Lesson:research autopilot 20260722t210001z
| 제목 | Research findings 20260722T210001Z: 0 negative/inconclusive, 0 mixed, 1 positive |
|---|---|
| 궁금했던 점 | What did the validated experiments or analyses establish, including useful negative results and the conditions under which they apply? |
| 해본 것 | 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…; next: Externally timestamped protocol on captured RF/radar with genuine intra-window frequency ramps, session-held-out, vs a tuned classical chirp/keystone baseline with detection/false-alarm curves — the only remaining path from this synthetic mechanistic result to a top-venue performance claim. This requires real data acquisition (out of scope for CPU synthetic work). A smaller CPU follow-up: replace the fixed lambda with a learnable per-atom rate gate or SNR-scheduled lambda and test whether it cl…; commit 9f879ee72bc75e0c1be88120a5616f4f8628ae11; PR https://github.com/mrcha033/complex-nn-signal/pull/1 |
| 당시 조건 | Only completed, evidence-backed research findings are included. Operational execution state is intentionally retained outside S3 Research Memory. |
| 실제 결과 | 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 |
| 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 | complex-nn-signal: An L1 penalty on an optional model degree of freedom behaves as a data-adaptive gate: because it competes against a cross-entropy data-gradient whose magnitude scales with how useful that DOF is for the training distribution, one fixed penalty weight nulls the DOF where it is useless and keeps it where it is load-bearing. When SGD leaves an optional DOF diffusely non-zero, a fixed L1 nudge can recover the sparse optimum without a per-distribution hyperparameter; verify noise dependence, since under heavy noise the DOF fits noise and the same weight may not null. |
| 언제 맞는지 | complex-nn-signal: Synthetic single-tone LFM chirp classification, CPU, K=4/L=16, one optimizer, descriptive SESOI verdicts, not externally preregistered and no captured data. The full escape holds at SNR=0; the SNR=-5 case only partially closes. The adaptive-gate lesson generalizes to models with an optional/nuisance DOF and a data-scaled loss gradient, but the specific accuracy numbers and the exact nulling lambda are task- and SNR-specific. |
| 신뢰도 | 중간 |
| 관련 자료 | 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 |
| 자료 출처 | 우리 기록 |
| 작성자 | S3ResearchAgent |
| 처음 작성한 시각 (UTC) | 2026-07-22T21:25:51.366741Z |
| 마지막 수정 시각 (UTC) | 2026-07-22T21:25:51.366741Z |
근거 research-artifact-e78e7ee1db0270be: src/chirp_rate_gate_benchmark.py; results_rate_gate.json (334 s CPU, seeds 310..319 disjoint from all prior blocks)
벤치마크 · 확인 범위: 일부 자료 확인 · S3ResearchAgent · 2026-07-22T21:25:51.366741Z