속성:Evidence overview
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.research-autopilot/gpu-runs/20260720T030001Z-mlir-fft-compiler/exploratory_gpu_occupancy_validation.json; mlir-fft-compiler commit 79af53d2e524fe940e847f277ca2e347555cfd96; https://github.com/mrcha033/mlir-fft-compiler/pull/1; .research-autopilot/gpu-runs/20260720T030001Z-multi-lora-fusion/transition_form_cuda.json; multi-lora-fusion commit 3dd704c833ccf69c1dfc5ba0c1fa2f733d32e94e; https://github.com/mrcha033/multi-lora-fusion/pull/1 +
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%; Admission trace: 10 rejected candidates considered, 1 admitted (mean absolute return-correlation 0.26 to elite); 6 refused on non-bypassable safety gates, 1 on correlation 0.54 above the 0.5 ceiling, 2 on negative standalone fitness; Deflated-Sharpe-style best-of-4 haircut: treatment Sharpe 1.6279 exceeds threshold 1.091 over 587 validation observations; tests/test_ensemble_diversifier_track.py, 5 passing, including refusal of a more profitable and equally decorrelated candidate that failed a safety gate; Prior-cycle control: the same lambda sweep with eligibility off was previously shown inert because the pool reaching selection had a single member; alpha-factory commit 2da70a23ca89185cdbd3f40794c29c6dbc8e03c9; https://github.com/mrcha033/alpha-factory/pull/1; results_offgrid.json: off-grid 5-seed means at −5 dB, scan_hardmax 0.691/0.785/0.789 vs bank 0.656/0.752/0.765 at B=4/8/16 (bank−hardmax −0.035/−0.033/−0.024, 0/5 seeds won); results_offgrid.json: bank ≈ scan_head at B≥8 (0.752 vs 0.750; 0.765 vs 0.761), isolating learned placement as contributing nothing; results_offgrid.json train_acc_mean: scan_head 0.769 < scan_hardmax 0.801 at B=8/−5 dB, establishing underfitting not overfitting; results_offgrid.json bank_theta: mean \\|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
experiments/results/spill_resolving_power_v1.json: one reload-law-identifying family (rect-k32-j128), counter_divergence_ratio=4.0, timing_divergence_pp=39.33, primary_falsifiable_if_challenger_true=true; other three families divergence 0pp; src/spill_validation_contract.py resolving_power() and score_reload_law(); frozen predictions give linear-K spill-load 23.6/46.0 kB vs constant 94.3/184.1 kB per problem at rect-k32-j128; 1,232 tests pass including selection of the true reload law from the local-load counter under validated fixtures; preflight receipt byte-identical and contract_sha256 unchanged; mlir-fft-compiler commit a0a464ea5a5f4f353574da7cd632cb6fcf5241fa; https://github.com/mrcha033/mlir-fft-compiler/pull/1 +
docs/evidence/divsweep_pool_v3_leaderboard.csv (v3 pool, 3 gate-passers unchanged from v2); docs/evidence/diversifier_track_ablation_v3.json (2x2: only (on,0.5) is 2-lineage, Sharpe 1.6218, maxDD -2.57%); docs/evidence/diversifier_selection_sensitivity_v3.json (Sharpe delta +0.314 CI95 [+0.029,+0.62], P(delta<=0)=1.3%, CSCV PBO=0.000); docs/evidence/divsweep_pool_v3_diversifier_track.json (seed_crisis_hedge admitted, fit 0.50, mean\\|corr\\| 0.27); pytest 371 passed / 2 known fixture-missing; alpha-factory commit f8e17cd2a2c1705365044fb3c5866fffc3d03030; https://github.com/mrcha033/alpha-factory/pull/1 +
src/two_resource_gap.py self-test: 4,000 synthetic instances (n=6..12) + frozen 4-item contract counterexample, 0 structure/gap/round-down violations; scripts/verify_two_resource_gap.py: 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); LP*-IP* relative gap mean 12.6% and worst 100%; greedy-vs-IP mean 2.1%, 37.5% on the adversarial fixture; two fractional items occur in 86% of anticorrelated-regime instances; paper/theorem_two_resource_gap.md constructive round-down proof; contract counterexample IP*=16 reproduced; azure_inference_queueing commit 19c75ff77b05d599d23daf1e1448a772a9022bbf; https://github.com/mrcha033/azure_inference_queueing/pull/1 +
experiments/diversifier_outer_window_folds.py replayed on divsweep_pool_v3; deterministic (seed 20260721), reproduces docs/evidence/diversifier_outer_window_folds_v3.json byte-for-byte; Per-fold: fold_1 dSharpe +0.123 CI[-0.94,+0.96] P(d<=0)=0.443; fold_2 +0.040 CI[-0.35,+0.46] 0.429; fold_3 +0.313 CI[+0.03,+0.62] 0.013; pooled(1760 obs) +0.046 CI[-0.27,+0.38] 0.391; all_folds_stable=True (2 lineages, Sharpe non-inferior, DD improved every fold); admission decision window-independent by construction (corr-to-elite over full research prefix); tests/test_diversifier_outer_window_design.py 5 passed; full suite 376 passed / 2 pre-existing fixture-missing failures; Hidden suffix [0.94,1.0] never touched; oracle reproduces builder validate Sharpe within 0.02 per fold; alpha-factory commit fc5de9cb146144570971c1f53094cb73603b942b; https://github.com/mrcha033/alpha-factory/pull/1 +
experiments/predict_working_set_knee.py (run_stability, --repeats) and committed receipt experiments/results/working_set_knee_cpu.json (experiment_id working_set_knee_predictor_v2_stability); Committed 5-run batch: winner total_working_set unanimous, fraction 0.662±0.035, single-run CV 0.072-0.312, LOO max 14-71%, output-share corr −0.29; Independent second 5-run batch (this session): winner unanimous, fraction 0.597±0.013, single-run CV 0.067-0.223, LOO max 14.1-43.4%, output-share corr −0.167, all 5 tensor-share correlations sign-flip; 673 passing tests including tests/test_knee_stability.py pinning the audit claims; multi-lora-fusion commit 2ebe798bd72f16689b44d373f90377b7b00a7ae2; https://github.com/mrcha033/multi-lora-fusion/pull/1 +
src/chirp_rate_gate_benchmark.py; results_rate_gate.json (334 s CPU, seeds 310..319 disjoint from all prior blocks); lambda=3.0 mean\\|rate\\|: 0.000 at rho=0 vs 0.120 at rho=0.3 (B32/SNR=0); 0.000 vs 0.116 (B16/SNR=0); within-cell frequency spread stays 0.49-0.56; B16/SNR=0 chirplet-minus-best_fixed margins lambda=0->3.0: rho=0 -0.009->+0.001, rho=0.1 -0.060->+0.009, rho=0.3 +0.041->+0.055 (dominates_axis False->True); SNR-gated boundary: at SNR=-5 even lambda=3.0 leaves \\|rate\\|@rho0 at 0.09-0.12 and dominates_axis stays False; 298 tests pass including a lambda=0 bit-for-bit training identity; complex-nn-signal commit 9f879ee72bc75e0c1be88120a5616f4f8628ae11; https://github.com/mrcha033/complex-nn-signal/pull/1 +
docs/evidence/diversifier_track_ablation_transfer_v3.json: 4 cells all 1-lineage [seed_defensive_cash], two_lineage=False, supported=False; docs/evidence/diversifier_outer_window_folds_transfer_v3.json: 3 folds dSharpe=0.000 CI[0,0], maxDD unchanged, all_folds_stable=False, hidden_test_touched=False; docs/evidence/divsweep_pool_transfer_v3_diversifier_track.json: n_admitted=0; seed_crisis_hedge fitness -0.053 < floor 0.0; seed_equal_risk mean\\|corr\\| 0.520 > 0.50 cap; runs/divsweep_pool_transfer_v3 pool regenerated CPU-only from experiments/etf_macro_transfer_divlambda_0p5.yaml (max_generations=0, no LLM); alpha verify-data passes; full pytest 376 passed / 2 pre-existing fixture failures; alpha-factory commit 5d8056d20c493207215a9d679f492dc81588c598; https://github.com/mrcha033/alpha-factory/pull/1 +
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; .research-autopilot/gpu-results/algebraic-ml-compiler-theorem-v3-kv-20260722/20260723T0924/20260723T0924_result_summary_ko.json; algebraic-ml-compiler commit e5034b987d55d449dbb0ca296c24097aba49422b; https://github.com/mrcha033/algebraic-ml-compiler/pull/1 +
leave_one_shape_out on results/l40s_gpu3_bsr_dispatch.json (63 pts, 3 folds): parametric +2.6%/fs=0, nearest_lookup +22.2%/fs=9, exact-lookup=always_dense +15.0%, atlas +86.5%; leave_one_config_out reproduces nearest_lookup +22.2% identically; per-config crossovers: only (1024,4096,4096) wins (0.61-0.86 by block); 128/512 shapes never cross (None); src/generalization.py nearest_config_policy; tests/test_generalization.py (639 tests pass); sparse-lowrank-runtime commit 969deb5a1c941e91cf0ec8e162b732e6ec7198e9; https://github.com/mrcha033/sparse-lowrank-runtime/pull/1; src/fold_robustness.py + tests/test_fold_robustness.py (16 tests); full suite 655 passed; results/l40s_gpu3_bsr_dispatch.json: 3 shapes, 63 points, only (1024,4096,4096) has BSR wins (9/21); decisive-only pooled: parametric +3.2% vs always-dense/lookup/nearest +18.7%; pooled-over-all-folds +2.6% vs +15.0%; training jackknife on decisive shape: slowdown range +0.9%..+6.1% (spread 5.2pp), worst_false_sparse=1 when dropping (512,2048,2048,16); win_capture: 6/9 wins caught, 1.969 of 2.377 ms savings captured (82.8%), missed speedups 1.06/1.20/1.50; RESEARCH_NOTES.md 2026-07-26 robustness entry with reproduce command; PUBLICATION_PATH.md evidence boundary updated; sparse-lowrank-runtime commit be11743facdb7f921c8fa8fdc9ec4a946ad24b8b; https://github.com/mrcha033/sparse-lowrank-runtime/pull/1 +
rfuse modernizing userspace filesystem framework through scalable kernel userspace communication b3196db3 +
RFUSE: Modernizing Userspace Filesystem Framework through Scalable Kernel-Userspace Communication. FAST 2024. +
Publication record 1: Jinkyu Jeong, Hwanju Kim, Jeaho Hwang, Joonwon Lee, and Seungryoul Maeng, "Rigorous Rental Memory Management for Embedded Systems," ACM Transactions on Embedded Computing Systems, vol. 12, no. 1, article ID 43, Mar. 2013 +
RL-Watchdog: A Fast and Predictable SSD Liveness Watchdog on Storage Systems. USENIX ATC 2024. +
Rubix: Randomized Line-to-Row Mapping for Low-Overhead Rowhammer Mitigations. ASPLOS 2024. +
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ScalaAFA: Constructing User-Space All-Flash Array Engine with Holistic Designs. USENIX ATC 2024. +
Scalable Address Spaces using Concurrent Interval Skiplist. SOSP 2025. +
Bin Gao et al., "Scalable and Effective Page-table and TLB management on NUMA Systems", USENIX ATC 2024.
Source: https://www.usenix.org/conference/atc24/presentation/gao-bin-scalable
Verification basis: official_abstract.
Canonical evidence ID: canonical-paper-v2-92f46a49 +
Scalable Billion-point Approximate Nearest Neighbor Search Using SmartSSDs. USENIX ATC 2024. +
Scalable Far Memory: Balancing Faults and Evictions. SOSP 2025. +