Стенд-проверка (stage-2) · промт оператора:

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Implemented the PSD Spectral CNN block as a matched replacement for the middle spatial convolution in a CIFAR-10 cnn_small system, using circular BB convolution and the canonical bench training loop. The trained PSD system had higher error than the swept baseline (0.8028125 vs 0.7859375; paired delta +0.016875), with permutation p=0.18975, so there was no significant win. The trained-model Fourier signature confirmed a nonnegative response on the tested 16x16 frequency grid, but this mechanism confirmation did not improve task performance.", "metrics": { "baseline": "Vision matched system, best lr=0.006, weight_decay=0.0; 8-seed test error mean 0.7859374881, std 0.0261885418.", "idea": "PSD BB system, best lr=0.003; 8-seed test error mean 0.8028124869, std 0.0279071407; paired delta +0.0168749988, p=0.18975, 4/8 paired wins.", "mechanism_signature": "Trained PSD model: predicted minimum Fourier eigenvalue 0.0; observed minimum eigenvalue 7.6315516e-06 on a 16x16 frequency grid; observed_nonnegative=true; confirmed=true." }, "bench_report": { "bench_version": 1, "track": "vision", "model": "cnn_small_psd_matched", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.7862499952 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.7881249636 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 0.7812499851 } ], "full": { "mean": 0.7859374881, "std": 0.0261885418, "per_seed": [ 0.7699999809, 0.7524999976, 0.7899999619, 0.8125, 0.7425000072, 0.8149999976, 0.8100000024, 0.7949999571 ], "n": 8 } }, "idea": { "mean": 0.8028124869, "std": 0.0279071407, "per_seed": [ 0.782499969, 0.75, 0.8125, 0.8100000024, 0.8399999738, 0.8100000024, 0.8349999785, 0.782499969 ], "n": 8 }, "comparison": { "delta_mean": 0.0168749988, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ 0.0124999881, -0.0024999976, 0.0225000381, -0.0024999976, 0.0974999666, -0.0049999952, 0.0249999762, -0.0124999881 ], "p_value": 0.18975, "mde": 0.0294937822, "mde_rel_pct": 3.75268805, "verdict": "no significant win", "system_worked": false }, "idea_sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.8043749779 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.8028124869 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 0.8074999824 } ], "track_rationale": "The idea changes a spatial multichannel convolution, so CIFAR-10 vision/cnn_small is structurally matched.", "mechanism_signature": { "predicted_min_eigenvalue": 0.0, "observed_min_eigenvalue": 7.6315516e-06, "predicted_nonnegative": true, "observed_nonnegative": true, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the fixed small CIFAR-10 vision track and 12 training epochs were tested. The Fourier signature used a finite 16x16 frequency grid; no exhaustive continuous-frequency proof, high-frequency gain, Gaussian-noise robustness, gradient-spike, FLOP, or larger-CIFAR experiment was run.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }