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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Built a cyclic recurrent dynamics model with Fourier-mode barrier regularization and compared it against the identical model without the barrier. The idea was better on all 8 paired seeds with delta_mean=-6.57e-06 and permutation p=0.0081, satisfying the benchmark's significant-win criterion, although the relative improvement was only about 0.16%. The trained-model signature was not confirmed: observed slopes differed from -kappa*mu by up to 56.8%, plausibly due to the finite discrete update and tanh Jacobian.", "metrics": { "baseline_mean_mse": 0.004087189445272088, "idea_mean_mse": 0.004080623359186575, "delta_mean": -6.566086085513234e-06, "p_value": 0.0081, "idea_wins": 8, "n_pairs": 8, "baseline_best_cfg": { "lr": 0.01, "weight_decay": 0.0 }, "idea_best_cfg": { "lr": 0.01, "weight_decay": 0.0 }, "mechanism_signature_confirmed": false, "mechanism_max_relative_error": 0.5679249704438308 }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "cyclic_rnn", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.073138820938766 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.01337075512856245 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0 }, "mean": 0.004462164011783898 } ], "full": { "mean": 0.004087189445272088, "std": 0.0010007874399045344, "per_seed": [ 0.005726089235395193, 0.0033913287334144115, 0.004283653572201729, 0.004447584506124258, 0.0049942112527787685, 0.0028478815220296383, 0.002599592786282301, 0.004407173953950405 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.07314319256693125 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.013368312735110521 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0 }, "mean": 0.004457225266378373 } ], "full": { "mean": 0.004080623359186575, "std": 0.0010004881538153208, "per_seed": [ 0.005722619593143463, 0.003379821078851819, 0.004280869383364916, 0.004445591010153294, 0.004983154591172934, 0.0028435608837753534, 0.0025958793703466654, 0.0043934909626841545 ], "n": 8 } }, "comparison": { "delta_mean": -6.566086085513234e-06, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -3.469642251729965e-06, -1.1507654562592506e-05, -2.784188836812973e-06, -1.9934959709644318e-06, -1.1056661605834961e-05, -4.320638254284859e-06, -3.7134159356355667e-06, -1.368299126625061e-05 ], "p_value": 0.0081, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "q": 0, "kappa": 0.15, "max_relative_error": 0.5679249704438308, "confirmed": false, "modes": [ 1, 2, 3 ], "systems": [ "baseline", "idea" ] } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_fourier_bench.py", "files": [ "stage2_fourier_bench.py", "bench_report.json" ], "limitations": "Only the built-in dynamics pendulum track was tested; no sequence copy-memory task, larger hidden sizes, alternative q profiles, explicit unstable-mode training, or longer horizons were evaluated. The mechanism probe used a simplified zero-input perturbation around the trained recurrent state rather than the full task trajectory Jacobian.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 0, "system_judged": true }