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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Built the Spectral-Edge Criticality Controller as a local recurrent training intervention and evaluated it against a matched fixed-gain recurrent baseline on the dynamics track. Across 8 paired seeds, the controller reduced test MSE from 0.0031334 to 0.0015524, with delta_mean -0.0015810 and permutation p=0.0324, satisfying the benchmark's significant-win criterion. The trained-model mechanism signature confirmed the predicted effective edge target: observed 0.89979 versus predicted 0.9, relative error 0.024%.", "metrics": { "baseline": "Dynamics rnn_small, best lr=0.006, 12 epochs: mean test MSE 0.0031333728547906503", "idea": "Spectral-edge controller, best lr=0.006, target=0.9, alpha=0.25, 12 epochs: mean test MSE 0.0015523835318163037; paired delta -0.0015809893229743466; 7/8 wins; permutation p=0.0324; observed effective edge mean 0.899786445939283; mechanism confirmed=true" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "Only the required dynamics track was tested; vision, tabular, and sequence tracks were not run because the idea's structural match is stability/control dynamics. The controller uses a compact analytic local-Jacobian approximation for the manually unrolled GRU rather than a full exact Jacobian, and the benchmark used 12 training epochs and 400/200 samples rather than larger-scale training.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 12 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12 }, "mean": 0.010470356093719602 }, { "cfg": { "lr": 0.003, "epochs": 12 }, "mean": 0.005202302592806518 }, { "cfg": { "lr": 0.006, "epochs": 12 }, "mean": 0.0028439466550480574 } ], "full": { "per_seed": [ 0.0017406052211299539, 0.0037223200779408216, 0.002092725830152631, 0.0038201354909688234, 0.0038962536491453648, 0.0017676886636763811, 0.0043351962231099606, 0.0036920576822012663 ], "mean": 0.0031333728547906503 } }, "idea": { "best_cfg": { "lr": 0.006, "epochs": 12 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12 }, "mean": 0.013453479390591383 }, { "cfg": { "lr": 0.003, "epochs": 12 }, "mean": 0.004181064839940518 }, { "cfg": { "lr": 0.006, "epochs": 12 }, "mean": 0.0015980473981471732 } ], "per_seed": [ 0.000768338271882385, 0.0010514536406844854, 0.0033124624751508236, 0.0012599352048709989, 0.0007571351598016918, 0.0012769970344379544, 0.002315002726390958, 0.001677743741311133 ], "mean": 0.0015523835318163037 }, "comparison": { "delta_mean": -0.0015809893229743466, "per_seed_diffs": [ -0.0009722669492475688, -0.002670866437256336, 0.0012197366449981928, -0.0025602002860978246, -0.003139118489343673, -0.0004906916292384267, -0.0020201934967190027, -0.0020143139408901334 ], "idea_wins": 7, "n_pairs": 8, "p_value": 0.0324, "verdict": "idea better (significant)" }, "mechanism_signature": { "prediction": "controlled effective edge near target below 1", "predicted_target": 0.9, "observed_edge_mean": 0.899786445939283, "relative_error": 0.0002372822896855463, "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }