPU oracle tests across 5 test files pass on RTX 3050 Ti (sm_86)
- `every_fold_and_soft_reset_entry_has_dispatch_arm` regression test
now passes (was failing pre-change)
- 14 baseline lib test failures unchanged (none introduced by this
commit)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The Phase 1.1 sp20_stats_compute kernel (de922c6a4) assumed the SP14-C aux
head emits 3-class logits {short, hold, long} with baseline 1/3. This was
an error in the SP19+20 spec — production aux head emits K=2 logits
{down, up} per gpu_aux_heads.rs:61 (AUX_NEXT_BAR_K = 2, established by
SP13 B1.1a). Wiring K=2 production aux into the K=3 kernel = OOB reads +
corrupt stats.
Retargets the kernel + launcher + tests to K=2 atomically per
feedback_no_partial_refactor:
- Kernel: SP20_K_CLASSES 3→2; SP20_UNIFORM_K3 0.333…→SP20_UNIFORM_K2 0.5f;
Pass A reads 2 logits (was 3); aux_conf range [0, 1/2] (was [0, 2/3]).
- Launcher: AUX_K_CLASSES 3→2; renamed unit test
aux_k_classes_is_three → aux_k_classes_matches_production_aux_head.
- Tests: rewrote CPU oracle for K=2 input shape and 0.5 baseline; updated
expected p50/std ranges; redesigned heterogeneous-distribution test to
use ramped (not lockstep) clusters — K=2's saturated softmax in the hot
half collapses every row to bin 255, triggering the
pearl_sp4_histogram_warp_tile_undercount trap; ramped clusters distribute
bin indices across each warp's 32 lanes so the histogram path matches
the CPU oracle within bin_width tolerance.
Phase 1.2 (sp20_emas_compute) and Phase 1.3 (sp20_controllers_compute)
consume the scalar [p50, std] outputs and do NOT carry the K dimension.
Both regression suites verified passing unmodified:
- sp20_emas_compute_test: 4 GPU + 1 unit, all pass.
- sp20_controllers_compute_test: 7 GPU, all pass.
Verification (RTX 3050 Ti, sm_86):
- sp20_stats_compute_test: 4 GPU oracle + 4 launcher unit, all pass.
- cargo check -p ml --features cuda: clean (pre-existing warnings only).
Spec + plan amended at top with "AMENDED 2026-05-09" notes; audit doc
Phase 1.1 entry has a "K=2 fixup" subsection documenting the change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Q1 design decision (b): add explicit hold_reward_ema to center the per-bar
Hold reward, so Q(Hold) and Q(trade) targets are scale-comparable. The
marginal Q(trade) > Q(Hold) preference now comes from data variance in
each state (high-aux states pull Q(Hold) more negative), not from
structural scale asymmetry that depends on cost_scale magnitude.
Q2 decision: keep 4-quadrant fixed (no ramping partials). Already in spec.
Changes:
- §4.2 Hold opp-cost: dual emission documented — R_per_bar_centered
(= R_per_bar - hold_reward_ema) for Q-target/replay tuple,
R_per_bar uncentered for hold_baseline_buffer (Component 1 baseline)
- §4.5 Kernel 1: hold_reward_ema added (per-step on Hold-state bars only)
- §5 data flow: per-bar reward path shows the centered/uncentered split
- ISV slots: 9 → 10 (HOLD_REWARD_EMA_INDEX added)
- §8 footprint: Component 2 LoC 70 → 90 (+20 for dual emission)
- Total LoC estimate: 1620
Mirrors the SP17 PP.1 plan-copy pattern. Plan and spec source-of-truth
live on the sister `feat/sp17-dueling-q-network` worktree; this commit
copies them onto the SP18 branch so subsequent commits reference local
paths.
Spec: docs/superpowers/specs/2026-05-08-sp18-reward-shape-hold-attractor-design.md
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>
Second critical review pass found 10 more issues, 3 critical.
All addressed:
CRITICAL:
- §8.2 (3.1): ALPHA_SPLIT cold-start was unspecified — formula
produces 0/0=0, not the claimed 0.5 sentinel. Now: ISV slot
initialized DIRECTLY to 0.5 in trainer constructor; formula
takes over only after both grad-norm EMAs accumulate N_warm
non-zero observations
- §9.2 (3.5.4): plasticity now performs TWO-STEP recovery:
(1) Flat all positions at fire bar (close current losing trade),
(2) engage warmup cooldown forcing Hold for M_warm bars.
Without step 1, forced Hold preserved the losing position
that drove drawdown for the entire 200-bar warmup
- §12.2: stale "5-10%" baseline cost estimate updated to "15-25%"
matching §6.4 (was contradicting earlier amendment)
IMPORTANT:
- §9.2 (3.5.5): DD_TRAJECTORY_DECREASING threshold 0.02 hardcoded
→ ISV-driven via new slot DD_TRAJECTORY_FLOOR (slot 441,
25th percentile of running dd_pct distribution)
- §8.2 (3.5): HOLD_FLOOR_ALPHA tracked from rolling 95th percentile
of |Q_dir| (NOT running max — was outlier-ratchet vulnerable)
- §9.2 (3.5.3): MEDIAN_STREAK_LENGTH formerly undefined in cooldown
K formula → ISV-driven via new slot 442, running median of
observed loss-streak lengths via two-heap algorithm
- §9.2 (3.5.2): asymmetric reward × α split compound interaction
explicitly stated as intentional with POS_CAP as binding ceiling
NIT:
- §7.4: "2C: Group 3 (4 tests)" → "(5 tests)" (was off-by-one
after 2.22 added)
- §9.2 (3.5.4): Xavier → Kaiming-He init for advantage head reset
(architecturally appropriate for ReLU-gated activation chain)
ISV_TOTAL_DIM: 441 → 443 post-SP15 (added DD_TRAJECTORY_FLOOR
and MEDIAN_STREAK_LENGTH at slots [441..443)). 46 SP15 slots
total. File: 799 → 811 lines.
Spec is now consistent end-to-end with no contradictions between
sections, no hardcoded values violating feedback_isv_for_adaptive_
bounds, and no underspecified load-bearing parameters.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two further user-flagged corrections from second-critical review:
1. Direction Q-head emits K=4 actions, NOT K=3.
Authoritative source: state_layout.cuh:123-126
#define DIR_SHORT 0 // open/maintain short
#define DIR_HOLD 1 // keep current position (no-op)
#define DIR_LONG 2 // open/maintain long
#define DIR_FLAT 3 // close all to zero
The "default: 3 — Short/Flat/Long" comment at gpu_dqn_trainer.rs:2438
is stale pre-SP13. Production callsites all set branch_0_size: 4.
SP13 added DIR_HOLD as a separate fourth direction action (Hold-pricing).
The config default comment was never updated when DIR_HOLD landed.
This is exactly the feedback_trust_code_not_docs failure mode — a
single stale comment would have silently corrupted the q_disagreement
signal (Hold/Flat being indices 1/3 instead of just Flat=1).
Updates:
- B.1 action-space context: 4 actions with Hold AND Flat both
non-committal (Hold = keep position, Flat = exit to zero)
- B.2.3 q_disagreement mapping: K=4↔K=2 with both Hold and Flat
masked from disagreement signal (no new directional commitment
to evaluate); only Short and Long picks contribute to disagreement
- Edge case handling for all-Hold/all-Flat batches
2. Adaptive β rate limiter (was structural β=0.9).
Per feedback_isv_for_adaptive_bounds, β should be signal-driven
not hardcoded. v3 derives β from variance of α_grad_raw, mirroring
the k_aux/k_q variance-driven steepness pattern in B.2.5.
Formula:
β = clip(β_base + variance_alpha_raw / variance_ref_alpha,
[β_base, β_max])
β_base = 0.5 (light smoothing baseline; ~2-step half-life)
β_max = 0.95 (heavy smoothing; ~20-step half-life)
Stable α_grad_raw → β = β_base (preserves directional intent)
Volatile α_grad_raw → β → β_max (dampens jitter)
Adds 2 ISV slots:
ALPHA_GRAD_RAW_VARIANCE_EMA_INDEX (Welford variance)
BETA_RATE_LIMITER_ADAPTIVE_INDEX (current β value)
ISV slot count: 11 → 13 (net +2 for variance + adaptive β).
LOC estimate: ~1150 → ~1180 (negligible delta; 3 Welford
variances now in alpha_grad_compute_kernel instead of 2).
HEALTH_DIAG pearl_egf_diag emit updated to expose all three
adaptive scalars (α, β, k_aux/k_q) plus all three driving
variances (var_alpha, var_aux, var_q) for full observability.
Verified against current code at HEAD d243a6f08:
- state_layout.cuh:123-126 (DIR_* enum truth source)
- gpu_dqn_trainer.rs:2438 (stale K=3 comment confirmed)
- branch_0_size: 4 in 5 production callsites
(smoke_tests, gpu_iqn_head, gpu_backtest_evaluator)
- DIR_HOLD usage in experience_kernels.cu:1298 + 14 other sites
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
8 corrections from code-anchored critical review at HEAD eaf4adcb9:
1. Direction Q-head emits K=3 (Short/Flat/Long), not K=4. Aux head
emits K=2 (down/up). Gate 2 q_disagreement now uses K=3↔K=2 mapping
with Flat masking. Verified against gpu_dqn_trainer.rs:2438.
2. Forward launch order constraint added: aux forward must complete
before direction Q-head forward (new serial dep). Cited
pearl_canary_input_freshness_launch_order.
3. Adaptive sigmoid k formula fixed: v1 had unreachable k_max=50
because formula caps k ≤ k_base. v2 uses max(..., k_min) with
k_max = k_base implicit.
4. α_grad rate limiter promoted from nice-to-have to v1. Schmitt
state-flip introduces sigmoid discontinuity. β=0.9 EMA smoothing
added; new ALPHA_GRAD_SMOOTHED_INDEX slot.
5. q_disagreement_baseline drop: v1 had adaptive baseline as long-EMA
(feedback loop risk). v2 uses structural 0.5 (analytic K=3-with-
Flat-masked random alignment). Drop BASELINE_INDEX slot.
6. Backward gradient scaling clarified: α_grad scales dL/dx (input
gradient flowing back to aux), NOT dL/dW (Q-head's weight grad).
Q-head learns to use the wire freely; gate only controls upstream
flow.
7. 4 hard rules added: feedback_no_hiding,
feedback_no_htod_htoh_only_mapped_pinned,
feedback_kill_runs_on_anomaly_quickly,
pearl_canary_input_freshness_launch_order.
8. Smoke A2 explicit kill criteria table added (8 triggers).
Net ISV slot count unchanged (11), composition shifted: dropped
BASELINE, added SMOOTHED. Total impl cost ~1150 LOC (was ~1060).
Verified against current code:
- TARGET_DIR_ACC_INDEX=372, AUX_DIR_ACC_SHORT_EMA_INDEX=373,
AUX_DIR_PREDICTION_INDEX=375 (sp13_isv_slots.rs)
- set_aux_weight clamp(0.05, 0.3) at gpu_dqn_trainer.rs:14722
(confirms Bug 3 from Smoke A diagnostic)
- mag_concat_qdir precedent at experience_kernels.cu:4560
(direction-conditioning pattern; SP14's wire is the analog)
- state_reset_registry pattern at lines 913-922 (canonical
template for new EMA fold-reset entries)
- branch_0_size = 3 in production config (the K=3 finding)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Designs the SP14 chain on top of SP13 Layer B (HEAD 6657e5626):
1. Sub-project A — stability fixes (3 small bugs found in Smoke A)
- C51 atom-probability floor (ISV-driven from SP4 atom_pos_p99)
- aux_w setter clamp lift [0.05, 0.3] → [0.15, 1.5]
- Stagnation warmup gate at fold boundary
2. Sub-project B — the architectural piece (THIS spec)
- Forward wire: aux_softmax_diff per-bar into direction Q-head input
concat (in_dim+1, fingerprint bump, zero-init new column)
- Earned Gradient Flow pearl — adaptive ISV-driven gradient gating:
* Gate 1 (aux competence) — Schmitt-trigger hysteresis
* Gate 2 (Q-head disagreement) — NEW signal, EMA per-step argmax
mismatch
* ISV-adaptive sigmoid steepness (variance-driven k_aux, k_q)
* Per-epoch warmup ramp
* Anti-gradient-hacking circuit breaker (mesa-opt defense)
- 11 new ISV slots, 3 new GPU kernels, ~1060 LOC total
- HEALTH_DIAG pearl_egf_diag observability line
3. Sub-project C — Adaptive LR (deferred until A+B effects measured)
Motivation from Smoke A diagnostic:
- aux_dir_acc reached 0.61 (signal extraction works)
- val_win_rate stuck 45-48% (no path to action selection)
- WR-flat-while-aux-varies = Q-head directional weights frozen
- 1109 GRAD_CLIP_OUTLIER events (chronic; not noise)
Three parallel diagnostic agents triangulated three interlocking root
causes:
- Slot 375 has zero readers (the wire was scoped but never built)
- C51 raw grad reaches 9.5e6, saturates SP7 budget controller
- aux_w controller muzzled by SP11-era [0.05, 0.3] clamp
The Earned Gradient Flow pearl is a new application of the codebase's
pearl pattern: ISV-driven adaptive controller, but applied to backward-
pass gradient flow instead of forward-pass features. The wire is one-
way (stop-gradient) by default; co-training is earned by both:
(a) aux head demonstrating label competence, AND
(b) Q-head showing it's actually fighting aux signal (informative
disagreement above baseline).
Stability additions hardened against:
- Oscillation around target (Schmitt hysteresis)
- Numerical sigmoid saturation (argument clipping ±30)
- Stale variance EMAs across folds (state-reset-registry)
- Discontinuous warmup transitions (linear ramp)
- Mesa-optimization (gradient-hacking circuit breaker)
- Cold-start sentinel-state spurious gate openings (Pearl-A bootstrap)
Awaiting user review before invoking superpowers:writing-plans for
sub-project B (and a separate small plan for sub-project A).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
P0a.T3 v2 implementer's audit revealed `DirectionAction` enum doesn't exist; the
codebase uses an 8-variant fused `ExposureLevel` (ShortSmall/Half/Full, Hold,
LongSmall/Half/Full, Flat) with cross-crate consumers across 77 files and 32+
test files pinning the 8-variant invariant. Atomic Hold elimination would
cascade massively.
User insight (2026-05-04): Hold being FREE is the bug, not Hold itself. MFT
trading legitimately needs multi-bar holds; we want the model to use them
deliberately, not as a CQL-bias lazy default. Holding isn't free in the real
world — broker fees, margin interest, opportunity cost.
v3 reframes as Hold-pricing:
- 4-way action space stays; ExposureLevel::Hold stays; no cross-crate cascade
- 3 new ISV slots (380-382): HOLD_COST_INDEX, HOLD_RATE_TARGET_INDEX,
HOLD_RATE_OBSERVED_EMA_INDEX
- Hold-rate observer: small GPU kernel + Pearls A+D smoothing
- Hold-cost controller: 5-line deficit-driven formula
(excess > target → cost rises 1×→5× base; observed ≤ target → relax)
- Per-bar reward subtraction at action == DIR_HOLD site
- 2 GPU oracle tests for the controller
P0a.T3 cuts from ~250 LOC + 32-test cascade → ~120 LOC additive. T1+T2
already-staged work unchanged. T4/T5/Layer B/C/D structure preserved.
Tension with pearl_event_driven_reward_density_alignment acknowledged in
spec — per-bar Hold cost is exposure-NEGATIVE (away from Hold), models real
economic carry, ISV-bounded by controller. Inverse of the pearl's failure
mode. Faithful reward modeling, not artificial shaping.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three architectural changes in one unified design to push the model from
HFT noise extraction (62% trade rate, sharpe-gaming) toward MFT alpha-hunting:
1. Asymmetric bounded cap (-10/+5) — restores loss aversion erased by
SP11 symmetric cap. 2:1 ratio matches Kahneman/Tversky prospect theory.
Anchor: pearl_audit_unboundedness_for_implicit_asymmetry.
2. Min-hold soft penalty with temperature curriculum — patience requirement
at exit. Soft factor = deficit/(deficit+T), T anneals 50→5 over 50 epochs.
Forces commitment without paralysis in early training.
3. Zero per-bar shaping (gate micro/opp_cost on events) — eliminates
continuous-reward gradient that pulls toward continuous exposure.
Anchor: pearl_event_driven_reward_density_alignment.
Combined: reward fires only on trade events with prospect-theory loss
aversion + commitment requirement. Pure per-trade event-driven Q-learning
properly aligned with per-trade P&L objective.
~50 LOC across 3-4 files. No new ISV slots in Phase 1 (constants only).
Cost ~€1.30 (€0.30 smoke + €1.00 30-epoch validation).
Empirical motivation: train-multi-seed-pmbwn 50-epoch on commit 6a259942e
showed sharpe-gaming pattern (PnL -30% over 8 epochs while sharpe held).
SP11 cap fix unmasked the per-bar shaping bias plus erased loss-aversion
that the unbounded loss path was implicitly providing.
Continues on sp11-reward-as-controlled-subsystem branch — SP12 is
architectural continuation of SP11, not separate work.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
smoke-test-6wd2c on commit 61b2fa962 (B1b + 4 bug fix-ups) revealed a 5th
pathology not covered by the previous fix-ups: the mag-ratio canary's
linear-magnitude-ratio formula amplifies whichever component is
intrinsically largest, regardless of whether that's a useful signal.
Popart (trade P&L on segment_complete) is O(100) per fire while the
other 5 components (cf/trail/micro/opp_cost/bonus) are O(0.1-2). Even
with the slot 360 fix preventing total-reward contamination, popart's
intrinsic magnitude makes popart_mag / Σ ≈ 0.93. The controller blend
'winner_weight = ratio' then amplifies popart further. Smoke trajectory:
w_pop=2.0 → 2.44 → 2.57, curiosity_b=30 → 120 → 199, sharpe_ema=10.7 →
2.4 → 0.75 (cascading collapse).
Resolution: z-score normalization. Each component's magnitude divided
by its own running standard deviation before computing the ratio:
popart_z = popart_mag_ema / max(sqrt(popart_var_ema), EPS_DIV) ≈ O(1)
cf_z = cf_mag_ema / max(sqrt(cf_var_ema), EPS_DIV) ≈ O(1)
...
ratio[c] = component_z / Σ component_z ≈ ~1/6 each when stable
Allocates 6 new ISV slots [361..367) for per-component variance EMAs.
Producers: extend popart_component_ema_kernel + reward_component_ema_kernel
to also emit variance via Welford's online algorithm (single-pass).
SP5_SLOT_END = 367, ISV_TOTAL_DIM = 367.
Carries forward main's slot 360 amendment (commit 52c0b7521 on main)
which the sp11 branch was missing, plus this z-score amendment.
Per-component gradient ratios (the original spec intent) don't fix this
either — in DQN there's no per-component gradient pathway; grad norm
scales with current_weight × magnitude, so it's the same bias. Z-score
normalization is the standard scale-invariant measure of significance
and matches what the SP11 controller is trying to express.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
User correction: curiosity is the *fix* for the ep1-peak overfitting
pathology, not a hazard to defend against. Reframed §7 from "Risks"
to "Design notes" — curiosity bound is a signal-relative scale, not
a defensive cap.
Fix the contradiction this exposed in the formula: previous
`curiosity_pressure = stagnant_or_worse * curiosity_bound` went to
zero when improving, which would cancel the always-on exploration
the §7 narrative now relies on. Replace with permanent-floor pattern
per pearl_blend_formulas_must_have_permanent_floor:
curiosity_floor = 0.2 * curiosity_bound (CURIOSITY_PERMANENT_FRACTION)
curiosity_dynamic = stagnant_or_worse * curiosity_bound
curiosity_pressure = max(curiosity_dynamic, curiosity_floor)
Now curiosity is always ≥ 20% of bound (anti-overfitting baseline)
and rises toward the bound when stagnant (stagnation breaker).
Updated unit-test guidance to assert pressure > 0 even at z=+10.
CURIOSITY_PERMANENT_FRACTION=0.2 added to Invariant-1 fraction list.
Saboteur-rising-with-improvement reframed as adversarial-load feature
rather than over-stress risk.
Brainstorm spec for SP11. Resolves the policy-stagnation pathology
surfaced in T10 train-multi-seed-xkjkb seed-0 ep0-14: model finds a
stable fixed point at ep1 (peak val sharpe 80.61), then OVERFITS to
it across remaining epochs (decline 80.61 → 70.58). Q-values grow but
val performance declines because reward function has no improvement
pressure.
Architecture (every input ISV-driven):
- Z-score-driven adaptation (no hardcoded "improving" threshold):
improvement_z = val_sharpe_delta_ema / max(val_sharpe_std_ema, EPS)
- 10 ISV outputs: 6 component weights + curiosity_pressure +
saboteur_intensity_mult + adaptive weight_floor + curiosity_bound
- 5 ISV canaries: val_sharpe_delta + val_sharpe_std (Z-score noise
estimate) + 6 per-component grad ratios + saboteur engagement +
PnL magnitude EMA (signal-relative curiosity bound)
- 4 new producer kernels (controller + 3 canary computers)
- Audit + migrate hardcoded cf_weight=0.3 in mse_loss_kernel.cu:318
and c51_loss_kernel.cu:789, plus other shaping multipliers
- NEW reward dimension: curiosity bonus, bounded by PnL magnitude
Per pearl_controller_anchors_isv_driven: every threshold replaced with
sigmoid(z) — no constants encode "what counts as improving". Per
pearl_blend_formulas_must_have_permanent_floor: every weight has
adaptive floor preventing zero-out. Per pearl_engagement_rate_self_
correction: saboteur intensity self-corrects via engagement rate canary.
Per pearl_cold_start_exit_signal_or: improvement signal OR'd from
multiple canaries so single-signal-failure doesn't stall controller.
New pearl authored alongside spec: pearl_reward_as_controlled_subsystem
— meta-principle that every reward path degree of freedom is a unified
controller output. Subsumes controller-anchor pearl at the reward layer.
Scope: 20 ISV slots, 4 producer kernels, audit + migration of
hardcoded reward shaping constants, 1 atomic commit.
~1300-1700 LOC; ~2.5-3 hours subagent work.
Success metric: val_sharpe[ep20] > val_sharpe[ep1] (Fix 33-38 baseline
peaked at ep1; SP11 should shift peak later as model continues
learning).
Brainstorm spec for SP10. Resolves the val-Flat-collapse pathology that
persisted through Fix 33-37: the eval-time argmax in experience_action_
select picks Hold deterministically every val bar (dir_entropy=0,
trade_count=1 in 214k bars) regardless of controller state.
Architecture:
- Delete `if (eval_mode)` argmax branch in direction-selector kernel
- Use temperature-blended Thompson: q_eff = E[Q] + τ × (Thompson - E[Q])
- τ = clamp(intent_eval_divergence / divergence_target, 0.5, 2.0)
- τ self-corrects: collapse → high τ; healthy → low τ; permanent 0.5 floor
- Reuses SP9's intent_eval_divergence_compute_kernel (extended, not new)
Per pearl_controller_anchors_isv_driven: τ is ISV-driven, no
hardcoded constants beyond Invariant 1 numerical anchors (clamp range).
Per pearl_blend_formulas_must_have_permanent_floor: MIN_TEMP=0.5 ensures
eval ALWAYS has stochasticity.
The pearl_thompson_for_distributional_action_selection was about Bellman
TARGET argmax (selector/target symmetry). It does NOT prohibit Thompson
at the rollout selector. SP10 amends the pearl to clarify.
Scope: 1 ISV slot, kernel modification (no new kernel — extend SP9's
producer), 1 consumer kernel rewrite, test update, pearl amendment,
audit doc Fix 38. ~300-500 LOC, single atomic commit per
feedback_no_partial_refactor.
Brainstorm spec for SP9 (Fix 37). Resolves the val-Flat-collapse pathology
surfaced in T10 train-multi-seed-wsnc6 ep1-3: training intent_dist_f=0.47
but eval_dist_f=0.00 because Kelly cap pinned eval mag to Quarter, with
trade_count=1 in 214k bars (chicken-and-egg: cap closed → no trades → no
Kelly samples → cap stays closed).
Architecture per pearl_controller_anchors_isv_driven and
pearl_cold_start_exit_signal_or:
final_kelly_f = max(measured_kelly_f, warmup_floor)
warmup_floor = base_floor × (1 − combined_confidence)
base_floor = clamp(q_var_mag / q_var_mag_ema, MIN, MAX)
combined_confidence = max(statistical, behavioral, temporal)
All thresholds ISV-driven via Pearl D Wiener-α; only Invariant 1 numerical
anchors (clamp ranges, EPS) remain as constants.
Scope: 9 new ISV slots, 6 new GPU producer kernels including a
mandatory eval_dist GPU migration (eliminating DtoH readback at
gpu_backtest_evaluator.rs:1353 per feedback_no_cpu_compute_strict).
~1000-1200 LOC, single atomic commit per feedback_no_partial_refactor.
Pearls authored as part of brainstorm:
- pearl_controller_anchors_isv_driven (regime-encoded constants)
- pearl_cold_start_exit_signal_or (OR'd signals across statistical /
behavioral / temporal axes)
Outcome-driven controller replacing Pearl 2's hardcoded-0 CQL budget and
floor-pinned C51 budget with multiplicative ratio adaptation. Targets
`grad_split_bwd cql/iqn = 2.0` and `c51/iqn = 1.0` per slice (trunk, dir,
mag). Slow EMA (α=0.01) consistent with existing controller pearls; Pearl
A sentinel-bootstrap on fold boundary; Pearl D Wiener-optimal smoothing.
Replaces ghost docstring at fused_training.rs:3409 ("0.10×(1−regime)×health"
formula was never implemented). Pearl 2 keeps owning IQN budget (reference
denominator) and FLATNESS_BASE (consumed by NoisyNet σ pearl); stops
writing CQL/C51/ENS slots so the new controller is the sole driver.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Critical self-review surfaced 15 issues; user directed fixes:
- "a no cpu path": KEEP host-EMA close-out (rule compliance) — but split
into separate Layer D atomic commit (was mis-scoped as Layer A)
- Pearl 4 kept: 3 concrete risks documented (constant-β proof break,
β2 memory reset destabilization, ε numerical envelope), structural
envelope bounds added, ALPHA_META halved, ε-only fall-back path defined
- Pearl 6 Kelly cross-fold persistence carve-out: separate slot range
280..286, NOT in SP4 fold-reset registry, Invariant 1 architectural exception
- Commit ordering Pearls 1 → 3 → 2 → 4 → 5 → 6 → 8 → 1-ext (resolves
Pearl 2 circular dependency on 1+3)
- Acceptance criteria: correctness gates (must pass) + performance gates
(loosened to "not catastrophically negative", within 2σ of pre-SP5)
- Pearl 7 timing: explicitly Layer C step 4, post-Layer-B + 3-seed validation
- Pearl 8 enumeration: 4 slots (TRAIL_DIST_PER_DIR per direction)
- Pearl 9 collapsed: 0 slots (Thompson achieved via Pearl 1's atom adaptation)
- Total slot count corrected: 110 (was 120-128 inconsistent)
Layer structure: A (additive, 8 commits) → B (atomic, 11 consumers) → C
(validation + Pearl 7 investigation) → D (host-EMA close-out, separate
atomic commit). Layer D split off from A's "close-out" because PnL
aggregation pipeline migration is its own architectural concern.
User final review pending before invoking writing-plans skill.
Per user direction:
Q1 (Layer A granularity): per-pearl commits (~9 commits, decision c)
Q2 (Pearl 7 timing): investigate-only in SP5; fix in follow-up
if Bin(2,0.5) persists post-Pearls-1-3 (decision a)
Q3 (Pearl 4 Adam β): include as designed; accept theoretical risk
with Pearls A+D + EPS_CLAMP_FLOOR mitigation;
Layer C smoke monitors for destabilization;
fall-back to ε-only if observed (decision a)
Spec section updated:
- Layer A description: per-pearl commit structure
- Pearl 7 framed as investigation-only with conditional follow-up
- Pearl 4 documents theoretical caveat + mitigation + fallback
User final review pending before invoking writing-plans skill.
SP5 design covers every known adaptive-parameter deferral in the DQN
training loop in a single coherent project. After SP5: zero hardcoded
multipliers, every adaptive value ISV-driven via Pearls A+D.
9 pearls + 1 sweep close-out + 1 validation milestone:
1-3. Per-branch atom span / loss budget / NoisyNet σ (52 slots)
4. Per-group Adam β1/β2/ε ISV-driven (24 slots)
5. Per-branch IQN τ schedule (20 slots)
6. Kelly cap signal-driven floors (6 slots)
7. dist_q/h/f Bin(2,0.5) audit + action_select fix (0-8 slots)
8. Trail stop signal-driven thresholds (6-8 slots)
9. Thompson direction-branch temperature (4 slots)
1-ext. Per-branch C51 num_atoms (4 slots)
Layer A close-out: 5 host-EMA host→GPU migrations
Validation: 3-seed × 50-epoch acceptance gate
Total: 120-128 new ISV slots, ~5000-7500 LOC, 11-13 producer kernels,
~12 consumer migrations.
Layer A (additive infrastructure, ~15 commits) → Layer B (atomic
consumer migration, single coordinated commit) → Layer C (validation +
cleanup). Mirrors SP4's layer pattern.
Triggering data: train-multi-seed-cv2mw 50-epoch L40S baseline
(terminated F0 ep10) revealed magnitude head Q-flatness, eval collapse,
and frozen action distributions. Plus all SP4 close-out + sweep
deferrals folded in per user direction "no deferrals — make a single
plan based on ALL findings".
Spec at:
docs/superpowers/specs/2026-05-01-sp5-magnitude-differentiation-and-eval-collapse-design.md
User review pending before invoking writing-plans skill.
Layer C close-out C1 redesigned. Original plan claimed
grad_norm_slow_ema_pinned was orphan post-Mech-6 migration; verification
surfaced a SECOND live consumer (fold_warmup_factor_update, commit
4ef1d8ebb) that legitimately reads the slow EMA as cross-fold
steady-state baseline.
The actual defect: the EMA UPDATE at update_adaptive_clip:22720-22737
was host-side `(1-α)*prev + α*obs` arithmetic — exactly the pattern
feedback_no_cpu_compute_strict (saved 2026-05-01) strictly forbids.
Migrated:
- New `update_grad_norm_emas_kernel.cu` — single-thread fused fast+slow
EMA update kernel. Reads `grad_norm_buf[0]`, updates two mapped-pinned
EMA scalars via dev_ptr. `__threadfence_system()` ensures the
`fold_warmup_factor_kernel` consumer sees freshly-written values.
- `launch_update_grad_norm_emas` Rust launcher chained on the
producer's stream — graph-capture-compatible, no host sync.
- update_adaptive_clip's host-side `unsafe { ... }` block replaced
with the GPU launcher call (warn-and-continue on launch failure
mirroring the launch_h_s2_rms_ema / launch_fold_warmup_factor
per-step ISV producer pattern at training_loop.rs:3450/3464).
Preserved:
- grad_norm_slow_ema_pinned mapped-pinned buffer (cross-fold persistent;
legitimate consumer is fold_warmup_factor_update).
- Fixed-α design (FAST_ALPHA=0.1, SLOW_ALPHA=0.001) — Pearls A+D
adaptive α would defeat the cross-fold-baseline semantic the warmup
factor depends on.
- Cold-start sentinel (`prev ≤ 0.0` ⇒ assign obs directly) — same
formula as the deleted host code.
- Host-side update_adaptive_clip early-return guard — kernel only
launches when observed_grad_norm is finite and > 0.
- grad_norm_emas_step_count host counter — scalar control-flow
metadata for warmup-window gating, not compute.
Plan/spec docs updated to remove stale "orphan" claim. State-reset
registry doc-comment + field doc-comments updated to reflect GPU-only
update path. fold_warmup_factor_kernel docstring no longer describes
its grad-norm EMA inputs as host-side-fed.
Build clean, sp4 + state_reset_registry lib tests pass (11/11), 16/16
SP4 producer GPU tests pass on RTX 3050 Ti. No behavior change — pure
architectural fix.
Refs: SP4 Layer C C1 redesigned (was: retire). Original plan
docs/superpowers/plans/2026-04-30-sp4-signal-driven-magnitude-control.md
lines 2184-2221.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
PEARL A — First-observation bootstrap: eliminates Xavier-derived
formulas (2.33 z-score, √2 std, √(2/K_in)) from the bootstrap section.
Sentinel ISV[X]=0 at fold reset; producer step 0 detects sentinel,
replaces directly with step_observation. Subsequent steps EMA-blend.
Consumer cold-start safety via .max(1.0) numerical floor only (Adam-ε
category, not magnitude). Truly zero magnitude constants in bootstrap.
PEARL B — Fused per-param-group statistics oracle: producer count
36 → 14. Per-group fused kernel reads (params, grads, adam_m, adam_v)
once and computes WEIGHT_BOUND, ADAM_M_BOUND, ADAM_V_BOUND, WD_RATE
in one multi-pass operation. Trunk's oracle adds Pass E for L1_LAMBDA
gradient-direction entropy. 4× memory bandwidth reduction. Cleaner
conceptual unit (per-param-group bounds = one oracle).
PEARL C — Engagement-rate self-correction: detects post-clamp
feedback-loop saturation. For in-kernel clamps, theoretical engagement
rate = 1% (top 1% by p99 definition). Producer-side rate-deficit EMA
detects sustained deviation; force-bumps bound to step_max when
detected. Resolves the "in-kernel feedback loop accepted" limitation
from first draft. Per-Adam-kernel block-shared-memory engagement
counter (no atomicAdd), block-wide reduce, host-side rate-deficit EMA.
PEARL D — Wiener-optimal adaptive α: replaces all hardcoded EMA rates
across 14 new producers AND 7 existing pre-SP4 producers. Per-step:
α* = diff_var / (diff_var + sample_var + ε_num). Theoretically optimal
under Wiener-filter analysis; subsumes Pearl A as t=0 edge case
(both vars=0 → α=1 → first-observation replacement). On stationary
signals: α→0 (smooth). On non-stationary: α→1 (track). Eliminates
the recursion problem (α controlled by signal stats, not another α).
ε_num = 1e-8 (Adam-ε numerical category). 3 floats of state per
producer. 36+ hardcoded α values eliminated codebase-wide.
Limitations section restructured: 6 of 8 first-draft limitations
RESOLVED by pearls (in-kernel feedback loop, smoke time-budget,
producer plumbing, stale-bound, 8 carved-out items, magnitude bootstrap
formulas). Remaining 6 limitations are genuinely irreducible (F0
launch-scheduling variance, Layer B atomic flip risk, novel-pearl
field-validation, equilibrium-formula non-stationarity, Pearl C
counter-state cost, Pearl D state cost).
SP4 now closes 100% of the magnitude/regularization/EMA-rate surface.
No hardcoded scalars remain in the entire bound/clamp/EMA chain.
Producer count: 14. ISV slot count: 36. Unit tests: 36.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
