속성:Observation
직접 확인한 결과를 적습니다. 원인에 대한 해석은 따로 적습니다.
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mlir-fft-compiler [scientific outcome=inconclusive]: Scientific outcome=inconclusive. GPU3 고아 프로세스를 정리하고 래퍼 점유·cuda:0 가시성을 확인했다. 기존 정사각 CUDA 하네스는 5.3MB nvcc 컴파일 중 목표 직사각형/타일 코드를 포함하지 않아 중단했다. CPU 정확성·신규 형상 timing·spill counters·reload law 판정은 산출하지 못했으며 exploratory artifact만 보존했다.; validation: The implied reload count local_ld/(4*excess) is measured at K=32, K=128, and K=256, decisively separating reloads = rho*K from reloads = const (they differ ~4x at K=32, J=128); Every lowering passes an independent CPU W@x correctness check (max_abs_error well under 5e-10) before its timing is admitted; The spill predicate 8+2*MAXLIVE > 255 is scored against nvcc's static spill bytes at all eight new (shape, lowering) points; any disagreement is reported rather than smoothed; Predicted vs measur…; commit 79af53d2e524fe940e847f277ca2e347555cfd96; PR https://github.com/mrcha033/mlir-fft-compiler/pull/1; GPU handoff 20260720T030001Z-mlir-fft-compiler
multi-lora-fusion [scientific outcome=passed]: Scientific outcome=passed. L40S 물리 GPU3(cuda:0)에서 격리 스냅샷 3dd704c8로 두 실험을 직렬 수행했다. boundary_ordering_cuda.json: LLC 60.0MiB, working-set cap 75(근접 shape; WS 37.4MiB knee), output cap 239, 두 cap 순서 역전 0/865; WS 경계 0.261배 graded rise, output 경계 0.791배 step. output-tail 2-boundary 상대오차 평균/최대 0%/0%, single-knee 3.35%/3.35%. transition_form_cuda.json: N=384 resident→448 spilled에서 output/LLC 0.400→0.467, 1.638배 jump; single-knee far-tail 평균/최대 오차 35.8~43.1%/45.0~55.6%, H_single 기각, H_two 지지(조합 모델 평균 38.1%, 출력 knee는 예상 N=959와 불일치). GPU3 check 후 유휴 확인. 아티팩트 두 개를 .research-autopilot/gpu-runs/20260720T030001Z-multi-lora-fusion/에 보존.; validation: Both knees located on L40S with each bracketed to within ~10% in batch size, and each point's working-set bytes, output bytes, L2 estimate, and per-boundary resident/spilled state logged separately (Codex preflight clm_003); A stated verdict on whether working_set_cap <= output_cap holds on GPU, with the measured working-set knee compared against 0.5*L2 -- the quantity that decides whether the ordering can invert; A stated verdict on whether the GPU working-set transition is graded (a rise over…; commit 3dd704c833ccf69c1dfc5ba0c1fa2f733d32e94e; PR https://github.com/mrcha033/multi-lora-fusion/pull/1; GPU handoff 20260720T030001Z-multi-lora-fusion
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
mlir-fft-compiler [scientific outcome=positive]: When validating a cost-model rate law whose form is degenerate at the calibration point (here spill reloads=0.685*K vs constant=87.68, which coincide at the only spilled calibration shape K=128), a pre-registered held-out design identifies the law only through shapes that break the degeneracy. In the mlir-fft-compiler spill contract, of four held-out families exactly one (rect-k32-j128, the only family that spills with K!=128) separates the two laws: 4.0x in the local-load NCU counter and 39.3pp in derived timing advantage vs a 5pp tolerance. The direct counter channel separates the laws far more sharply than wall-clock, where spill and instruction terms partly cancel.; validation: 1,232 tests pass (7 new) via .venv/bin/python -m pytest tests/ -q; new tests: tests/test_spill_resolving_power.py (3) and 4 in tests/test_spill_validation_contract.py; Pending preflight receipt confirmed byte-identical to experiments/results/spill_heldout_preflight_v1.json after the change; contract_sha256=c8b3bce... unchanged; git diff --stat over experiments/spill_heldout_contract_v1.json, src/spill_model.py, and the preflight receipt is empty (frozen files untouched); verify_spill_heldout --…; commit a0a464ea5a5f4f353574da7cd632cb6fcf5241fa; PR https://github.com/mrcha033/mlir-fft-compiler/pull/1 +
alpha-factory [scientific outcome=positive]: On the alpha-factory real ETF-macro pool, the diversifier-track finding (ensemble diversity requires BOTH an upstream eligibility track admitting a decorrelated quality-gate-failer AND a downstream diversification-lambda>0) survives regeneration under hidden-suffix-sealed evaluator semantics v3, and survives a pre-registered single-window selection-sensitivity gate. The (eligibility=on, lambda=0.5) cell adds seed_crisis_hedge, improving ensemble_validate max-drawdown from -4.07% to -2.57% and raising Sharpe from 1.31 to 1.62. Paired stationary block bootstrap: Sharpe delta +0.314 (95% CI [+0.029,+0.62], P(delta<=0)=1.3%), P(drawdown better)=100%; CSCV PBO=0.000 over 252 partitions. The cand…; validation: pytest: 371 passed, 2 failed (known fixture-missing: data/etf_macro_daily_v1/SPY.csv absent; matches prior state's 2 known deselections); v3 regeneration: divsweep_pool_v3 artifacts carry evaluator_semantics id close-t-signal_..._hidden-suffix-sealed_v3; hidden_test_included=False; 2x2 ablation v3 (docs/evidence/diversifier_track_ablation_v3.json): only (on,0.5) is 2-lineage, validate Sharpe 1.6218 maxDD -2.57%; claim supported=True; selection sensitivity (docs/evidence/diversifier_selection_se…; commit f8e17cd2a2c1705365044fb3c5866fffc3d03030; PR https://github.com/mrcha033/alpha-factory/pull/1 +
azure_inference_queueing [scientific outcome=positive]: For the two-constraint (KV-memory × compute) admission knapsack, the LP relaxation vertex has at most two fractional items, giving the constructive per-instance certificate LP*−IP* ≤ v_s1+v_s2 ≤ 2·max_i v_i (the single-resource single-break-item bound with the boundary count doubled 1→2; the single-resource bound is the C→∞ corner). The *relaxation* certificate transfers to two resources, but the greedy/Dantzig optimality certificate does not: single-resource optima are jointly infeasible and the effective-density two-resource greedy is near-optimal only as a workload-composition regularity (mean 2.1% below IP* on random grids, but 37.5% below on an adversarial anticorrelated instance).; validation: src/two_resource_gap.py self-test: 4,000 instances (n=6..12) + frozen contract fixture — 0 structure violations, 0 gap-bound violations, 0 round-down/oracle violations, PASS; identical output across two runs (deterministic); scripts/verify_two_resource_gap.py full grid: 1,296 structural instances with n up to 200 (max fractional items = 2, 0 violations) + 576 exact-enumeration-IP-oracle instances (0 gap-bound violations, 0 round-down violations); contract counterexample IP*=16.0 as sealed, gree…; commit 19c75ff77b05d599d23daf1e1448a772a9022bbf; PR https://github.com/mrcha033/azure_inference_queueing/pull/1 +
alpha-factory [scientific outcome=mixed]: In the alpha-factory ensemble diversifier, the frozen (eligibility=on, lambda=0.5) admission rule replicates STRUCTURALLY across three non-overlapping rolling-origin outer validate windows (2018-2025) — every fold admits the decorrelated crisis hedge, builds 2 lineages, is Sharpe non-inferior, and improves drawdown directionally — but its ECONOMIC MAGNITUDE is regime-specific: the paired-bootstrap Sharpe-delta 95% CI excludes zero only in the most recent 2022-2025 window (+0.313 CI[+0.03,+0.62]), while pooled over all seven years dSharpe=+0.046 CI[-0.27,+0.38] and aggregate maxDD is essentially unchanged (-10.55%->-10.26%). A single-window bootstrap that clears significance can substantiall…; validation: tests/test_diversifier_outer_window_design.py: 5 passed (fold design causal, non-overlapping, hidden-sealed, frozen admission rule); Re-ran experiments/diversifier_outer_window_folds.py --run-id divsweep_pool_v3; JSON output reproduces docs/evidence/diversifier_outer_window_folds_v3.json byte-for-byte (deterministic, bootstrap seed 20260721); Per-fold oracle: recomputed validate Sharpe matches builder-reported within 0.02 (fold_2 flagged reproduces=false at 0.035 gap on a negative-Sharpe window…; commit fc5de9cb146144570971c1f53094cb73603b942b; PR https://github.com/mrcha033/alpha-factory/pull/1 +
multi-lora-fusion [scientific outcome=mixed]: For a memory-capacity cost knee fit from noisy CPU timing (LoRA fusion working-set knee), the governing quantity is total working-set bytes at a fixed cache fraction (~0.6-0.65·LLC), not any single tensor/subset — the winning predictor is unanimous across independent runs (CV ~0.16 vs ≥0.28 for every subset). But the precision of the fitted fraction is far worse than a single pass suggests: single-pass cross-shape CV ranges 0.07-0.31 and LOO max 14-71%, and even a 5-run batch aggregate is soft — two independent 5-run batches placed the fraction at 0.662±0.035 and 0.597±0.013, a batch-to-batch gap exceeding either batch's own across-run sd. A plausible-looking second-order tensor-share corre…; validation: pytest -q → 673 passed in ~1.2s (CUDA_VISIBLE_DEVICES='' OMP_NUM_THREADS=1 MKL_NUM_THREADS=1); pytest tests/test_knee_stability.py tests/test_knee_predictor.py -q → 32 passed against the committed receipt; Independent re-execution: python -m experiments.predict_working_set_knee --repeats 5 (fresh process, out=/tmp/knee_repro_check.json, committed receipt left untouched) — winner total_working_set unanimous in all 5 runs; fraction 0.597 ± 0.013 [0.573,0.613]; single-run CV 0.067–0.223; LOO max 1…; commit 2ebe798bd72f16689b44d373f90377b7b00a7ae2; PR https://github.com/mrcha033/multi-lora-fusion/pull/1 +
complex-nn-signal [scientific outcome=positive]: In a synthetic chirp/Doppler classification task (K=4, L=16), a learnable freq+rate chirplet correlator bank (immutable cells, per-cell hard max, no learned head) fails under plain SGD to null its chirp-rate degrees of freedom on constant-frequency data, costing ~0.09-0.10 accuracy vs the constant-frequency matched-filter scan at rho=0. Adding an L1 penalty lambda*mean(\\|rate\\|) resolves this: it is an optimization limit, not a capacity limit. A single fixed lambda=3.0 acts as a data-adaptive gate, driving mean\\|rate\\| to 0.000 on constant-frequency data (rho=0) while the cross-entropy data-gradient keeps it ~0.12 on chirped data (rho=0.3). This recovers matched-filter accuracy at rho=0…; validation: pytest tests/ -> 298 passed (292 prior + 6 new), CPU via .venv (uv py3.12, torch 2.13.0), 8.2 s; python -m src.chirp_rate_gate_benchmark -> results_rate_gate.json in 334 s CPU; 640 chirplet trainings over B{16,32} x rho{0,0.1,0.2,0.3} x SNR{-5,0} x lambda{0,0.3,1.0,3.0} x seeds 310..319; Adaptivity: lambda=3.0 gives mean\\|rate\\|@rho0=0.000 vs @rho0.3=0.120 (B32/SNR=0) and 0.000 vs 0.116 (B16/SNR=0); freq spread stays 0.49-0.56; Escape conversion: B16/SNR=0 chirplet-minus-best_fixed margins la…; commit 9f879ee72bc75e0c1be88120a5616f4f8628ae11; PR https://github.com/mrcha033/complex-nn-signal/pull/1 +
alpha-factory [scientific outcome=negative]: The alpha-factory diversifier-track finding (eligibility=on, lambda=0.5 admitting a second crisis-hedge lineage that improves drawdown at non-inferior Sharpe) does NOT transfer to a genuinely separate universe. Under a fully frozen protocol (same seed, lambda, admission rule, folds, bootstrap; only the universe changed) regenerated on etf_macro_transfer_daily_v1 (international/real-asset ETF subset), the diversifier track admits 0 candidates: all four 2x2 ablation cells and all three rolling-origin outer folds collapse to the identical single-lineage ensemble with exactly zero treatment-vs-baseline delta. The mechanism is measurable: the carrying sleeve seed_crisis_hedge has standalone fitn…; validation: Full pytest suite: 376 passed, 2 pre-existing fixture-missing failures (data/etf_macro_daily_v1 absent), 123s — matches documented baseline, no regressions; alpha verify-data on experiments/etf_macro_transfer_divlambda_0p5.yaml: verified real dataset etf_macro_transfer_daily_v1, 5 files sha-checked; 2x2 ablation (docs/evidence/diversifier_track_ablation_transfer_v3.json): all 4 cells -> 1 strategy / 1 lineage [seed_defensive_cash], two_lineage=False, supported=False; Outer-window folds (docs/ev…; commit 5d8056d20c493207215a9d679f492dc81588c598; PR https://github.com/mrcha033/alpha-factory/pull/1 +
algebraic-ml-compiler [scientific outcome=mixed]: Scientific outcome=mixed. L40S 물리 GPU 3(cuda:0) 격리 스냅샷에서 theorem-v3 K=3/128의 5개 실행 프로파일씩 총 10개가 모두 참조 비트와 일치했다(10/10, 2.270초). TinyLlama 1.1B FP16의 6/12/11 토큰 프롬프트와 4-token greedy decode는 eager/compiled 생성 토큰열이 3/3 일치했으나 logits 최대 절대오차 0.154296875, NLL 최대 절대차 0.00563097, KV 최대 절대오차 0.02264404로 1e-3 품질 게이트를 실패했다. KV는 프롬프트별 44개 텐서, 형상 [1,4,9,64]/[1,4,15,64]/[1,4,14,64]로 실제 비교했다. 판정: theorem 하드웨어 적합성 통과, pretrained 수치 동등성 실패의 mixed 결과.; validation: All ten theorem-v3 hardware profiles match their reference bits; Pretrained artifact records logits, NLL, generated tokens, and KV-cache comparison with provenance; Artifacts: algebraic-ml-compiler/RESEARCH/precision_aware_rewrite_legality_20260718_174112/theorem_v3_cuda_sm89_validation_20260722.json, algebraic-ml-compiler/RESEARCH/precision_aware_rewrite_legality_20260718_174112/pretrained_kv_cache_quality_l40s_20260722.json; commit e5034b987d55d449dbb0ca296c24097aba49422b; PR https://github.com/mrcha033/algebraic-ml-compiler/pull/1; GPU handoff algebraic-ml-compiler-theorem-v3-kv-20260722 +
sparse-lowrank-runtime [scientific outcome=positive]: On the L40S FP32 torch.sparse_bsr sweep, the frozen parametric break-even rule (bsr_ms ≈ floor + beta(block_size)·flops·density) generalizes to held-out shapes at +2.6% of oracle with 0 false-sparse, while a nearest-config lookup that borrows the closest benchmarked config's crossover threshold is +22.2% out of sample — worse than always-dense (+15.0%) — and the only memorizer that dispatches into slowdowns (9 false-sparse). The advantage survives the strongest simple memorizer, not just a strawman exact-match table.; validation: .venv/bin/python -m pytest -q → 639 passed, 1 warning (was 635; +4 new nearest-config tests); .venv/bin/python -m pytest tests/test_generalization.py -q → 25 passed; Reproduced leave_one_shape_out pooled regret on results/l40s_gpu3_bsr_dispatch.json: parametric +2.6% (false_sparse=0), nearest_lookup +22.2% (false_sparse=9), exact-lookup/always_dense +15.0%, atlas +86.5%, always_sparse +142.3%; Confirmed leave_one_config_out gives identical nearest_lookup +22.2% (not an artifact of the shape-lev…; commit 969deb5a1c941e91cf0ec8e162b732e6ec7198e9; PR https://github.com/mrcha033/sparse-lowrank-runtime/pull/1
sparse-lowrank-runtime [scientific outcome=mixed]: A cross-validated dispatch-rule generalization headline (+2.6% out-of-sample vs +15% lookup) can be carried by a single decisive fold. On the L40S FP32 BSR sweep, exactly 1 of 3 leave-one-shape-out folds has any held-out point where BSR beats dense; the other two are trivial (oracle all-dense) so every conservative policy ties, and ms-weighted pooling averages the one real +3.2% with two +0.0% folds into +2.6%, disguising a single-shape extrapolation (N of held-out winning shapes = 1). A training-config jackknife of that sole extrapolation shows the direction is robust (all refits stay far under always-dense's +18.7%, spread 5.2pp) but the zero-false-sparse safety is not (dropping one train…; validation: python -m pytest tests/test_fold_robustness.py -q → 16 passed; python -m pytest -q → 655 passed, 1 warning (639 prior + 16 new); Reproduce command in RESEARCH_NOTES 2026-07-26 robustness entry executed: prints '1 decisive, 2 trivial', decisive parametric +3.2% vs always-dense +18.7%, jackknife spread 5.2pp with worst_false_sparse=1, win capture 6/9 wins 82.8% of savings; commit be11743facdb7f921c8fa8fdc9ec4a946ad24b8b; PR https://github.com/mrcha033/sparse-lowrank-runtime/pull/1
rfuse modernizing userspace filesystem framework through scalable kernel userspace communication b3196db3 +
workloads=data and metadata filesystem workloads; baselines=traditional FUSE and in-kernel filesystems; metrics=throughput and scalability; results=comparable throughput to in-kernel filesystems; no exact value in official abstract +
Android/Linux smartphone board에서 반환시간은 모든 테스트에서 0.77초 미만이었고, 가용 메모리와 workload memory intensity에 따라 throughput이 2–200% 증가했다. +
workloads=single SSD; RAID; realistic power-fault injection; baselines=fixed-timeout watchdogs; metrics=data loss; failure-point accuracy; detection predictability; results=96.7% less data loss; 99.8% failure-point accuracy +
workloads=Rowhammer mitigations at TRH 128; baselines=AQUA; SRS; BlockHammer; metrics=slowdown; metadata; results=Slowdown 15→1%, 60→2%, 600→3%; <1 KB metadata. +
s
workloads=all-flash-array workloads; names not stated in abstract; baselines=state-of-the-art AFA engines; metrics=write throughput; average write latency; results=2.5x write throughput; 52.7% lower average write latency +
workloads=mmap microbenchmark, LevelDB, Apache, Metis, Psearchy; baselines=Linux 6.8 address-space design; metrics=throughput; results=13.1x, 4.49x, 3.19x, 1.47x, and 1.27x respectively +
workloads=4- and 8-socket x86_64; Webserver; Memcached; memory-management microbenchmarks; baselines=baseline Linux; eager full page-table replication; metrics=runtime; TLB shootdowns; memory-management overhead; results=12% Webserver and 36% Memcached runtime improvement; up to 40× baseline overhead characterized. +
workloads=billion-scale ANN datasets; commercial Samsung SmartSSDs; baselines=CSDANNS; metrics=QPS; recall; multi-SSD scalability; results=up to 10.7x QPS; near-linear scaling +
workloads=batch applications and latency-critical Memcached; baselines=existing page-based far-memory coordination; metrics=throughput and p99 latency; results=up to 4.2x throughput; 94.5% lower p99 +