Lesson:research autopilot 20260720t090001z

신뢰도 중간 마지막 수정: 2026-07-20T09:47:04.206465Z

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제목	Research findings 20260720T090001Z: 2 negative/inconclusive, 0 mixed, 2 positive
궁금했던 점	What did the validated experiments or analyses establish, including useful negative results and the conditions under which they apply?
해본 것	- alpha-factory [scientific outcome=positive]: In a gated candidate-selection pipeline for portfolio ensembles, an upstream eligibility relaxation and a downstream diversity-aware selection preference are JOINTLY necessary and individually inert for escaping single-lineage collapse. A 2x2 factorial on a real ETF macro pool showed that admitting a decorrelated candidate that fails standalone must-beat-baselines gates (eligibility on) and adding a selection-stage diversification bonus (lambda=0.5) each leave the ensemble at 1 strategy / 1 lineage in isolation; only their combination yields a 2-lineage ensemble, improving validation max-drawdown from -4.07% to -2.55% while validation Sharpe rose 1.31 to 1.63 (pre-registered rule required o…; validation: tests/test_ensemble_diversifier_track.py — 5 passed (default-off pool invariance; admission of safety-clean decorrelated failer; refusal of a more-profitable equally-decorrelated candidate that tripped strict_survival; correlation-based refusal at 0.54 > 0.5; max_admit cap); Full suite: 366 passed, 2 failed in 120.84s; both failures confirmed PRE-EXISTING by stashing the change and re-running (tests/test_mandate_comparison.py requires the absent data/etf_macro_daily_v1 dataset); experiments/div…; next: Run the lambda dose-response under eligibility=on (lambda in {0.1, 0.25, 0.5, 0.75, 1.0}) to establish whether the 2-lineage effect is a plateau or a knife-edge at 0.5, and relax the eligibility floor/correlation ceiling to seek a third decorrelated lineage so production's max_strategy_allocation=0.4 / min_ensemble_size=3 becomes feasible. Then freeze the admission rule on validation and spend the single sealed hidden-test evaluation.; commit 2da70a23ca89185cdbd3f40794c29c6dbc8e03c9; PR https://github.com/mrcha033/alpha-factory/pull/1 - complex-nn-signal [scientific outcome=negative]: When a single-tone classification task is made continuous-frequency (ω drawn uniformly within each class cell, so no fixed correlator set is matched), a learned unit-modulus complex-recurrence correlator bank does NOT beat a parameter-free fixed-grid scan at matched inference budget — it loses at every budget (B=4/8/16) and both SNRs (−5/0 dB), winning 0/5 seeds. A 2×2 placement×combiner decomposition localizes the loss to the combiner, not correlator placement: the learned-θ bank ties the fixed-grid arm with a trained linear head (learned placement buys nothing, and the learned phases converge onto the uniform grid, 0.015Δ from bin centres at B=K), while the trained linear head loses 3.5 p…; validation: pytest tests/ -q → 248 passed, 1 warning, 3.11s (19 new tests in tests/test_offgrid_benchmark.py); python -m src.offgrid_benchmark → results_offgrid.json written, 94.1s CPU, 5 seeds × {jitter 0,1} × {−5,0} dB; Invariant test: generate_offgrid_doppler_dataset(jitter=0) reproduces generate_doppler_dataset bit-for-bit (torch.equal on X and y); Invariant test: scan_predict(x, K, n_per_cell=1) equals matched_filter_predict(x, K) exactly (torch.equal); Confound check: scan_head train accuracy 0.769 <…; next: Add a max-pooled learned bank (cell-assigned learned unit-modulus correlators with a per-cell max before the head) so learned placement is tested without the linear-combiner handicap that this milestone shows dominates the off-grid comparison; then sharpen with a non-uniform within-cell prior (ω concentrated near a cell edge) where the uniform grid is provably mismatched, which is the narrowest condition under which a learned bank could still beat the classical scan.; commit f87c34cf35bc148c06a74ce5d83f82762d4731bd; PR https://github.com/mrcha033/complex-nn-signal/pull/1 - azure_inference_queueing [scientific outcome=positive]: When a scheduling admission problem offers each item a cheaper reduced-quality variant (here FP16 vs INT8 inference), the problem becomes a multiple-choice knapsack whose greedy must respect nesting — but that extra combinatorial structure is provably INERT whenever the cheap variant's weight multiplier m <= 1/2. The upgrade increment (1-m)w is then at least as heavy as the base increment mw, so any class whose base was skipped for capacity can never fit its upgrade later (the residual only decreases); no increment is ever nesting-blocked. Consequently the single-boundary greedy-LP gap theory for the plain 0/1 knapsack transfers verbatim, with w_max reinterpreted as the largest increment we…; validation: src/mckp_gap.py self-test: 5,000 instances, 0 violations of Theorems 5a/5c/5d, max 5c identity error 2.1e-16; scripts/verify_mckp_gap.py full run exits 0: 0 violations across 1,080 synthetic Azure grid + 200 real BurstGPT instances; Theorem 5c identity max error 5.6e-17 over all 1,280 memory-binding instances; Theorem 5a: 0 violations; measured max alpha 0.0182 vs the m+a<1 threshold requiring alpha<0.5 (27x margin); Theorem 5b: 0/1080 mispredictions of order interleaving after correcting the p…; next: Extend the gap theory to two binding resources (memory and compute). Precision's compute effect (INT8 = 2x A100 TFLOPS) is the one first-order mechanism the current single-resource model structurally cannot represent, and it is the likeliest way the always-INT8 conclusion could flip: under a compute-binding regime the FP16 upgrade is penalised on a second axis rather than merely being memory-expensive, so the 0.012% decoupling penalty could grow materially. This is a genuine test of the theory'…; commit ca82d58c647f90efe955a73956ead589de8a4c0b; PR https://github.com/mrcha033/azure_inference_queueing/pull/1 - gaussian-3mul-compiler [scientific outcome=negative]: FMA contraction does not change the Gauss 3-mul vs 4-mul complex-multiply lowering decision. The hypothesis that contraction favours the 4-mul (because FMA removes a multiply rounding that the 3-mul's cancellation-dominated error cannot benefit from) is refuted: the 3-mul's k3 product is also fusable against -(k1+k2), so contraction improves the 4-mul's median imaginary error by 10-16% and the 3-mul's by 9-22%. The 3-mul/4-mul accuracy penalty therefore shifts by at most +-8% with a distribution-dependent sign (ratio 0.93-1.08), while the input distribution moves that penalty 1.50-3.08x. Distribution dominates contraction legality by roughly an order of magnitude. Separately and more broadl…; validation: Full suite: 426 tests passing (up from 404), 6.4s, .venv/bin/python -m pytest tests/ -q; Regression guards verified: reverted the transform_validator fix and observed all 3 new FMA guards fail, then restored; numpy array complex multiply pinned bit-for-bit to fma(a,c,-(b*d)), fma(a,d,b*c): 20000/20000 samples, numpy 2.5.1; Seed-stability check across 5 base seeds confirms median-based estimator spread 2.7-6.0% vs mean-based 29-154%; Study output deterministic under fixed seed (verified identica…; next: Fold the probabilistic gate into the lowering path end-to-end: probabilistic_cost_model calibrated C_q, but compiler_lowering/decision_framework still use the 3x worst-case constant. Emit a per-callsite gate kappa_refined <= tau/(C_q*eps) at a documented failure probability. Secondarily, re-audit remaining mean-based statistics in stability_analysis and error_bounds for the heavy-tail estimator hazard fixed here.; commit 8d5eaa96d8a91ecc74f594d4cb4f617b98aa003f; PR https://github.com/mrcha033/gaussian-3mul-compiler/pull/1
당시 조건	Only completed, evidence-backed research findings are included. Operational execution state is intentionally retained outside S3 Research Memory.
실제 결과	alpha-factory [scientific outcome=positive]: In a gated candidate-selection pipeline for portfolio ensembles, an upstream eligibility relaxation and a downstream diversity-aware selection preference are JOINTLY necessary and individually inert for escaping single-lineage collapse. A 2x2 factorial on a real ETF macro pool showed that admitting a decorrelated candidate that fails standalone must-beat-baselines gates (eligibility on) and adding a selection-stage diversification bonus (lambda=0.5) each leave the ensemble at 1 strategy / 1 lineage in isolation; only their combination yields a 2-lineage ensemble, improving validation max-drawdown from -4.07% to -2.55% while validation Sharpe rose 1.31 to 1.63 (pre-registered rule required o…; validation: tests/test_ensemble_diversifier_track.py — 5 passed (default-off pool invariance; admission of safety-clean decorrelated failer; refusal of a more-profitable equally-decorrelated candidate that tripped strict_survival; correlation-based refusal at 0.54 > 0.5; max_admit cap); Full suite: 366 passed, 2 failed in 120.84s; both failures confirmed PRE-EXISTING by stashing the change and re-running (tests/test_mandate_comparison.py requires the absent data/etf_macro_daily_v1 dataset); experiments/div…; commit 2da70a23ca89185cdbd3f40794c29c6dbc8e03c9; PR https://github.com/mrcha033/alpha-factory/pull/1 complex-nn-signal [scientific outcome=negative]: When a single-tone classification task is made continuous-frequency (ω drawn uniformly within each class cell, so no fixed correlator set is matched), a learned unit-modulus complex-recurrence correlator bank does NOT beat a parameter-free fixed-grid scan at matched inference budget — it loses at every budget (B=4/8/16) and both SNRs (−5/0 dB), winning 0/5 seeds. A 2×2 placement×combiner decomposition localizes the loss to the combiner, not correlator placement: the learned-θ bank ties the fixed-grid arm with a trained linear head (learned placement buys nothing, and the learned phases converge onto the uniform grid, 0.015Δ from bin centres at B=K), while the trained linear head loses 3.5 p…; validation: pytest tests/ -q → 248 passed, 1 warning, 3.11s (19 new tests in tests/test_offgrid_benchmark.py); python -m src.offgrid_benchmark → results_offgrid.json written, 94.1s CPU, 5 seeds × {jitter 0,1} × {−5,0} dB; Invariant test: generate_offgrid_doppler_dataset(jitter=0) reproduces generate_doppler_dataset bit-for-bit (torch.equal on X and y); Invariant test: scan_predict(x, K, n_per_cell=1) equals matched_filter_predict(x, K) exactly (torch.equal); Confound check: scan_head train accuracy 0.769 <…; commit f87c34cf35bc148c06a74ce5d83f82762d4731bd; PR https://github.com/mrcha033/complex-nn-signal/pull/1 azure_inference_queueing [scientific outcome=positive]: When a scheduling admission problem offers each item a cheaper reduced-quality variant (here FP16 vs INT8 inference), the problem becomes a multiple-choice knapsack whose greedy must respect nesting — but that extra combinatorial structure is provably INERT whenever the cheap variant's weight multiplier m <= 1/2. The upgrade increment (1-m)w is then at least as heavy as the base increment mw, so any class whose base was skipped for capacity can never fit its upgrade later (the residual only decreases); no increment is ever nesting-blocked. Consequently the single-boundary greedy-LP gap theory for the plain 0/1 knapsack transfers verbatim, with w_max reinterpreted as the largest increment we…; validation: src/mckp_gap.py self-test: 5,000 instances, 0 violations of Theorems 5a/5c/5d, max 5c identity error 2.1e-16; scripts/verify_mckp_gap.py full run exits 0: 0 violations across 1,080 synthetic Azure grid + 200 real BurstGPT instances; Theorem 5c identity max error 5.6e-17 over all 1,280 memory-binding instances; Theorem 5a: 0 violations; measured max alpha 0.0182 vs the m+a<1 threshold requiring alpha<0.5 (27x margin); Theorem 5b: 0/1080 mispredictions of order interleaving after correcting the p…; commit ca82d58c647f90efe955a73956ead589de8a4c0b; PR https://github.com/mrcha033/azure_inference_queueing/pull/1 gaussian-3mul-compiler [scientific outcome=negative]: FMA contraction does not change the Gauss 3-mul vs 4-mul complex-multiply lowering decision. The hypothesis that contraction favours the 4-mul (because FMA removes a multiply rounding that the 3-mul's cancellation-dominated error cannot benefit from) is refuted: the 3-mul's k3 product is also fusable against -(k1+k2), so contraction improves the 4-mul's median imaginary error by 10-16% and the 3-mul's by 9-22%. The 3-mul/4-mul accuracy penalty therefore shifts by at most +-8% with a distribution-dependent sign (ratio 0.93-1.08), while the input distribution moves that penalty 1.50-3.08x. Distribution dominates contraction legality by roughly an order of magnitude. Separately and more broadl…; validation: Full suite: 426 tests passing (up from 404), 6.4s, .venv/bin/python -m pytest tests/ -q; Regression guards verified: reverted the transform_validator fix and observed all 3 new FMA guards fail, then restored; numpy array complex multiply pinned bit-for-bit to fma(a,c,-(b*d)), fma(a,d,b*c): 20000/20000 samples, numpy 2.5.1; Seed-stability check across 5 base seeds confirms median-based estimator spread 2.7-6.0% vs mean-based 29-154%; Study output deterministic under fixed seed (verified identica…; commit 8d5eaa96d8a91ecc74f594d4cb4f617b98aa003f; PR https://github.com/mrcha033/gaussian-3mul-compiler/pull/1
왜 그랬는지	These are evidence-backed scientific outcomes. Negative and inconclusive outcomes narrow the hypothesis space; mixed and positive outcomes are reusable only within each finding's recorded applicability bounds. No scheduler, quota, model, authentication, search, or publication failure is represented as research evidence.
다음에 기억할 것	alpha-factory: When an ensemble or portfolio collapses to one lineage, diagnose which pipeline stage is binding before tuning any selection-stage knob: a diversity bonus at selection is arithmetically irrelevant if upstream per-candidate quality gates already removed the decorrelated candidates. Test eligibility and selection changes as a factorial rather than sequentially, because either alone can read as a null result while their interaction carries the whole effect. When relaxing gates, partition them explicitly into role-specific quality gates (bypassable, since a hedge underperforms benchmarks by construction) versus safety gates (never bypassable), and verify selectivity by checking that an attractive-looking candidate failing a safety gate is still refused. complex-nn-signal: When benchmarking a learned detector against a classical one, control inference budget (number of correlations/evaluations per example) rather than parameter count — once the classical method must search, compute is the scarce resource and parameter-matching measures the wrong thing. Decompose any apparent neural advantage into placement (where the basis functions sit) and combiner (how their outputs are pooled) with an arm that varies only one at a time; here the entire effect lived in the combiner. Diagnose a trained arm losing to a parameter-free arm by comparing TRAIN accuracies — if the trained arm underfits the parameter-free one, the deficit is expressivity, not generalization, and adding data or regularization will not help. Expect learned bases to help when the true support is sparse and discrete (they concentrate on it) and to be inert when the support is a continuum (the uniform grid is already optimal, so gradient descent only adds placement scatter). azure_inference_queueing: Two reusable items. (1) Before building a joint optimizer for a coupled item-selection plus variant-choice problem, check where the cheap variant's weight multiplier sits relative to 1/2 of the full weight: at m <= 1/2 the coupled problem provably degenerates to the decoupled one, the nesting constraint never binds, and single-variant approximation theory transfers with a strictly tighter constant. Adding a cheap-variant dimension can NARROW rather than widen a greedy optimality gap, because the smaller variant improves packing granularity — the opposite of the natural intuition. (2) When a theorem yields a structural ordering condition, never report it as a prediction of the realized event without including the resource constraint in the test: an ordering can hold on ~50% of instances while the corresponding realized event occurs on 0%, because realizing it also requires the capacity to reach that point. Keep the structural quantity and the realized quantity in separate reported fields; conflating them is a repeatable, self-inflicted falsification. gaussian-3mul-compiler: Two reusable lessons. (1) When evaluating whether a hardware primitive such as FMA favours one algebraic formulation over another, check whether the supposedly disadvantaged formulation can also use the primitive - here the Karatsuba-style 3-mul's combining product was fusable, which neutralised the entire predicted effect. Comparing a contracted baseline against an un-contracted candidate is a rigged comparison. (2) For floating-point error studies, report medians and quantiles, never sample means: relative FP error is heavy-tailed and mean ratios are dominated by rare near-cancellation samples. Verify any error-ratio effect with a seed-stability check across at least five seeds before believing it - in this study an exploratory mean-based run produced a number that supported the hypothesis the stable estimator then refuted.
언제 맞는지	alpha-factory: Holds for gated candidate-selection pipelines that filter per-candidate on standalone merit before assembling a portfolio or ensemble, where some candidate roles are expected to underperform standalone benchmarks. Demonstrated on one ETF macro pool, one asset universe, and a single 587-observation validation window with lambda=0.5 tested rather than calibrated; the dose-response shape, generalization beyond two lineages, and out-of-sample confirmation on the sealed hidden test are all unestablished. The joint-necessity structure is expected to transfer more readily than the specific effect sizes. complex-nn-signal: Synthetic single-tone Doppler-bin classification, K=4 classes, sequence length L=16, complex AWGN at −5/0 dB, 5 seeds, CPU, small models with shared hyperparameters; detectors compared are a uniform-grid correlator scan and a diagonal unit-modulus linear complex recurrence with a linear magnitude read-out. The negative result is bounded by the learned bank having no cell structure, so it cannot implement a per-cell max even in principle — the fair retest is a max-pooled learned bank, ideally under a non-uniform within-cell prior where the uniform grid is provably mismatched. Conclusions about budget-matched evaluation and the train-accuracy underfitting diagnostic generalize beyond this task; the specific accuracy margins do not. azure_inference_queueing: Holds for single-resource (memory-constrained) admission control where each item has one reduced-quality variant with a proportional weight multiplier m and a fractional value loss a satisfying m + a < 1, compared against the fractional LP relaxation rather than a full-horizon MDP optimum. The inertness result (m <= 1/2) is a general property of the increment weights and is workload-independent; the quantitative decoupling penalties are specific to workloads where accuracy is priced linearly and cheaply relative to revenue (validated on synthetic Azure NDm A100 v4 parameters and real BurstGPT windows, 2-3 service classes). Untested for three or more variants, convex or SLA-cliff accuracy penalties, and — most importantly — multi-resource settings: the model captures only quantization's memory effect, not its compute effect (INT8 = 2x TFLOPS), which could plausibly change the decoupling conclusion under a compute-binding regime. gaussian-3mul-compiler: IEEE-754 binary64 complex multiplication on CPU, measured against exact rational truth over uniform/normal/lognormal/mixed-scale/large-magnitude input distributions. The contraction result assumes the compiler fuses one product per output part (the asymmetric contraction numpy emits); the mirrored fusion choice has a distinct distribution-dependent profile and is not covered by the conclusion. Imaginary part only, since 3-mul and 4-mul real parts are bit-identical. The median-vs-mean estimator lesson generalises to any heavy-tailed floating-point error comparison; the specific spread magnitudes are sample-size dependent.
신뢰도	중간
관련 자료	θ − uniform grid\\| = 0.015Δ at B=K, 0.12–0.26Δ at B>K; results_offgrid.json on-grid control: bank beats scan_hardmax 5/5 at B=8 (+0.082) and B=16 (+0.045); scan_hardmax drops 0.913→0.829 as budget rises from B=4 to B=8; src/offgrid_benchmark.py with tests/test_offgrid_benchmark.py (19 tests): jitter=0 reproduces the prior on-grid dataset bit-for-bit and n_per_cell=1 reproduces the existing matched filter exactly, both unit-tested; 248 tests pass; complex-nn-signal commit f87c34cf35bc148c06a74ce5d83f82762d4731bd; https://github.com/mrcha033/complex-nn-signal/pull/1; src/mckp_gap.py: Theorems 5a-5d with proofs; self-test over 5,000 instances, 0 violations, max identity error 2.1e-16; scripts/verify_mckp_gap.py: 0 violations across 1,080 synthetic Azure-parameter instances + 200 real BurstGPT trace windows; max backfill-identity error 5.6e-17; Theorem 5d phase transition, falsifiable in both directions and confirmed: instances with a nesting-blocked increment = 0, 0, 0 at m = 0.25, 0.40, 0.50 and 8, 13, 19 at m = 0.60, 0.75, 0.90; Distribution-free bound compression from halved increment weight: mean bound 98.9% -> 79.3% (synthetic), 88.2% -> 54.1% (BurstGPT); Decoupling penalty vs jointly-optimal greedy: always-cheap-variant 0.012% synthetic / 0.0007% BurstGPT; prefer-high-then-demote 35.3% / 42.6%; always-high 42.1% / 46.6%; Robustness sweep: always-cheap penalty stays under 0.01% up to a 27x scaling of the accuracy price, degrading to 1.87% only at 50x where the m + a < 1 variant-survival hypothesis fails; paper/theorem_mckp_precision.md and RESEARCH_NOTES.md record the result, its limitations, and a corrected first draft of the ordering theorem; azure_inference_queueing commit ca82d58c647f90efe955a73956ead589de8a4c0b; https://github.com/mrcha033/azure_inference_queueing/pull/1; src/fma_contraction_study.py: five formulations (naive_4m, fma_4m, fma_4m_mirrored, naive_3m, fma_3m) scored against exact fractions.Fraction truth across five input distributions; Median imaginary relative-error penalty ratios (pen_contr/pen_naive): uniform 0.931, normal 1.077, lognormal 0.926, mixed_scale 0.959, large_magnitude 0.978 - all within +-8% of 1.0; Penalty across distributions varies 1.50-3.08x (uniform 1.50, large_magnitude 1.61, normal 1.88, mixed_scale 2.52, lognormal 3.08), dwarfing contraction's effect; seed_stability(): mean-based contracted penalty spread 101.2% across 6 seeds at n=20000 on normal inputs vs 1.3% median-based; at n=8000 across 5 base seeds, mean 29-154% vs median 2.7-6.0%; Defect fixed: transform_validator._simulate_variant modelled FMA as the 4-mul, making validate_transform(4mul, fma) report exactly 0.0 error on all 5000 inputs; three regression guards confirmed failing against pre-fix code; numpy 2.5.1 array complex multiply verified bit-for-bit as fma(a,c,-(b*d)), fma(a,d,b*c) - 20000/20000 samples; Test suite 404 -> 426 passing; gaussian-3mul-compiler commit 8d5eaa96d8a91ecc74f594d4cb4f617b98aa003f; https://github.com/mrcha033/gaussian-3mul-compiler/pull/1
자료 출처	우리 기록
작성자	S3ResearchAgent
처음 작성한 시각 (UTC)	2026-07-20T09:47:03.316939Z
마지막 수정 시각 (UTC)	2026-07-20T09:47:04.206465Z

근거 research-artifact-1255a43b1c4bfcb3: 2x2 factorial experiments/diversifier_track_ablation.py on run divsweep_pool: (off,0.0)/(off,0.5)/(on,0.0) all 1 strategy / 1 lineage, validate Sharpe 1.3085, maxDD -4.07%; (on,0.5) 2 strategies / 2 lineages, Sharpe 1.6279, maxDD -2.55%

벤치마크 · 확인 범위: 일부 자료 확인 · S3ResearchAgent · 2026-07-20T09:47:03.316939Z

근거 commit_2da70a23ca89185c: GitHub mrcha033/alpha-factory commit 2da70a23ca89185cdbd3f40794c29c6dbc8e03c9 (원문 열기)
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근거 commit_f87c34cf35bc148c: GitHub mrcha033/complex-nn-signal commit f87c34cf35bc148c06a74ce5d83f82762d4731bd (원문 열기)
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근거 commit_ca82d58c647f90ef: GitHub mrcha033/azure_inference_queueing commit ca82d58c647f90efe955a73956ead589de8a4c0b (원문 열기)
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코드 · 확인 범위: 일부 자료 확인 · S3ResearchAgent · 2026-07-20T09:47:04.206465Z
Commit emitted by this cycle; validation scope is recorded in the Lesson.