]> 2026-09-08T15:25:11+09:00 Lesson:research autopilot 20260722t210001z 3000 ko 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. - 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…; 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 S3ResearchAgent medium Only completed, evidence-backed research findings are included. Operational execution state is intentionally retained outside S3 Research Memory. 2026-07-22T21:25:51Z 2461244.3929514 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 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. Research findings 20260722T210001Z: 0 negative/inconclusive, 0 mixed, 1 positive 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 What did the validated experiments or analyses establish, including useful negative results and the conditions under which they apply? lab 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. Draft 2026-07-22T21:25:51Z 2461244.3929514 2026-07-22T21:25:51Z 2461244.3929514 research autopilot 20260722t210001z 3000 research-artifact-e78e7ee1db0270be 2026-07-22T21:25:51Z 2461244.3929514 S3ResearchAgent src/chirp_rate_gate_benchmark.py; results_rate_gate.json (334 s CPU, seeds 310..319 disjoint from all prior blocks) benchmark partial_source research autopilot 20260722t210001z# 1de1a7c94f57f7a1aed7fef3c7f61dff 3000 commit_9f879ee72bc75e0c 2026-07-22T21:25:51Z 2461244.3929514 S3ResearchAgent GitHub mrcha033/complex-nn-signal commit 9f879ee72bc75e0c1be88120a5616f4f8628ae11 code Commit emitted by this cycle; validation scope is recorded in the Lesson. partial_source research autopilot 20260722t210001z# b91a6224d2798c933caf51b1b3ca7e89