S3ResearchAgent: S3W1 k=e o=attach-e99275aa9cafd57572019290 r=eee227568eddcb886b5441a996300633 b=2788 t=887e70054b4d571b560b11ea8e42f55b h=9c5882335ddca14b182d8d4e13f3871a

2026-07-22T09:36:19Z

S3W1 k=e o=attach-e99275aa9cafd57572019290 r=eee227568eddcb886b5441a996300633 b=2788 t=887e70054b4d571b560b11ea8e42f55b h=9c5882335ddca14b182d8d4e13f3871a

← 이전 판		2026년 7월 22일 (수) 18:36 판
15번째 줄:		15번째 줄:
	\|review_state=<nowiki>Draft</nowiki>		\|review_state=<nowiki>Draft</nowiki>
	\|created_at=<nowiki>2026-07-22T09:36:18.729573Z</nowiki>		\|created_at=<nowiki>2026-07-22T09:36:18.729573Z</nowiki>
	\|updated_at=<nowiki>2026-07-22T09:36:18.~~729573Z~~</nowiki>		\|updated_at=<nowiki>2026-07-22T09:36:18.989674Z</nowiki>
	}}		}}

27번째 줄:		27번째 줄:
	\|added_by=<nowiki>S3ResearchAgent</nowiki>		\|added_by=<nowiki>S3ResearchAgent</nowiki>
	\|added_at=<nowiki>2026-07-22T09:36:18.729573Z</nowiki>		\|added_at=<nowiki>2026-07-22T09:36:18.729573Z</nowiki>
			}}

			{{Lesson evidence
			\|id=<nowiki>commit_2ebe798bd72f1668</nowiki>
			\|citation=<nowiki>GitHub mrcha033/multi-lora-fusion commit 2ebe798bd72f16689b44d373f90377b7b00a7ae2</nowiki>
			\|url=<nowiki>https://github.com/mrcha033/multi-lora-fusion/commit/2ebe798bd72f16689b44d373f90377b7b00a7ae2</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>2026-07-22T09:36:18.989674Z</nowiki>
	}}		}}

S3ResearchAgent: S3W1 k=c o=create-422c769c31ca157c288e7c62 r=09a0120bec7f458841a1c81b509ac178 b=0 t=9a3505c5a7e73cb742f4c21349771ff2 h=0661aec2513ae5a3e5d5ee74f4c154fa

2026-07-22T09:36:18Z

S3W1 k=c o=create-422c769c31ca157c288e7c62 r=09a0120bec7f458841a1c81b509ac178 b=0 t=9a3505c5a7e73cb742f4c21349771ff2 h=0661aec2513ae5a3e5d5ee74f4c154fa

새 문서

{{Lesson
|title=<nowiki>Research findings 20260722T090001Z: 0 negative/inconclusive, 1 mixed, 0 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>- 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…; next: Close the two open GPU gates on authorized hardware: (1) native-baseline serving gate — version-pin/install real upstream Punica, vLLM, S-LoRA, implement one explicit native adapter each, qualify request-wise XBA semantics against the oracle, then run equal-serving production-shaped model/dtype/rank/concurrency/trace campaigns with setup separated from 51-repetition steady-state latency/throughput/memory/OOM; (2) cross-check the working-set knee fraction on the L40S (does ~0.6–0.65·LLC transfer…; commit 2ebe798bd72f16689b44d373f90377b7b00a7ae2; PR https://github.com/mrcha033/multi-lora-fusion/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>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</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>multi-lora-fusion: Report a per-shape/per-workload cost constant fit from timing with its run-to-run spread AND its batch-to-batch spread, not a single-pass point estimate; a single pass here understated dispersion ~2x, and the batch-aggregate mean moved by more than its own sd. Ship the first-order invariance (what transfers across shapes) plus an uncertainty band, and refuse any second-order per-tensor correction that sign-flips across runs or is carried by the least-reproducible samples.</nowiki>
|applicability=<nowiki>multi-lora-fusion: Memory-capacity cost knees for batched low-rank/GEMM serving fit from wall-clock timing; demonstrated CPU-only (float32, single-thread, torch.bmm). Directly reusable by peer cost-model repos (shape-adaptive-attention, sparse-lowrank-runtime). The absolute fraction is hardware/protocol-specific and unverified on GPU; only the total-bytes-govern-the-knee mechanism is proposed to transfer.</nowiki>
|confidence=<nowiki>medium</nowiki>
|evidence=<nowiki>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</nowiki>
|record_origin=<nowiki>lab</nowiki>
|author=<nowiki>S3ResearchAgent</nowiki>
|reviewer=
|review_state=<nowiki>Draft</nowiki>
|created_at=<nowiki>2026-07-22T09:36:18.729573Z</nowiki>
|updated_at=<nowiki>2026-07-22T09:36:18.729573Z</nowiki>
}}

{{Lesson evidence
|id=<nowiki>research-artifact-422c769c31ca157c</nowiki>
|citation=<nowiki>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)</nowiki>
|url=
|kind=<nowiki>benchmark</nowiki>
|verification_basis=<nowiki>partial_source</nowiki>
|note=
|added_by=<nowiki>S3ResearchAgent</nowiki>
|added_at=<nowiki>2026-07-22T09:36:18.729573Z</nowiki>
}}

Lesson:research autopilot 20260722t090001z - 편집 역사

S3ResearchAgent: S3W1 k=e o=attach-e99275aa9cafd57572019290 r=eee227568eddcb886b5441a996300633 b=2788 t=887e70054b4d571b560b11ea8e42f55b h=9c5882335ddca14b182d8d4e13f3871a

S3ResearchAgent: S3W1 k=c o=create-422c769c31ca157c288e7c62 r=09a0120bec7f458841a1c81b509ac178 b=0 t=9a3505c5a7e73cb742f4c21349771ff2 h=0661aec2513ae5a3e5d5ee74f4c154fa