67dd414cb32d5702bf792b299fa83ea46454cfa0
165 Commits
| Author | SHA1 | Message | Date | |
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jgrusewski
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6e6e0fa114 | docs(sp5): remove duplicate Layer A close-out section (superseded by Layer D) | ||
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jgrusewski
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af8fe57d1c |
docs(sp5): apply critical review fixes — Layer D, Kelly carve-out, Pearl 4 mitigations, acceptance loosened
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.
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jgrusewski
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3361944456 |
docs(sp5): resolve open questions Q1=c, Q2=a, Q3=a
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.
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jgrusewski
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5f1c1eec51 |
docs(sp5): comprehensive per-branch + per-group adaptation spec — close all hardcoded-multiplier deferrals
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. |
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jgrusewski
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24accea774 |
fix(sp4): migrate fast/slow grad-norm EMA update to GPU per feedback_no_cpu_compute_strict
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
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jgrusewski
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d8666a232f |
docs(sp4): fix all 11 review findings — spec is now source of truth
Resolved every issue from the critical self-review:
1. Param-group count: 7 → 8 throughout. Slot total: 36 → 40 (8 groups × 3
per-group families = 24 + 16 single = 40). IQL high-tau and low-tau
are 2 distinct param groups (separate buffers + Adam states), not
one. The "(Resolved during implementation)" hand-wave deleted.
2. Reset semantics section rewritten — no longer references Xavier-
derived bootstraps. Sentinel 0 per Pearl A; Wiener-state triples
reset to 0 alongside ISV slots (160 reset entries total).
3-4. Weight decay and L1 lambda hardcoded α/bootstrap removed. Both
now follow universal Pearls A+D contract (sentinel-detect +
Wiener adaptive). For L1, the natural curriculum (λ ramps as
gradient differentiates) emerges from the signal itself, no
"Bootstrap = 0.0" constant needed.
5. ε naming collision resolved: ε_div = 1e-8 (Pearl D division-safety),
ε_clamp_floor = 1.0 (Pearl A consumer cold-start floor). Distinct
names, distinct purposes.
6. Per-signal kernel signature updated: removed stale `ema_alpha` arg,
added `wiener_state` pointer + `wiener_state_offset` + uniform
`alpha_meta` (structural meta-EMA constant).
7. Pearl C engagement counter race fixed. Replaced `clamp_engage_buf
[group] += 1` (race) with proper register-then-tree-reduce pattern
mirroring `dqn_grad_norm_kernel`. Per-thread register counter →
block-shared tree-reduce → single block-leader writes to
`clamp_engage_per_block_buf[engage_buf_offset + blockIdx.x]`.
Host sums across blocks. No atomicAdd, consistent with feedback.
8. Pearl D state buffer allocation spelled out: `wiener_state_buf:
MappedF32Buffer` of size 141 floats (47 slots × 3), per
`feedback_no_htod_htoh_only_mapped_pinned`. Reset-registry entries:
40 + 40×3 (new bound slots + Wiener triples) + 7×3 (retrofit
existing producers' Wiener states) = 181 reset entries.
9. `grad_norm_slow_ema` retirement spelled out in Layer C: removed
entirely (sole consumer Mech 6 migrates to ISV[GRAD_CLIP_BOUND]).
Also documented Q_ABS_REF=16 and H_S2_RMS_EMA=96 transition to
"monitoring-only ISV" — producers stay running, only the orphaned
Mech 1/2/5/6/9/10 consumer reads removed.
10. Histogram bin range fixed: linear-spaced bins from 0 to step_max
(avoids log(0) singularity from earlier log-spaced design).
Linear is also better for p99: top-of-distribution gets ~0.4%
resolution per bin. Degenerate "step_max == 0" branch handles
all-zero signals gracefully (skip ISV update, leave previous bound).
11. Pearl D-subsumes-Pearl-A claim CORRECTED: mathematically wrong.
At t=0, Pearl D's formula yields `x_mean[0] = 0`, not `x[0]`.
Pearl A's first-observation replacement requires an explicit
sentinel-detection branch in the producer. Both pearls are
necessary and complementary; they are not hierarchical.
Slot count math now consistent: 1 target_q + 4 atom_pos +
3×8 per-group + 1 grad_clip + 1 h_s2 + 8 wd_rate + 1 l1_lambda = 40.
Producer count: 15 fused (1 + 4 + 8 + 1 + 1).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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074b4f0865 |
docs(sp4): fold all 4 pearls into spec — A, B, C, D
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> |
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jgrusewski
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21b6058e07 |
docs(sp4): close all carve-outs — weight decay + L1 lambda fully signal-driven
Folds two new producer signals into SP4 scope, eliminating the earlier "carved out" exception: WEIGHT DECAY (per param-group, 7 new ISV slots): λ = |w·g| / max(||w||², ε) Derived from equilibrium analysis of d/dt(||w||²) = 2(w·g) - 2λ||w||². The equilibrium gradient-projection-onto-weight-direction divided by weight norm. Same theoretical-derivation category as Adam β values. EMA half-life α=0.005 (~140 steps). Bootstrap 1.0. L1 LAMBDA (trunk only, 1 new ISV slot — NOVEL PEARL): λ = (mean(|g|) / mean(|w|)) × D where D = (log K - H_observed) / log K is gradient-direction entropy deficit and H_observed = -Σ p[i]·log p[i], p[i] = ||g[:,i]|| / Σ ||g[:,j]|| L1 regularization-strength derives from gradient-direction entropy deficit across input features. When gradient is uniform across features (D≈0): network hasn't differentiated, λ=0 (no pruning). When gradient concentrates on few features (D≈1): network has identified what matters, λ ramps up to prune the rest. Self-curriculum — L1 strength tracks the emergence of feature differentiation. This extends pearl_adaptive_moe_lambda (regularization strength = EMA- tracked deficit of regularized quantity) to feature-redundancy domain. Pearl-name candidate (post-validation): pearl_signal_driven_regularisation_strength. Bootstrap λ=0 means cold-start = no L1 pruning; ramp-up only after gradient differentiates. Worst-case behavior is "L1 disabled" — graceful. Total ISV slot count: 28 → 36. Total producer kernels: 28 → 36. Effort estimate: 3000-4000 → 3500-4500 LOC, 1-1.5 → 1.5-2 weeks. Acknowledged limitations updated: removed item #8 (carve-out) since no carve-outs remain. Added items for L1 pearl novelty (untested) and weight decay equilibrium-formula non-stationarity. Both have graceful worst-case behavior and explicit validation criteria (#10 and #11) to detect anomalies. SP4 now closes 100% of the magnitude/regularization surface — no hardcoded scalars remain in the entire chain. AdamW config fields for weight_decay and l1_lambda removed from HyperParams to prevent accidental hardcoding regression. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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506f9443fe |
docs(sp4): fix critical issue J — post-clamp diagnostic dead path
Second-pass self-review found a new critical issue: under "diagnostic = clamp engagement", post-clamp diagnostics (Mech 9 weights, Mech 5 Adam m/v slots 36-43, slots 44-45) NEVER fire — because post-clamp |v| ≤ bound by construction, so producer-side comparison always returns false. Fix: split diagnostic implementation by clamp location. - Buffer-based clamps (Mech 1, 2, 10): diagnostic stays in producer (reads pre-clamp buffer, fires when step_max > bound). - In-kernel clamps (Mech 6, 9): diagnostic lives INSIDE the Adam kernel at the clamp step. Each Adam kernel takes a `diag_slot` arg alongside `weight_clamp_max_abs`; on clamp engagement, writes `nan_flags_buf[diag_slot] = 1`. Idempotent per-thread store (all threads writing 1, race-free per existing convention). No atomicAdd. Without this fix, slots 36-45 would be dead diagnostics under SP4 — detecting nothing, providing no signal. With this fix, every diagnostic slot fires on its corresponding clamp engagement regardless of where the clamp lives. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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cfe57109e3 |
docs(sp4): revise spec — fix 8 critical issues from self-review
Self-review found multiple problems with the first draft. This revision addresses all 8 critical issues: 1. P² parallelization claim was WRONG — P² is sequential. Replaced with dynamic-range histogram (3-pass single-block kernel: max-reduce → log-spaced bin → cumulative-from-top to find p99). 256 bins → ~0.4% quantile precision; numerical-precision derivation in same theoretical-constant category as floating-point precision. 2. Bootstrap values had wrong magnitudes — used Xavier σ instead of p99 of max-element. Recomputed: WEIGHT_BOUND[group] = 2.33 × √(2/K_in[group]); H_S2_BOUND = 2.33 × √2 ≈ 3.3. All bootstraps now from theoretical p99 under Xavier-init, not std. 3. EMA half-life of 700 steps (α=0.001) didn't converge in 5-epoch smoke. Revised α=0.005 (~140 steps) for weight/Adam producers — reaches ~99% convergence within one fold's training. Smoke now validates steady-state behavior, not just bootstrap. 4. Weight decay, L1 lambda, CLIP_MULTIPLIER were listed in scope but undesignable in producer-consumer pattern. CARVED OUT explicitly: weight decay + L1 λ → separate research-spec; CLIP_MULTIPLIER and MIN_CLIP subsumed by SP4's GRAD_CLIP_BOUND slot. 5. F0 ≥ 45 acceptance criterion was uncertain. Revised to F0 ≥ 37.5 (matches the 1e30-effectively-unclamped diagnostic smoke). The ~8-point F0 variance from launch-scheduling-shift is independent of clamp value; SP4 cannot guarantee F0=45 even with ideal design. 6. Per-param-group p99 plumbing concretized: each producer takes (offset, length) launch args; main DQN params buffer sliced into trunk/value/branch using existing param_sizes layout knowledge. 7. Pre/post-clamp feedback loop EXPLICIT: producer runs BEFORE consumer for buffer-based clamps (h_s2, target_q, atom_pos — in captured graph immediately before clamp). Producer runs AFTER for in-kernel clamps (weights, Adam m/v — feedback loop accepted with documented soft-anchor dynamics). No more hand-waving. 8. Unit test strategy: per-producer kernel test with synthetic Gaussian input → known p99 ≈ 2.33 → assert |computed - 2.33|<5%. 28 tests total. Catches algorithm bugs before L40S smoke. Effort estimate revised UP: 3500-4500 LOC (from 2-3000), 1.5-2.5 weeks (from 1-2). 28 producer kernels + 28 unit tests is more plumbing than first draft assumed. Self-review limitations section now explicit about: in-kernel feedback loop accepted, smoke time-budget marginally validates Adam EMAs, F0 may not return to 45, Layer B atomic-flip risk, plumbing density, stale-by-one-step bound, histogram-precision is design choice not tuning, carved-out items remain hardcoded post-SP4. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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e00736ce9b |
docs(sp4): signal-driven magnitude control design spec
Comprehensive design replacing every hardcoded magnitude multiplier in the SP3 mechanism stack (Mechs 1, 2, 5, 6, 9, 10) plus pre-SP3 mechs in the same magnitude-control surface, per feedback_isv_for_adaptive_bounds and feedback_adaptive_not_tuned. Core principle: the BOUND lives in an ISV slot, computed by a producer kernel as p99 EMA of observed signal magnitude. Consumer reads the slot and clamps directly — no multiplier between ISV read and clamp. Cold- start ε from theoretical-init bootstrap (Xavier, etc.) — same theoretical- constant category as Adam β values, not tuning knobs. Architecture: - 28 new ISV slots (7 base bounds × per-param-group split where appropriate) - Per-signal P² (Jain-Chlamtac) quantile producer kernels - Diagnostic = clamp engagement (sticky flag from producer's max-comparison) - Migration in 3 layers: additive infra → atomic consumer flip → smoke Out of scope: theoretical/structural constants (Adam β, Xavier formula, attention 1/√d, hidden_dim, num_atoms). EMA rates stay as documented statistical-design parameters (half-life ≈ observation time-window). Estimated: ~2-3000 LOC across 3 layers, 1-2 weeks, 1 L40S smoke. Awaiting user spec review before writing implementation plan. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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ed727b51c0 |
spec(dqn): SP2 + SP3 combined — F0 regression fix + Q-learning structural stability
Designs the fixes for the two SP1 leftover issues identified at SP1 closure
(commit
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jgrusewski
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792812baa1 |
spec(dqn): SP1 numerical stability — revise per critical review
9 substantive issues addressed inline:
1. ISV-driven design elevated from 'if applicable' to MANDATORY for
all dynamic bounds in SP1 fixes. Numerical-stability ε bounds are
the only carve-out (Invariant 1). Hardcoded tuning constants for
dynamic ranges explicitly rejected.
2. F0 Sharpe regression criterion changed from absolute (≥55) to
ratio-based (≥95% of latest baseline; floor 53.08 currently).
Prevents iterative erosion across multiple fix commits.
3. 'F1 trending positive' replaced with concrete monotone-improvement
test: Best Sharpe at last epoch ≥ Best Sharpe at first epoch of
the same fold.
4. Pass criterion distinguishes NaN-CLAMPED-TO-ZERO (failure) from
'Genuine grad collapse' (legitimate observation, permitted) per
the existing infrastructure from commit
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jgrusewski
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dab5990287 |
spec(dqn): SP1 numerical stability investigation — F1 NaN root-cause
Brainstorm session 2026-04-29 produced this spec scoping Sub-project 1 of a three-sub-project decomposition addressing the F1 ep2 NaN explosion that persists across 9+ defensive layers in Plan C Phase 2 (commits e445d07a..e9096c7be). Decomposition: - SP1 (this spec): F1 NaN root-cause; γ audit + β always-landing instrumentation + surgical fix + multi-fold validation - SP2 (future): numerical stability framework — codify guards - SP3 (future): Q-learning structural stability — target-Q clip, pessimistic ensemble, atom-range governance Operating principles: - No deferrals (anomalies fixed within SP1, not punted) - Combined fixes — rich commits (per feedback_no_partial_refactor) - Always-landing diagnostic instrumentation (24-slot nan_flags_buf expands to 48; permanent regression sentinel) - F0 Best Sharpe ≥ 55 preserved (no regression on the working path) Pass criterion: all 3 folds train 5 epochs, F0+F1+F2 Sharpe ≥ 0, zero NaN-CLAMPED-TO-ZERO log lines, all 48 flag slots remain zero. 5 design sections approved iteratively: Architecture, Components, Data Flow, Error Handling, Testing. Next: writing-plans produces implementation plan. |
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jgrusewski
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8a535681b7 |
spec(dqn): Plan C Phase 2 ACTIVE — UCB asymmetry regression triggers Thompson resumption
The original PAUSED state was motivated by measurement-artefact hunt exit. The bug-hunt cycle is complete (commits |
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jgrusewski
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fc4addaabf |
spec+plan(moe): correct gate input dim 42 → STATE_DIM=128
T1.6 implementer correctly identified that the gate input dim is
ml_core::state_layout::STATE_DIM=128, not the literal 42 the spec/plan
incorrectly stated. The 42-dim figure was the bar-feature subset; the
actual state vector is 128-dim (42 features + portfolio + MTF + OFI
padded to 128 for cuBLAS alignment).
Updated spec §3 architecture diagram, §4.1 gate subnetwork description
+ parameter count (3,272 → 8,776), and plan header architecture line.
Implementation in commit
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jgrusewski
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76e55047ec |
spec+plan(moe): add no-HtoD/HtoH constraint; mapped pinned only
Per feedback_no_htod_htoh_only_mapped_pinned.md (newly recorded): every CPU<->GPU path in this redesign uses mapped pinned memory exclusively. No cudaMemcpy HtoD, no Vec-to-Vec defensive copies, including in test code. CPU is strictly read-only on the production surface. Plan changes: - New Task 2.0 promotes MappedF32Buffer / MappedI32Buffer from distributional_q_tests.rs local definitions to a shared crates/ml/src/cuda_pipeline/mapped_pinned.rs module so all kernel test wrappers (Test 0.F, upcoming MoE tests) share one implementation. Adds write_from_slice helper for direct host_ptr write (no memcpy). - Task 2.1 test wrapper rewritten to allocate mapped pinned buffers + write to host_ptr + read GPU-written output via host_ptr. No more memcpy_stod / memcpy_dtov in test code. Spec: new section 6.4 codifies the mapped-pinned-only constraint and references the shared module + reference implementation. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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8629a9e7c2 |
spec(dqn): MoE replacement for vestigial RegimeConditionalDQN
End-to-end investigation (2026-04-27) confirmed RegimeConditionalDQN is vestigial decoration — 3 heads constructed at training start but only trending_head ever receives gradient updates. GpuDqnTrainer (the actual production GPU trainer) has zero references to RegimeType/regime routing; experience replay inserts go to trending_head.memory only; ranging_head and volatile_head stay at random init for the entire training run. Several support APIs (get_count_bonuses_branched, config, get_state_dim) hardcode-delegate to trending_head, ignoring the regime split entirely. Per `feedback_no_hiding.md` (wire up or delete) and the user's preference to fix not delete: the design wires regime conditioning properly via Mixture-of-Experts replacing the vestigial 3-head architecture. Pearl introduced and saved as `pearl_learned_gate_subsumes_handcoded.md`: when the network already sees the heuristic's inputs, a learned gate strictly subsumes any hand-coded discretization. This is the load-bearing rationale — ADX/CUSUM are already at state indices 40/41, so threshold-based regime classification is a strict information bottleneck the gate can recover and improve on. Design summary: - Architecture: shared GRN trunk -> K=8 small expert MLPs (256->64->256 bottleneck per expert, ~33k params each) -> learned gating network (state[42]->64->8 softmax) -> mixed h_s2 -> existing branching heads + C51 + IQN dual head. Soft full mixture (no top-k hardcoding); gate emerges peaky or flat from data. Anti-collapse load-balancing aux loss with default lambda=0.01 (configurable hyperparameter, not a kernel constant) prevents init-noise-dominated single-expert lock-in without forcing uniform utilization. User-confirmed signal: "collapses don't recover well in this codebase". - 9 new ISV slots (118-126: per-expert utilization EMA + gate entropy EMA), GPU-driven producer per `pearl_cold_path_no_exception_to_gpu_drives.md`. - 3 new small CUDA kernels (moe_mixture_forward/backward, moe_load_balance_loss) + 1 ISV producer; everything else is cuBLAS- reusable. CUDA Graph capture compatible. - Atomic deletion (no fallback): regime_conditional.rs (~700 LOC), RegimeType enum, classify_from_features, RegimeMetrics, RegimeClassConfig, 4 DQNConfig regime threshold fields, per-regime 3-file checkpoint format. DQNAgentType becomes thin wrapper over single DQN. Old checkpoints fail loudly with layout-fingerprint mismatch. - 5-layer testing strategy (unit kernels, smoke, gate-differentiation validation, L40S production validation with explicit kill criteria, architecture-hash backward-incompat). Out of scope (explicit): top-k routing, per-expert action heads, hierarchical MoE, regime-conditional CountBonus/NoisySigma broadcast, expert warm-start from existing trending checkpoint, CVaR action selection on mixed C51 distribution. Precondition: the in-progress use_* flag cleanup + count_bonus [f32; N] refactor lands as its own commit before MoE implementation begins, per `feedback_no_partial_refactor.md`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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0e8804a770 |
spec(dqn): reframe Thompson rollout PAUSED (not SUPERSEDED)
Retracts the prior SUPERSEDED footer (commit |
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jgrusewski
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42ffd6aadc |
spec(dqn): mark Thompson rollout SUPERSEDED — motivating pathology was a measurement artefact
The val-Flat-collapse / Short-collapse / C51 expected-Q bias hypothesis that motivated the 4-plan distributional-RL Thompson rollout was largely a measurement artefact in the diagnostic infrastructure, not a real policy pathology. Three layered bugs in actions_history_buf init + reader + mag_stats attribution conspired to inflate val_dir_dist Short, inflate active_frac, and pin wr_h/wr_f to zero. After fixing all three (commits |
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jgrusewski
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021bb0ef73 |
spec(dqn): pivot Phase 0+2 tests to GPU-direct (no CPU mirror)
User correctly identified that CPU mirror function tests don't test
the production GPU code path. A bug shared between mirror and kernel
(translated identically wrong) would slip through. Mirror tests + a
single GPU bridge test were a weak compromise.
GPU-direct testing strategy:
- All Phase 0 kernel-correctness tests (0.A, 0.B, 0.C, 0.D, 0.F):
launch tiny test-only kernels with the SAME math the Phase 2
production kernel will use; assert properties of the output.
- Test 0.E (synthetic edge discovery): stays CPU. It tests an
ALGORITHMIC PROPERTY of Thompson exploration (does it discover
edge if edge exists?), not a kernel correctness property.
- All Phase 2 unit tests (2.A-2.D): GPU-direct against the
modified production kernel.
- Phase 0.F (real checkpoint extraction): unchanged — already GPU.
Local development uses RTX 3050 GPU (per memory user_dev_environment.md).
CI runs --ignored flag to skip GPU tests on CPU-only runners.
Time budget: Phase 0 was 1-2 days (CPU mirror); now 2-3 days
(GPU-direct, includes kernel wrapper setup half-day).
Other delta:
- Phase 0 deliverable file renamed: distributional_q.rs ->
distributional_q_tests.rs (no mirror functions, just tests +
kernel wrappers).
- Phase 2 unit tests rephrased to launch production kernel rather
than compare against CPU mirror.
- L1 verification gate runtime: seconds -> minutes (GPU launch
overhead per test).
The user's intuition was right: testing production directly is the
honest approach. Mirror was an optimization that traded correctness
for speed; with local GPU available the optimization isn't needed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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25ba051157 |
spec(dqn): critical review pass — fix all majors, mediums, minors
Self-review identified 8 major + 8 medium + 15 minor issues. All fixed:
MAJORS:
M1 Test 0.A: clarified — compare argmax(E[Q]), Boltzmann(E[Q]),
and Thompson sampling distributions; assertion is on relative
ordering across all three.
M2 IQN quantile count: replaced hardcoded `5` with N_IQN_QUANTILES
constant (defined per task #147 fixed-quantile design).
M3 "Converged checkpoint" definition: ≥60 epochs trained AND
val_sharpe stabilised (no >10% change over last 10 epochs).
Cites prior 60-epoch validation runs (task #80, train-7rgqd).
M4 R3 reframed: replaced "no issue" handwave with explicit
by-design tradeoff acknowledgment + cost analysis. Wasted
exploration is the cost of finding out whether edge exists.
M5 Test 0.D σ_long=0.05 justified: chosen to match expected order
of magnitude given typical |return| ~ 50bps; Phase 0.F
validates against real checkpoint.
M6 rng_ctr post-increment: clarified — matches existing pattern
at experience_kernels.cu:858 (no behaviour change).
M7 train_active_frac instrumentation: NEW Phase 2 deliverable —
existing HEALTH_DIAG only has val_active_frac, but L3 verifies
training-time active_frac. Spec now explicitly adds this
~10-line metrics.rs change as a Phase 2 deliverable.
M8 eps_dir cleanup code-level detail: explicit reference to
experience_kernels.cu lines 814-865; remove eps_dir from both
static EPS_FLOOR clamp AND adaptive boost block; verify
variable can be removed from kernel signature via grep.
MEDIUMS:
Med1 Current C51/IQN combination: clarified that compute_expected_q
blends per training schedule; Phase 2 replaces with explicit
0.5*E_C51 + 0.5*E_IQN equal weighting; Phase 0.F verifies.
Med2 Eval mode phrasing: "eval mode already sets eps=0 in existing
kernel" — no semantic override, factually correct.
Med3 Magnitude σ claim: clarified — magnitude branch likely has σ
bias in OPPOSITE direction (Full has larger σ; UCB would
prefer Full and worsen saturation). Empirical verification
deferred. Phase 0.F should also report per-magnitude σ.
Med4 Hierarchical sampling claim corrected: it's not about
balancing 50/50 (already 50/50). It's about decoupling
cluster-best decisions; clarified.
Med5 n_atoms vs N_IQN_QUANTILES: clarified — n_atoms variable per
config (currently 51); N_IQN_QUANTILES fixed at 5.
Med6 Conviction code: removed pseudo-code; references existing
implementation at experience_kernels.cu:1091; provides
implementation hint for E[Q] reuse.
Med7 Q-target propagation: clarified — uses full distribution
(C51 atom projection / IQN quantile regression), not just
E[Q]. Thompson modifies action selection only.
Med8 References: added Thompson 1933 (original), Bellemare 2017
(C51), Dabney 2018 (IQN) for theoretical foundations.
MINORS:
Min1 Date updated to 2026-04-27.
Min2-3 Argmax monotonic /2 simplified out — argmax(a+b) =
argmax((a+b)/2). Code clarity improved.
Min4 P(argmax picks Long) = 0 deterministic; reframed assertion.
Min5 Test 0.F structural assertions added: σ_C51[FLAT] < 0.01 ×
σ_C51[LONG]; same for IQN; E[Q_FLAT] > E[Q_LONG]; argmax
picks FLAT; Thompson P(LONG)+P(SHORT) ≥ 0.20.
Min6 -INFINITY → CUDART_INF_F (CUDA convention).
Min7 dir_idx scope: comment notes it's declared earlier in kernel.
Min8 action_select args: explicit — three buffers exist on GPU
but not currently passed; new params, no new buffers.
Min9 Phase 0 time math: 5 hours tests + 1 hour enumeration + 2
hours 0.F + (3 hours runtime if checkpoint training needed,
runs in parallel). Honest budget.
Min10 "Two-stream" → "5-Layer Gate" header.
Min11 Plan 5 reference uses full path consistently.
Min12 Plan B time budget: explicit 1 day if pass; 2-5 days if bug.
Min13 active_frac: clarified Long+Short combined, not per direction.
Min14 train_active_frac: now in Phase 2 deliverables (see M7).
Min15 "20 mechanisms" → 21, with sub-counts in section headers.
Spec now 615 lines, comprehensive coverage of:
- Pearl + theoretical foundation
- Problem statement (with bias-might-be-correct caveat)
- Architecture (Thompson at training, argmax at eval)
- Train vs eval distinct selectors with behavior-change disclosure
- Direction-only scope with magnitude σ-bias warning
- Conviction stays E[Q]-based (no Kelly cap jitter)
- Interaction matrix: 21 mechanisms in 3 categories
- 6 v2 enhancements documented + deferred
- Phase 0/1/2/3 with tests, exit gates, time budgets
- 5-layer verification + train_active_frac instrumentation
- 8 risks with mitigations + 5 stop conditions
- What v1 doesn't touch (referencing interaction matrix)
- References (Thompson 1933, Bellemare 2017, Dabney 2018, etc.)
- Aggregation contract (project-wide pearl, enforced)
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
308c54484e |
spec(dqn): full interaction matrix + outside-the-box Thompson improvements
Per-user direction: every existing mechanism in the DQN system MUST be
explicitly considered for interaction with Thompson, and Thompson itself
MUST be examined for system-specific improvements beyond vanilla.
INTERACTION MATRIX (3 categories, 20 mechanisms):
Category 1 — Compose with Thompson (no change required):
Counterfactual reward, B.2 novelty bonus, PopArt, Saboteur,
Curiosity, NoisyNets/VSN, Distillation, CQL, Polyak target EMA,
HER, PER, Replay warm-start, Multi-fold validation harness.
Category 2 — Trivially adapt to Thompson (one-line changes):
D7/N7 contrarian sign flip (negate the SAMPLE), cosine epsilon
schedule (still applies to mag/ord/urg), per-sample epsilon (IQL
expectile gap), adaptive Boltzmann tau (still applies to mag/ord/urg).
Category 3 — Take precedence over Thompson (hard constraints):
Plan-based action lock (Thompson sample discarded if plan active),
per-magnitude Kelly cap, trail stop, capital floor breach.
Critical insights from the audit:
1. NoisyNets is ALREADY a form of training-time Thompson at the
parameter level. Output-space Thompson stacks on top —
total exploration = parameter-space ⊗ output-space (multiplicative).
2. Curiosity is ORTHOGONAL to Thompson — Thompson explores actions
whose Q is uncertain; curiosity explores states whose dynamics
are uncertain. Both axes desirable; no conflict.
3. Plan lock takes precedence; same as currently with Boltzmann.
OUTSIDE-THE-BOX v2 ENHANCEMENTS (deferred to follow-up specs):
v2.1 Triple-source Thompson (C51 + IQN + Ensemble) — incorporate
the existing ensemble Q-head as 3rd uncertainty source.
v2.2 Persistent Thompson (anti-churn for HFT) — bias sampling
toward current direction, ISV-driven; reduces tx_cost from
Long/Short oscillation across bars.
v2.3 CVaR-aware eval (risk-adjusted deployment) — eval picks
argmax(E[Q] − λ·CVaR_α[Q]); risk-aware decision making for
production with real capital.
v2.4 Information-Directed Sampling (Russo & Van Roy 2014) — picks
action minimizing regret²/info_gain; more efficient than
vanilla Thompson when learning saturates.
v2.5 Hierarchical Thompson on (trade vs no-trade) → (which
direction) — addresses 50/50 structural advantage of no-trade.
v2.6 Composition with curiosity-driven exploration — explicit
coupling beyond reward-side composition.
Each v2 enhancement gets its own spec/plan when prioritised. Vanilla
Thompson is v1; ships first; verified independently.
ALSO FIXED (from earlier self-review):
- Pearl claim softened: only the C51 Hold/Flat bias is directly
attributed; other historic bugs had different mechanisms.
- TFT entry removed from contract table — TFT is a Variable
Selection Network (feature processor), not a Q-head. Replaced
with generic "Future Q-head additions" placeholder.
- Eval direction = argmax E[Q] explicitly flagged as a behavior
change from current Boltzmann-with-tau (val_dir_dist will be
more concentrated than current).
- Phase 0.E budget reduced (1-2 hours, not 1 day) — synthetic
bandit is ~50 lines of Rust, not full RL training loop.
- Phase 0 enumerates existing checkpoints before training new one.
- Architecture diagram parenthesis fixed.
- Conviction implementation note: compute E[Q] once, reuse for
conviction AND eval-mode argmax — no redundant computation.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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1c63c32297 |
spec(dqn): reframe Phase 3 as direction-branch dead-code cleanup
User correctly challenged the "band-aid removal" framing. All fixes shipped during the val-Flat-collapse investigation addressed real bugs at their respective layers and should be preserved: - Kelly cap warm-branch ( |
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jgrusewski
|
5c325f8947 |
spec(dqn): pivot to Thompson sampling — distributional RL action selection
Revises the C51-bias spec after deeper review surfaced 12 design gaps,
of which 4 were critical:
1. Train-only vs train+eval ambiguity — UCB at eval would conflate
"model recommends Long" with "model is uncertain about Long",
inflating reported edge. CRITICAL for trading where eval drives
real capital decisions.
2. Thompson sampling is more principled than UCB:
- parameter-free (no κ to tune)
- uses distribution directly without scalar reduction
- naturally explore-exploit balanced via distribution shape
3. c51_alpha is the wrong blend weight (it's C51-vs-MSE-warmup, not
C51-vs-IQN). Equal-weight average of C51 and IQN samples is the
structural choice — no tuned blend weight needed.
4. The bias might be CORRECT BEHAVIOUR — model rationally choosing
Flat when no edge has been discovered. Phase 0 must include a
synthetic-edge test (controlled MDP with KNOWN positive Long
expected value) to verify Thompson can discover edge if it exists.
Other gaps fixed:
- Eval at argmax E[Q] (not Boltzmann, not Thompson)
- Pearl wording broadened to cover ensembles + future methods
- Ensemble Q-head added to aggregation contract table
- Explicit caveat: NEVER extend Thompson to magnitude branch (would
worsen existing magnitude saturation)
- Phase 0.F uses CONVERGED checkpoint (≥30 epochs), not 2-epoch run
- L4 long smoke (30 epochs, ~1 hour) added — Thompson edge discovery
needs longer feedback loop than 5 epochs
- Phase 3 explicitly removes eps-floor + tau-floor band-aids
(Thompson replaces direction Boltzmann; band-aids become dead code)
- Conviction stays E[Q]-based, not sample-based (avoid Kelly cap
jitter from stochastic samples)
Architecture (Thompson only, no UCB):
TRAINING: dir_idx = argmax(0.5 × (sample_C51(d) + sample_IQN(d)))
magnitude/order/urgency: existing Boltzmann + ε-greedy
EVAL: dir_idx = argmax(0.5 × (E[Q_C51] + E[Q_IQN]))
magnitude/order/urgency: existing Boltzmann (eval mode)
Direction-branch ε-greedy + Boltzmann are REMOVED — Thompson is the
exploration mechanism. No new GPU buffers; existing C51 atoms + IQN
quantiles passed to action_select.
5-7 days active work across 4 sub-plans; each gets its own
writing-plans cycle.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
494125c104 |
spec(dqn): distributional RL aggregation — UCB on C51+IQN σ for action selection
Designs the structural fix for the C51 expected-Q Hold/Flat bias exposed
by the Kelly val-Flat-collapse fix. The bias is a manifestation of a
deeper project-wide pearl:
"Distributional RL aggregation discards uncertainty;
action selection must restore it."
Any value head representing Q as a distribution (atoms, quantiles,
ensembles) MUST expose both E[Q] and σ(Q) to action selection. Boltzmann
on E[Q] alone produces structural bias toward low-variance actions
regardless of expected payoff — the C51+IQN Flat-attractor is one
instance of this lost-information pattern.
Fix: extract σ(Q) from BOTH C51 atoms (closed form) and IQN quantiles
(IQR/1.349), blend by loss-time weight, feed Q_eff = E[Q] + κ·σ
(κ=1.0 structural identity) to direction-branch Boltzmann ONLY.
4-phase implementation:
Phase 0 — TDD hypothesis verification (Rust mirror functions + 5 unit
tests including GPU integration on real checkpoint)
Phase 1 — Audit existing reward levers (B.2, CF, PopArt, Q-target)
via 6 unit tests; fix any bugs found
Phase 2 — UCB integration: new compute_q_with_uncertainty kernel,
modified action_select, Rust orchestration, project-wide
aggregation contract in dqn-wire-up-audit.md
Phase 3 — Verification per Plan 5 Task 5 multi-seed × multi-fold
5-layer verification gate; 8 risks with mitigations; 4 stop conditions
that halt execution and force redesign.
Existing band-aid fixes (Kelly cap, eps-floor, tau-floor) stay — they
address symptoms at different layers. UCB adds the missing aggregation
step that was the common root across all the symptoms.
Direction-branch only — magnitude/order/urgency don't have the
Flat-attractor (atom-mass collapse asymmetry).
5-7 days active work across 4 sub-plans; each gets its own
writing-plans cycle.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
f3a8a5ff62 |
spec(dqn-v2): D.8 TLOB pivot — cuBLAS port using existing DQN infra, no ONNX, no pretraining
User pearl (2026-04-24): the DQN already has every primitive TLOB needs (gpu_attention, batched_forward/backward, GpuLinear, cuBLASLt handles, cuda_autograd). Port TLOB's Q/K/V + attention as a composition of existing primitives. Random-init, trainable end-to-end from the DQN's reward signal. Uniform with Mamba2, IQL, atoms/γ/τ/ε — all of which already follow this pattern. Eliminates: - ONNX Runtime dependency (was already dead — stripped from ml-supervised) - Separate supervised pretraining pipeline - "Freeze vs fine-tune" false dichotomy - Pretrained-checkpoint-file-not-found failure mode Prerequisites for the cuBLAS-native design (all satisfied): - gpu_attention.rs exists - GpuLinear trainable layer exists - cuda_autograd over cuBLAS exists - MBP-10 data already in the DQN data pipeline What was "BLOCKED on prerequisites" in the Task 6C audit referred to the OLD pretrained-ONNX design. The cuBLAS-native design has all prerequisites satisfied — Task 6C can proceed under the new scope. Pearl captured in memory: pearl_tlob_no_pretraining.md. Generalises to any future attention/state-space/Neural ODE module: port to cuBLAS, random init, let the DQN teach it. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
cf36091eeb |
spec+plan(dqn-v2): GPU drives, CPU reads (§4.C.6 revision)
User raised that the CPU-controller pattern (where CPU computes adaptive values from ISV and writes back to ISV) violates the architectural principle: GPU kernels should compute adaptive decisions; CPU-side code is pure observation. Replaces the AdaptiveController trait with read-only AdaptiveMonitor: - No update() — CPU doesn't compute adaptive values. - No write_output() — CPU doesn't write adaptive values to ISV. - read() returns the GPU-computed value from ISV. - diagnose() emits HEALTH_DIAG snapshot. - observe() + fire_rate() track how often the GPU-computed value changes. Reclassifies the 9 adaptive mechanisms: - 6 reactive get GPU kernel + CPU monitor: atoms, gamma, kelly_cap, tau (Polyak EMA), epsilon, grad_balancer (last is already GPU-driven). - 3 static get ISV constructor-write, no monitor: cql_alpha, conviction_floor, plan_threshold. New ISV slots for GPU-written adaptive outputs (EPSILON_EFF, TAU_EFF, GAMMA_EFF, KELLY_CAP_EFF) and CPU-born inputs (EPOCH_IDX, TOTAL_EPOCHS), plus slots for the 3 static configs. Rationale: - Unified adaptive machinery, no CPU-side special cases. - Per-sample granularity available (Expected SARSA τ in c51_loss_kernel is already the exemplar — reads ISV q_gap + health per sample). - Zero CPU→GPU config transfer in any path. - ISV is single source of truth for every adaptive value. Plan 1 Tasks 8-17 restructured: - Task 8 creates AdaptiveMonitor trait (not AdaptiveController). - Tasks 9, 10, 11, 13, 14, 17: GPU kernel + monitor pairs. - Tasks 12, 15, 16: static ISV writes only. Follow-up commits will revert Batch A ( |
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jgrusewski
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fb6d0f40f9 |
audit(dqn-v2): A.5 orphan audit complete
Populated docs/dqn-wire-up-audit.md with every pub module and CUDA kernel in the DQN path. Each entry classified Wired / Partial / Orphan / Ghost / OUT-of-DQN-scope with the action plan linking to the plan+task that resolves any non-Wired status. No Orphan left unclassified. Orphans fall into three buckets: 1. Scheduled for wiring by a later Plan (gpu_statistics → Plan 2 D.2; tlob_loader → Plan 2 D.8). 2. OUT-of-DQN-scope because supervised consumers exist (PPO kernels, xLSTM, KAN trainable adapter, flash_attention, benchmarks). 3. Genuinely unused — escalated to user review in the task output, not deleted autonomously (streaming_dbn_loader, unified_data_loader, training/orchestrator, training_pipeline, inference_validator, model_loader_integration, paper_trading/mod.rs, portfolio_transformer, regime_detection/mod.rs). Summary: 109 total modules/kernels, 74 wired, 7 partial, 11 orphan, 0 ghost, 17 OUT-of-DQN-scope. Plan 1 Task 6. Spec §4.A.5, Invariant 2. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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1353e71ae6 |
spec+plan(dqn-v2): layout fingerprint replaces schema version (§4.A.2)
User raised a backward-compat concern: integer "schema version" semantically implies a family of coexisting versions with upgrade paths between them, inviting the forbidden pattern (feedback_no_legacy_aliases.md, feedback_no_partial_refactor.md). Replace with a compile-time structural fingerprint: - ISV[0..2) stores a u64 FNV-1a hash of the slot layout (split across two f32 lanes preserving raw bits). - The hash is computed by a const fn over the slot list; any slot change automatically updates the fingerprint. No human decides "what version is this now". - Checkpoint load is fail-fast only. Error message does NOT mention migration as an option. - Pre-commit hook rejects any `fn migrate_isv|upgrade_isv` to make the no-migration rule structurally enforced (landed as part of Plan 1 Task 5 hook extension). - StateResetRegistry entry renamed ISV_SCHEMA_VERSION → ISV_LAYOUT_FINGERPRINT (Task 2's landed code touched in Task 5's commit per no-partial-refactor). Updated: - spec §4.A.2 (fingerprint design + rationale) - spec §5 landing-order note (fingerprint auto-updates on layout change) - Plan 1 architecture line - Plan 1 Task 2 StateResetRegistry test + entry naming - Plan 1 Task 5 — full rewrite of implementation steps - Plan 1 exit criteria #6 - Plan 2 pre-plan verification (grep fingerprint constants, not version==0) - Plan 2 Task 6D.1 (update fingerprint seed, not bump version) - Plan 2 Task 6D exit criteria #7 - Plan 3 pre-plan verification - docs/isv-slots.md ISV[0..2) row No code changed. Plan 1 Task 5 is not yet implemented — this commit realigns the spec + plans so the implementer subagent works from the corrected design. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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336ee40b9d |
spec(dqn): add Invariant 9 — no deferred work, no stubs, concrete E decisions
User-mandated addition: Invariant 9 — No deferred work, no stubs, no TODO/FIXME Every commit lands complete. No stubs (placeholder return values), no TODO/FIXME/XXX/HACK/TBD markers, no half-finished implementations. If it can't finish in this commit, it doesn't start. Stubs and deferrals train the network on semantic emptiness — invisible in convergence metrics but burn GPU time optimising against partly-fake signal. Authority: feedback_no_stubs.md, feedback_no_todo_fixme.md, feedback_no_quickfixes.md elevated to first-class spec enforcement. Pre-commit hook greps for forbidden markers. Part E decisions made concrete (per Invariant 9): - E.1 TFT VSN: IN (full VSN extension) - E.2 GRN: IN if A.5 audit finds absent (otherwise mark existing as canonical) - E.3 Multi-quantile heads: IN (5/25/50/75/95 quantile decomposition) - E.4 Encoder-decoder: IN (extends D.3 to full trunk/head separation) - E.5 Interpretability: IN (attention-weight ISV diagnostics) - E.6 Auxiliary heads: IN (next-return + regime-classification) - xLSTM: OUT (redundant with Mamba2+TLOB; YAGNI) - KAN: OUT (function approx not a bottleneck) No "evaluate later" items. Every concept either lands or explicitly doesn't, with rationale. §8 decisions tightened: - D.8 TLOB mode: decision criterion = per-step cost benchmark ≤ 5ms - C.6 controller order: fixed in spec (atoms→gamma→Kelly→cql_alpha→ tau→epsilon→conviction_floor→plan_threshold→balancer) - A.3 resource plan: auto-switch L40S→H100 at 12 GPU-hour threshold - A.4 metric band init: [mean − 3σ, mean + 3σ] from last 3 good runs No discretionary "we'll see" remain. All decisions data/order/budget/ statistic-driven. Counter updates: "seven invariants" → "nine" in §3 header and §5 cleanup contract. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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3a62e23056 |
spec(dqn): review fixes — clarify hot-path scope, add Invariant 8 named dims
Self-review pass on the DQN v2 unified design spec (
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jgrusewski
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d13b535862 |
spec(dqn): DQN v2 unified integrated policy system design
Design spec consolidating every hard lesson from the val-Flat-collapse investigation, the task #92 gradient-pathology triad, the ISV v-range unification work, the f64→f32 ABI cleanup, and many sessions of fold-1 grad explosions and orphan-feature accumulation. Seven non-negotiable invariants: 1. ISV-driven adaptive bounds (no tuned constants) 2. Wire-It-Up (no orphans) 3. GPU-only hot path (zero memcpy DtoH; pinned-zero-copy only) 4. Pinned-only host memory 5. Diagnostic-first 6. Convergence discipline (primary guardrail, multi-seed × multi-fold) 7. Wire-up + diagnostic audit per-commit Five Parts, landing as one coordinated rollout: A. Foundation — reset registry, ISV contract, multi-seed harness, regression detection, orphan audit, hot-path purity audit. B. Flat-trap escape — ISV-driven reward shaping, trade-attempt bonus, replay warm-start via scripted-policy portfolio, adaptive plan threshold. C. Refinement — quantile atom support, reward attribution, state-dist divergence signal, temporal timing bonus, CQL-seed coupling, adaptive controller unification refactor. D. Temporal architecture — Mamba2 backward completion, per-branch gamma, horizon-decomposed value function, generalised temporal reward, soft fold transitions, plan temporal dynamics, liquid audit, TLOB-DQN integration. E. Supervised→DQN concept audit — TFT VSN, GRN, multi-quantile, xLSTM, KAN, encoder-decoder, interpretability, auxiliary heads. Tiered success criteria: Tier 1 — convergence discipline (must pass first). Tier 2 — behavioural parity (val trades escape Flat-trap). Tier 3 — profitability (val Sharpe > 1.0 on window). ISV slots grow from 37 to 72. Five new audit docs tracked by pre-commit hook. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
9deda5f65b |
feat(dqn): ISV-unified per-branch Q-support range (spec 2026-04-23)
Unifies the Q-support range source across atom grid / warm-start quantile
clamp consumers via the ISV signal bus. One broadcast written at epoch
boundary from per-branch Q-stats EMAs, read by the two consumers that
previously held disagreeing ranges. Target observation: atom utilisation
≥40% (up from 11-15% on train-fpxnw).
Phase 0 — per-branch Q-stats kernel Rust plumbing:
* Load q_stats_per_branch_reduce alongside legacy q_stats_reduce
* Add per_branch_q_stats_pinned (28 f32 = 4 × 7, device-mapped)
* PerBranchQValueStats struct: [QValueStatsResult; 4]
* reduce_current_q_stats_per_branch launches the new kernel with the
four branch (off, size) pairs derived from config.branch_N_size
Phase 1 — ISV v-range plumbing (zero behavioural change at epoch 1):
* ISV_NETWORK_DIM=23 preserved for w_isv_fc1 sizing; ISV_TOTAL_DIM=31
allocates 8 additional slots for per-branch (centre, half-width)
* Slot constants V_CENTER_DIR..V_HALF_URG covering slots 23..30
* eval_q_mean_ema / eval_q_std_ema / eval_ema_initialized promoted
to [f32; 4] / [bool; 4]; scalar setters preserved for trajectory
backtracking (broadcast same value to all branches)
* Bootstrap at construction: centre=0, half=(v_max-v_min)/2 → the
byte-identical [config.v_min, config.v_max] span per branch before
any Q observations arrive
* reset_eval_v_range_state resets the 4 per-branch EMAs AND the 8 ISV
slots to bootstrap values; legacy eval_v_range_pinned[2] still reset
(deferred removal — spec Phase 3)
* update_eval_v_range reworked: signature takes PerBranchQValueStats and
per_branch_q_gaps. Maintains 4 independent adaptive-rate EMAs,
computes (centre, half) per branch with min_half_floor=0.1×(v_max-v_min)
and clamps to config bounds, writes 8 ISV slots. Branch-0 (direction)
centre±half is also mirrored into the legacy eval_v_range_pinned for
consumers that have not yet migrated to the per-branch bus.
Phase 2a/2b — atom grid per-branch v-range:
* adaptive_atom_positions kernel signature changed from
(v_min: float, v_max: float) to (branch_idx: int, isv_signals: float*);
reads centre/half from ISV slots 23+2·b, 24+2·b. Eliminates the f64→f32
ABI trap (spec Phase 2 side-effect) since the only per-branch range
path is now pointer-based.
* recompute_atom_positions passes branch_idx + isv_signals_dev_ptr per
branch; no scalar v_min/v_max arg remains.
Phase 2c — warm-start quantile clamp per-branch from ISV:
* warm_start_atom_positions reads per-branch (centre, half) from pinned
ISV host memory, clamps shared reward-quantile vector into each
branch's adaptive range before tiling into atom_positions_buf.
Bootstrap makes this equivalent to the pre-spec config.v_{min,max}
clamp until the first Q observation lands.
Deviations from spec:
* Phase 2d (per_sample_support_buf → [N, 4, 3]) NOT implemented. The
spec's premise was that per_sample_support is host-tiled from
eval_v_range, but the active path in this codebase has it filled by
iql_compute_per_sample_support (V(s)-centered, per-sample, already
adaptive) — orthogonal to the ISV bus. Migrating that kernel to
per-branch output would require rewriting iql_value_kernel +
iql_support_floor + C51/MSE loss kernel indexing in lockstep, which
the "no unrelated refactoring" constraint disallows. The loss-kernel
Bellman projection today uses V-centered bounds that are themselves
adaptive; the ISV v-range fix still lands the primary win (atom grid
+ warm-start agreement) without touching IQL.
Compile verified: cargo check -p ml + --workspace pass (SQLX_OFFLINE,
CARGO_INCREMENTAL=0, sccache). No TODO/FIXME/XXX introduced.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
baa4151fab |
docs(isv): signal quality audit for DQN ISV bus — L40S oscillation diagnosis
Diagnostic-only audit of all 22 ISV slots using train-mdh86 HEALTH_DIAG (20 epochs, same oscillation pattern as train-rq6n8). Identifies three dead writers (slots 2, 3, 4), one strongly anti-correlated signal (slot 12 learning_health, r=-0.765 vs Sharpe), sample-0 bias in four regime slots (8-11), and EMA-pollution risk on the Q-scale EMAs (16, 21) that feed Kelly conviction + C51 bin weighting. Produces a per-slot table with writer/reader citations and an ordered fix list. Hypothesises the observed +34 → -67 Sharpe swing is driven by health mis-reporting "improving" while policy diverges, q_dir_abs_ref contamination collapsing Kelly, and the zero-writer TD-error EMA disabling micro-reward regime awareness. No code changes — reconnaissance only. |
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jgrusewski
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746b8b6754 |
docs(tlob): feature-diff doc for TLOB 51 vs OFI 20 — Phase A
Compares the 51-feature TLOBFeatureExtractor (crates/ml-supervised/src/tlob/ features.rs + mbp10_feature_extractor.rs) against Foxhunt's 20-slot OFI vector persisted in .fxcache. Findings: of TLOB's 51 slots, 23 are placeholders (sine waves / hardcoded constants), 12 duplicate existing OFI or 42-dim market features, and only ~10 carry genuinely novel information. Meanwhile the existing MicrostructureState struct in ml-features already computes 10 features (realized variance, Hawkes intensity, weighted book pressure, spread dynamics, aggression ratio, queue-depletion asymmetry, order-count flux, intra-bar momentum, regime score, OFI trajectory) that are dropped before reaching fxcache — these dominate the TLOB novel candidates and require zero new math. Phase B plan (drafted inline): bump OFI_DIM 20→28, bump FXCACHE_VERSION 4→5, prioritize persisting the 10 Foxhunt-internal MicrostructureState features before any TLOB-derived additions, sweep all hard-coded 18/20 constants across CUDA kernels + gpu_dqn_trainer.rs, and rely on the ensure-fxcache Argo step for cache regeneration. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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b8cd4e1d94 |
diag+docs(dqn): trunk-slice grad decomposition + stale IQN-trunk doc fix
Extends grad_decomp_kernel to snapshot the trunk tensor slice (tensors
0..4 = w_s1, b_s1, w_s2, b_s2) in addition to the existing direction +
magnitude branch slices (8..12 / 12..16). Adds a new HEALTH_DIAG group:
grad_trunk [iqn=<abs> ens=<abs> c51=<abs> cql=<abs> distill=<abs>
rec=<abs> pred=<abs> cql_sx=<abs> c51_bs=<abs>]
Prior grad_split_bwd / grad_split_aux groups report mag_norm / dir_norm
ratios per loss component, computed over branch-head tensors only. That
measurement range structurally reports 0.0000 for any loss component
that writes exclusively to the trunk — IQN and Ens in particular. This
caused the persistent misdiagnosis that IQN-to-trunk was not wired; the
prior scoping in /tmp/foxhunt_research/iqn-to-trunk-wiring-scoping.md
confirmed the wiring is live (apply_iqn_trunk_gradient at
gpu_dqn_trainer.rs:4882) and that the zero reading was a blind spot in
the measurement pipeline.
Smoke confirms the diagnostic: after iqn_readiness ramps up (late
epochs), grad_trunk reports iqn=100..381 (real trunk SAXPY amplitude),
ens=0.07..3.57, c51=2.46..8.91 (value-head dueling path contributes
through trunk), while cql/cql_sx/distill/rec/pred stay near-zero — a
clean diagnostic baseline.
Also fixes stale documentation at dual-distributional-c51-iqn-design.md
that claimed "IQN trains in isolation — its gradients don't flow back
to the shared trunk": reworded to reflect current wired state with
file:function citation and explicit iqn_readiness gating note. Updated
the "What Changes" table ("IQN training") and "Implementation Order"
(Phase 1 marked DONE) with the same citation.
Changes:
- grad_decomp_kernel.cu: per-component result slot 2 → 3 floats
(mag_norm, dir_norm, trunk_norm); extra __shared__ sum_trunk +
tree reduction; new grad_trunk_start/trunk_len kernel args.
- gpu_dqn_trainer.rs: pinned result buffer 18 → 27 floats; snapshot
now does two copy_f32 passes (trunk → dst[0..trunk_len), branch →
dst[trunk_len..]); per-component slot offsets 0/2/… → 0/3/…;
grad_component_norms_trunk cached field + accessor; compute trunk
range from padded_byte_offset(¶m_sizes, 0..4).
- fused_training.rs: grad_trunk_norms_by_component() + per-component
grad_trunk_*_abs() accessors.
- training_loop.rs: HEALTH_DIAG emits new grad_trunk group ordered
[iqn ens c51 cql distill rec pred cql_sx c51_bs]; extended doc
comment explaining the three groups' roles.
- design spec (Problem #1 + What Changes row + Implementation Order):
stale "IQN trains in isolation" replaced by current wired-state
description, cites gpu_dqn_trainer.rs:4882 and readiness ramp at
gpu_dqn_trainer.rs:4228-4243.
Pure diagnostic — no training dynamics change, no atomicAdd, no tuning
knobs, no TF32 changes. Smokes unaffected (magnitude_distribution H10
regression pre-exists on HEAD 810b3c570; 4 other smokes pass).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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a9a51e8fa0 |
cleanup+fix(reward): Task 2.4 R6 relocation + Task 2.5 Bug #6 docstring
Task 2.4: Relocates negative-tail compression from R6 reward-layer
(asymmetric_soft_clamp at experience_kernels.cu:78-81) to C51 Bellman
target smoothing (c51_loss_kernel.cu::block_bellman_project_f).
Functionality preserved — same invariant, better location. Upper +10
cap kept inline as fminf(reward, 10.0f) for numerical safety.
Deletions (reward layer — R6 no longer shapes the reward itself):
- asymmetric_soft_clamp() from experience_kernels.cu:78-81 (no callers)
- Reward-layer clamp replaced with fminf(reward, 10.0f) at ~1922
(segment_complete) + ~3049 (hindsight_relabel opt_reward)
- la slot from reward_contrib_fractions (was slot 4; tuple shrinks 5→4)
- loss_aversion_per_sample buffer from GpuExperienceCollector
(field + alloc + kernel arg + dtoh + memset, all removed)
- la={:.3} field from HEALTH_DIAG reward_contrib format string
- loss_aversion assertion from reward_component_audit smoke test
- loss_aversion comment reference in raw_returns comment block
Additions (gradient layer — R6 invariant moves here):
- Huber-style `if t_z < 0 { t_z = -10*(1-exp(t_z/10)); }` in
c51_loss_kernel.cu::block_bellman_project_f BEFORE v_min/v_max clamp
- Inline kernel comment documenting the relocation rationale
- Track 2 triage doc updated: R6 verdict DELETE → DELETED / RELOCATED
with landed-relocation notes (both call sites + C51 Bellman edit)
Task 2.5 Bug #6: Stale `patience_mult` docstring at
experience_kernels.cu:1144 referenced the defunct R7 V8 reward (deleted
in Task 0.8). Rewrote the reward-shape docstring to reflect current
post-V7 / Task 0.8 reality (sparse = 2.0 * vol_normalized_return, capped
inline) and notes the R6 relocation. Per feedback_trust_code_not_docs.md.
Per feedback_no_functionality_removal.md: R6's invariant is RELOCATED,
not deleted. The negative-tail compression — which protects against
catastrophic-loss-gradient dominance in the Q update — is now at the
Bellman target smoothing step where the invariant structurally belongs
(reward-inventory §"wrong-level regularization" pattern).
Tolerance band validation (smoke suite at this commit):
magnitude_distribution: F_Half=0.150 F_Full=0.237 (≥0.05 floor ✓)
(H10 eval_dist assertion fails pre-existing at HEAD 90e1e3dbb; not
introduced by this change — verified by running at HEAD before stash
pop, same [EVAL_DIST] 1.000/0.000/0.000 collapse.)
reward_component_audit: cf_flip=0.584 trail=0.304 (cf_flip≥0.1 ✓, PASS)
controller_activity: [CTRL_FIRE] anti_lr=0.000 tau=0.000 gamma=0.000
clip=0.400 cql=0.000 cost=0.000 (PASS)
exploration_coverage: entropy @ep5=0.988 @ep20=0.985 (PASS)
multi_fold_convergence: Best Sharpe 81.54/38.82/84.18 (≥20 floor ✓)
best_val_metric 0.043/0.024/0.049 (baseline was 0.028/0.018/0.019 at
policy-quality-baseline — 26 intervening commits of bug fixes from
Task 2.5 bugs #1–#7 would account for persistent drift; within
run-to-run variance of HEAD-pre-change)
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jgrusewski
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f9286e938d |
docs(policy-quality): Task 2.X scoping — per-bin variance + Q-spread regularization
Scoping output from the Full=0% investigation (post-Task-2.2 smoke showed
eval_dist [eq=0.580 eh=0.420 ef=0.000] — Quarter+Half recovered,
Full still 0%).
Root cause: Q-estimation bias (category C) — C51 expected-Q over atoms
systematically under-prices higher-variance bins. Full has 4x Quarter's
PnL variance per experience_kernels.cu risk scaling. Bias is already
documented at experience_kernels.cu:904 (commented as "structural
distributional bias — tight return distributions (Small) get higher
expected Q under the C51 softmax regardless of actual expected returns").
Not noise-starvation: Full picked on 32% of training samples across
60 epochs × 3 folds = ~61k Full samples. Ample data.
Proposed Task 2.X = Rank 1 + Rank 2 combined (~90 LOC):
Rank 1: per-bin variance weighting in C51 branch loss — amplify
Full's gradient by sqrt(var_f / mean_var)
Rank 2: Q-spread regularization — lambda * ReLU(target_spread -
Q_spread)^2 penalty preventing degenerate fixpoint
Alternative fixes ranked but not recommended:
Rank 3 per-magnitude reward shaping (~80 LOC) — only if data-disfavor
is confirmed at L40S
Rank 4 magnitude curriculum (~20 LOC) — rejected (noise-starvation ruled out)
Rank 5 state-vector enrichment (~150 LOC) — premature
Prerequisite: ~40 LOC of per-magnitude win-rate + realized-variance
instrumentation to confirm category C vs A at L40S scale.
L40S gating recommendation was:
ef >= 0.15 at L40S → Task 2.X not needed (smoke artefact)
0.05 <= ef < 0.15 → Task 2.X scoped but optional
ef < 0.05 → Task 2.X required
Per feedback_fix_aggressively.md (landed this session): we ship Task 2.X
ahead of L40S since the bias is already code-documented, the fix is
well-scoped, and the 90-LOC cost is reasonable. L40S validation (Task
2.8) will verify the fixed state end-to-end instead of the un-fixed one.
No deletions anywhere per feedback_no_functionality_removal.md.
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jgrusewski
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7b74290dd0 |
docs(dqn): R5 micro-reward — documented intentional disable (Phase 2 Task 2.3)
Per feedback_no_functionality_removal.md: R5 was originally scoped as
DELETE in the Phase 2 plan and the Track 2 triage because
reward_contrib[3] = 0.000 across 60 / 60 smoke epochs and
dqn-smoketest.toml sets micro_reward_scale = 0.0. Re-examination during
Phase 2 Task 2.3 rejected the DELETE path:
- dqn-production.toml already sets micro_reward_scale = 0.1, so R5 is
load-bearing in production, not dead code. The 0.0 value in smoke is
deliberate test-isolation (td_propagation / magnitude_distribution /
reward_component_audit all want the sparse-reward TD path isolated).
- The state-vector OFI block at state[SL_OFI_START..SL_OFI_START+SL_OFI_DIM)
= [42..62) provides representation features for the encoder (policy
side). R5 is a per-bar reward gradient on the critic (critic side).
Different mechanisms — production deploys both together.
- R5 also reads PREV_MID (retrospective hold quality), which is NOT in
the state vector. That signal exists only in the kernel branch.
Changes — pure documentation, no behavior change:
- experience_kernels.cu: ~30-line comment block at the R5 wiring site
(~L1915) documenting the parameter-not-flag status, production vs
smoke values, why state-vector OFI is complementary not redundant,
and the feedback_no_functionality_removal.md seal.
- experience_kernels.cu: fix stale kernel-signature comment that claimed
OFI was at state[66..74). Correct range is [42..62) per state_layout.cuh.
- config.rs: extend DQNHyperparameters::micro_reward_scale docstring and
add a comment at the Default impl pointing back at the kernel site.
- gpu_experience_collector.rs: extend reward_contrib_fractions docstring
to mark the micro=0.000 slot as a SEMANTIC value when the loaded profile
has micro_reward_scale=0.0, not a wiring regression.
- track2-triage.md: R5 verdict changed from DELETE to FIX-documented-disable
with the rationale above; "Proposed Phase 2 changes" section 1 and
"Next Track 2 steps" updated accordingly.
Smoke tests: 3 / 4 pass (reward_component_audit, controller_activity,
exploration_coverage). magnitude_distribution is failing on baseline
HEAD
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jgrusewski
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0611d32b06 |
docs(policy-quality): Track 3 controllers audit (Phase 1) — 0 load-bearing, 6 diagnostic, 1 candidate-for-delete
V7 audit of the 7 adaptive controllers named in spec §5.3 against the
baseline controller_activity smoke run (3 folds × 20 epochs = 60 epochs,
intervention-based fire detection per commit
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jgrusewski
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207fce8778 |
docs(policy-quality): Track 2 reward audit (Phase 1) — 2 DELETE, 3 KEEP
V7 audit of reward terms R1-R8 against the baseline smoke run
(magnitude_distribution.rs, 3 folds x 20 epochs = 60 HEALTH_DIAG rows):
* R1 step_return: KEEP (base reward, denominator)
* R2 PopArt drift: PENDING (warmup-gated zero at smoke; re-check at L40S)
* R3 CF-flip: KEEP (49-74% contribution, dominates shaping)
* R4 trail_r: KEEP-WITH-CAVEAT (fold-3 dominance, trade-volume gated)
* R5 micro-reward: DELETE (micro_reward_scale=0 in smoke, intended)
* R6 loss-aversion: DELETE (sub-1% in 54/60 epochs, relocate protection)
* R7 segment-patience: ALREADY-REMOVED (stub deleted
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jgrusewski
|
5c70c68a15 |
docs(policy-quality): Track 1 magnitude triage (Phase 1) — H4 + H10 CONFIRMED
Preliminary triage of spec §5.1 hypotheses H1–H10 using the Phase 0 baseline capture on RTX 3050 Ti. L40S validation pending per plan. Verdicts: H1 PENDING (needs forced-exploration instrumentation not yet wired) H2 REJECTED var_scale=0.96 across 19/20 epochs; Var[Q] inactive at smoke scale H3 INCONCLUSIVE kelly degenerate (insufficient win/loss counts at smoke scale) H4 CONFIRMED grad_ratio_mag_dir=0.0000 across 20/20 epochs (threshold <0.1) H5 REJECTED ent_mag stays ≥0.98 throughout; no bootstrap collapse H6 REJECTED Full fire rate (0) is lower than Quarter fire rate, not higher H7 REJECTED vsn and sigma symmetric between mag and dir branches H8 REJECTED target-net drift equal (mag=dir=0.001) H9 PENDING (same instrumentation gap as H1) H10 CONFIRMED training ent_mag=0.98, eval F_Quarter=100% Synthesis: H4 is the root cause. Magnitude branch receives ~0 gradient → weights stay near init → three magnitude Q-values near-identical → argmax picks bin 0 (Quarter) on ties → H10 manifests at eval time. H2, H5, H7, H8 all ruled out as contributors. Proposed Phase 2 priority: fix H4 (gradient-flow path into magnitude head — likely per-component advantage weighting or direction-conditioning of w_b1fc) + H10 (Q-margin argmax + stochastic eval rollouts as safety net). Phase 1 next: validate preliminary verdicts on L40S, instrument per-component gradient decomposition for magnitude, proceed with Tracks 2/3/4 in parallel. |
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jgrusewski
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8ab368de58 |
docs(policy-quality): Task 0.17 baseline metrics — captured (Phase 0 complete)
All <PENDING> markers replaced with real values from a clean 20-epoch
magnitude_distribution smoke run + 3-fold multi_fold_convergence run
on RTX 3050 Ti at HEAD
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jgrusewski
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e1ac2c7578 |
docs(policy-quality): Task 0.17 baseline metrics scaffold (PENDING GPU run)
Phase 0 instrumentation is complete (Tasks 0.4 / 0.5 / 0.8 / 0.10 / 0.16
shipped this session; 0.6 partial; 0.3 partial). This scaffold encodes
the full metric set with explicit <PENDING …> markers per row so the
GPU-run capture can fill in values without forgetting any HEALTH_DIAG
field.
The policy-quality-baseline tag is intentionally NOT applied yet —
tagging requires real captured values, not the scaffold. Runner workflow:
for t in magnitude_distribution reward_component_audit \\
controller_activity exploration_coverage multi_fold_convergence; do
SQLX_OFFLINE=true CUBLAS_WORKSPACE_CONFIG=:4096:8 \\
FOXHUNT_TEST_DATA=test_data/futures-baseline \\
cargo test -p ml --release --lib -- \$t --ignored --nocapture 2>&1 | tee out_\$t.log
done
Then edit this doc, replace markers with values, amend, and tag.
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jgrusewski
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944fecd913 |
docs(policy-quality): main-branch workflow — cut feat/wip branches
User preference: everything lands on main, ~500 LOC is small enough
that feature-branch isolation isn't needed.
Spec changes:
* §2.3 outcome paths: drop "unmerged branch" vocabulary
* §3 architecture: main-branch timeline, no feat/, no wip/
* §4 Phase 0: commits on main, tag policy-quality-baseline at end
* §5 Phase 1: experiments are worktree edits reverted after, only
triage docs commit (to main)
* §6 Phase 2: commits land on main, author chooses granularity,
each leaves smokes green
* §7.3 outcome handling: iterate on main, baseline tag for rollback
* §7.4 closing: tag policy-quality-v1, update status
Plan changes:
* Task 0.1: verify-starting-state (not branch creation)
* Task 1.x: temp-edit-then-revert (not wip branches)
* Task 1.5: triage docs commit directly to main
* Phase 4: tag + close (not merge + cleanup)
* Rollback: one git reset --hard command
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jgrusewski
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ad677466c8 |
design(policy-quality): revision 4 — functional gaps
Addressed functional concerns from third review:
1. Surrogate-noise gate was statistically weak — "random Sharpe ≤ 0.5×
trained Sharpe on same val data" could be satisfied by sample
noise. Replaced with percentile-based test:
* Pool all 6 val folds (~600 trades) for statistical power.
* Run N=30 surrogate random-action rollouts (matched marginal
action distribution — so difference is state-action mapping,
not action frequency).
* Assert trained pooled Sharpe > 95th-percentile of surrogate
distribution. Explicit false-positive control.
2. Escalation path ("scope exceeds sub-project A v2") was hand-wavy.
Now concrete: failure after 3 iterations → paused, not closed →
triage spec opened with evidence summary + diagnosis (policy/
reward/state/architecture/data bottleneck) + explicit decision
(continue on feat/policy-quality or fork feat/policy-quality-v2).
Triage itself is a full brainstorming cycle.
3. HEALTH_DIAG §4.2 Track 1 fields only covered H1–H5 — H6–H10
needed their own detection signals. Added:
- trail_fire_rate_{quarter,half,full} (H6)
- hold_time_at_exit_{quarter,half,full} (H6)
- vsn_mask_{magnitude,direction} (H7)
- noisy_sigma_{mag_head,dir_head} (H7)
- target_drift_{mag_head,dir_head} (H8)
- action_dist_eval_{quarter,half,full} (H10)
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jgrusewski
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b5b70fdd8e |
design(policy-quality): revision 3 — internal consistency pass
Fixed inconsistencies between sections after the rev-2 feature-branch
refactor (sections had gotten out of sync):
1. §2 (success criteria) rewritten — was stale from rev 1. Now has
clean mandatory/soft split matching §7.2, corrects the trade-count
gate (per-fold not averaged), references the surrogate-noise gate.
New §2.3 enumerates the outcome paths.
2. §4 and §6 no longer say "commit on main" — both land on
feat/policy-quality per the feature-branch architecture. Phase 4
merges to main at §7.4.
3. §7.3 outcome handling rewritten — was written for single-branch
model ("Phase 2 commit is NOT reverted"). New wording matches
feature-branch semantics: failing mandatory = don't merge; failing
soft = merge + follow-up.
4. §6 smoke-tests rule now lists all six Phase 0 smokes, not just
Track 1/2.
5. §7.4 Phase-4 merge-strategy added — --no-ff merge commit, tag
policy-quality-v1, cleanup sequence documented.
6. §5.5 conflict-resolution added — tracks can reach contradictory
conclusions (e.g. T1 says fix, T2 says delete). Resolution rules:
mandatory-gate dominance, simplification wins, escalation.
7. §4.3 baseline metrics now committed to the feature branch (not
"investigation-only"), matching the crash-safety discipline.
8. Budget / risk register aligned — 5.5–6.5 hrs plan + 1 buffer,
hard-capped at 3 Phase-3 validation runs.
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jgrusewski
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5552517f61 |
design(policy-quality): caveat Criterion A for H9 (delete-magnitude-branch) case
If H9 confirms and Phase 2 removes the magnitude branch, F_Half/F_Full gates become vacuous. Substitute: direction bin distribution healthy (F_Short + F_Long each >= 20% of non-Flat actions). Preserves the 'model actually trades' intent of Criterion A. |
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jgrusewski
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26be2b8abe |
design(policy-quality): revision 2 — full 4-track V7 + outside-the-box
Addresses critique from second self-review: 1. SCOPE GAP fixed: spec now covers all 4 V7 categories the user asked for. Added Track 3 (controllers) and Track 4 (exploration) with their own smoke tests (controller_activity, exploration_coverage) and HEALTH_DIAG fields. Previous version only had action-space + reward. 2. H1-H5 expanded to H1-H10 with 5 new codebase-specific hypotheses: - H6: Regime-adaptive trailing stop closing Full-positions early - H7: VSN / NoisyNets masking magnitude features - H8: Target tau too slow for magnitude propagation - H9: Data genuinely favors Quarter (no bug, delete magnitude branch) - H10: Entropy regularization + argmax tie ordering bias 3. H3 REFRAMED from "Kelly bug" to design critique — Kelly as hard multiplier vs soft gate. Fix sketch updated to offer replacement. 4. Feature branch + tag strategy replaces "single branch" containment. feat/policy-quality isolates work from main until Phase 4 merge via PR. git tag policy-quality-baseline gives named rollback anchor. 5. SURROGATE-NOISE CHECK added as MANDATORY validation gate — catches look-ahead/leakage bugs that would otherwise produce false-positive edges. Permanent smoke artifact. 6. Validation gate restructured: B + surrogate-noise MANDATORY; A criteria soft; controller-activity soft. Partial success path explicit per-gate. 7. Risk register expanded — added scenarios for B-mandatory failure (feat branch stays unmerged), all-10-hypotheses-REJECTED (fall back to H9 delete-branch), surrogate-noise failure (halt spec, open diagnostic spec), multi-load-bearing-controllers (tech debt doc). 8. Budget corrected to 5.5-6.5 hrs L40S (was underestimated 3.5-4), with per-track breakdown. Spec now: 464 lines, 10 sections, covers 4 V7 tracks × ~10 hypotheses + 8 reward terms + 7 controllers + 5 exploration mechanisms = ~30 audit items total. |