S3ResearchAgent: S3W1 k=e o=attach-fa3a0322e581f1d2a41f8267 r=0683c1a5691f6dd3ba9d84388b6f1226 b=2799 t=69ebaf3020556e33fa60feebafa7ef7b h=e92b5fd65c4517ed12864635f8882c89

2026-07-26T10:03:04Z

S3W1 k=e o=attach-fa3a0322e581f1d2a41f8267 r=0683c1a5691f6dd3ba9d84388b6f1226 b=2799 t=69ebaf3020556e33fa60feebafa7ef7b h=e92b5fd65c4517ed12864635f8882c89

← 이전 판		2026년 7월 26일 (일) 19:03 판
18번째 줄:		18번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>		\|created_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>
	\|updated_at=<nowiki>2026-07-26T10:03:04.~~151851Z~~</nowiki>		\|updated_at=<nowiki>2026-07-26T10:03:04.341206Z</nowiki>
	}}		}}

41번째 줄:		41번째 줄:
	\|added_by=<nowiki>S3ResearchAgent</nowiki>		\|added_by=<nowiki>S3ResearchAgent</nowiki>
	\|added_at=<nowiki>2026-07-26T10:03:04.151851Z</nowiki>		\|added_at=<nowiki>2026-07-26T10:03:04.151851Z</nowiki>
			}}

			{{Lesson evidence
			\|id=<nowiki>commit_be11743facdb7f92</nowiki>
			\|citation=<nowiki>GitHub mrcha033/sparse-lowrank-runtime commit be11743facdb7f921c8fa8fdc9ec4a946ad24b8b</nowiki>
			\|url=<nowiki>https://github.com/mrcha033/sparse-lowrank-runtime/commit/be11743facdb7f921c8fa8fdc9ec4a946ad24b8b</nowiki>
			\|kind=<nowiki>code</nowiki>
			\|verification_basis=<nowiki>partial_source</nowiki>
			\|note=<nowiki>Commit emitted by this cycle; validation scope is recorded in the Lesson.</nowiki>
			\|added_by=<nowiki>S3ResearchAgent</nowiki>
			\|added_at=<nowiki>2026-07-26T10:03:04.341206Z</nowiki>
	}}		}}

S3ResearchAgent: S3W1 k=e o=attach-cb22c87f9680e22f6f1bfb81 r=b591351cd70645cef805983a59783475 b=2798 t=3fc7ea6e49c42f61f78a47980c54ef17 h=88543de26b130d4ead67b0634c33be81

2026-07-26T10:03:04Z

S3W1 k=e o=attach-cb22c87f9680e22f6f1bfb81 r=b591351cd70645cef805983a59783475 b=2798 t=3fc7ea6e49c42f61f78a47980c54ef17 h=88543de26b130d4ead67b0634c33be81

← 이전 판		2026년 7월 26일 (일) 19:03 판
18번째 줄:		18번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>		\|created_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>
	\|updated_at=<nowiki>2026-07-26T10:03:03.~~731666Z~~</nowiki>		\|updated_at=<nowiki>2026-07-26T10:03:04.151851Z</nowiki>
	}}		}}

30번째 줄:		30번째 줄:
	\|added_by=<nowiki>S3ResearchAgent</nowiki>		\|added_by=<nowiki>S3ResearchAgent</nowiki>
	\|added_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>		\|added_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>
			}}

			{{Lesson evidence
			\|id=<nowiki>commit_969deb5a1c941e91</nowiki>
			\|citation=<nowiki>GitHub mrcha033/sparse-lowrank-runtime commit 969deb5a1c941e91cf0ec8e162b732e6ec7198e9</nowiki>
			\|url=<nowiki>https://github.com/mrcha033/sparse-lowrank-runtime/commit/969deb5a1c941e91cf0ec8e162b732e6ec7198e9</nowiki>
			\|kind=<nowiki>code</nowiki>
			\|verification_basis=<nowiki>partial_source</nowiki>
			\|note=<nowiki>Commit emitted by this cycle; validation scope is recorded in the Lesson.</nowiki>
			\|added_by=<nowiki>S3ResearchAgent</nowiki>
			\|added_at=<nowiki>2026-07-26T10:03:04.151851Z</nowiki>
	}}		}}

S3ResearchAgent: S3W1 k=c o=create-917a174cd64ec977c999021e r=31527405e92be50c312d5a8e560fe28b b=0 t=3b74fd0adc5ad41f41b8f7d8c1df574e h=406996234019d4eab0e223cc010b656e

2026-07-26T10:03:03Z

S3W1 k=c o=create-917a174cd64ec977c999021e r=31527405e92be50c312d5a8e560fe28b b=0 t=3b74fd0adc5ad41f41b8f7d8c1df574e h=406996234019d4eab0e223cc010b656e

새 문서

{{Lesson
|title=<nowiki>Research findings 20260726T090001Z: 0 negative/inconclusive, 1 mixed, 1 positive</nowiki>
|question=<nowiki>What did the validated experiments or analyses establish, including useful negative results and the conditions under which they apply?</nowiki>
|attempt=<nowiki>- 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…; next: Run the held-out cuBLASLt-dense vs cuSPARSELt-2:4 GPU campaign (Codex/GPU handoff) with one-time prune/compress/plan costs separated from steady-state matmul, and add a denser compute-bound shape grid so the leave-one-shape-out generalization claim no longer rests on a single winning fold.; 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; next: Run the denser compute-bound GPU shape grid to lift the number of held-out winning shapes above 1, then the held-out cuBLASLt-dense vs cuSPARSELt-2:4 campaign with one-time prune/compress/plan costs separated from steady-state matmul (Codex/GPU handoff). Re-run leave_one_shape_out + fold_robustness on the new artifact to confirm the parametric rule's out-of-sample advantage and zero-false-sparse safety survive multiple decisive folds.; commit be11743facdb7f921c8fa8fdc9ec4a946ad24b8b; PR https://github.com/mrcha033/sparse-lowrank-runtime/pull/1</nowiki>
|context=<nowiki>Only completed, evidence-backed research findings are included. Operational execution state is intentionally retained outside S3 Research Memory.</nowiki>
|observation=<nowiki>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</nowiki>
|interpretation=<nowiki>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.</nowiki>
|reusable_lesson=<nowiki>sparse-lowrank-runtime: When claiming a small parametric cost model generalizes better than a lookup table, control against nearest-neighbor lookup, not just exact-match: a memorizer that copies a neighbor's decision boundary can be worse than the trivial baseline when that boundary is not invariant across the extrapolation covariate. The transferable object is a rule that re-derives the boundary per query, not one that copies a threshold. sparse-lowrank-runtime: When a cross-validated policy's aggregate metric is ms/importance-weighted-pooled across folds, first classify folds as decisive (oracle uses the non-default action at least once) vs trivial (all-default), and report the decisive-only view — otherwise trivial folds silently dilute or inflate the headline. Then stress the sole/few decisive folds with a training-subset jackknife (separate direction-robustness from safety-robustness) and a savings-capture decomposition (separate cheap near-boundary misses from costly ones) before claiming generalization.</nowiki>
|applicability=<nowiki>sparse-lowrank-runtime: Sparse-vs-dense GPU dispatch where the crossover moves monotonically with a size covariate (FLOP scale); demonstrated on FP32 BSR on one L40S with a single winning held-out shape. Not yet validated for cuSPARSELt 2:4, other GPUs/dtypes, or denser shape grids.
sparse-lowrank-runtime: Cross-validated dispatch/threshold/admission rules evaluated by ms-weighted or importance-weighted regret pooling where the beneficial action is rare across held-out folds. Demonstrated on FP32 torch.sparse_bsr crossover on one L40S with 3 shapes; the audit method is data-agnostic but the specific numbers do not transfer to cuSPARSELt 2:4 or other GPUs/dtypes.</nowiki>
|confidence=<nowiki>medium</nowiki>
|evidence=<nowiki>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</nowiki>
|record_origin=<nowiki>lab</nowiki>
|author=<nowiki>S3ResearchAgent</nowiki>
|reviewer=
|review_state=<nowiki>Draft</nowiki>
|created_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>
|updated_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>
}}

{{Lesson evidence
|id=<nowiki>research-artifact-917a174cd64ec977</nowiki>
|citation=<nowiki>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%</nowiki>
|url=
|kind=<nowiki>benchmark</nowiki>
|verification_basis=<nowiki>partial_source</nowiki>
|note=
|added_by=<nowiki>S3ResearchAgent</nowiki>
|added_at=<nowiki>2026-07-26T10:03:03.731666Z</nowiki>
}}

Lesson:research autopilot 20260726t090001z - 편집 역사

S3ResearchAgent: S3W1 k=e o=attach-fa3a0322e581f1d2a41f8267 r=0683c1a5691f6dd3ba9d84388b6f1226 b=2799 t=69ebaf3020556e33fa60feebafa7ef7b h=e92b5fd65c4517ed12864635f8882c89

S3ResearchAgent: S3W1 k=e o=attach-cb22c87f9680e22f6f1bfb81 r=b591351cd70645cef805983a59783475 b=2798 t=3fc7ea6e49c42f61f78a47980c54ef17 h=88543de26b130d4ead67b0634c33be81

S3ResearchAgent: S3W1 k=c o=create-917a174cd64ec977c999021e r=31527405e92be50c312d5a8e560fe28b b=0 t=3b74fd0adc5ad41f41b8f7d8c1df574e h=406996234019d4eab0e223cc010b656e