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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the residual-screened Koopman latent bottleneck on the structurally matched dynamics track using the same rnn_small GRU/head for both systems, differing only by the residual regularization loss. The idea reduced mean test MSE from 0.0011708620 to 0.0011421148 across 8 paired seeds, with 6/8 wins, but the permutation p-value was 0.05535, so there was no significant win. The trained-model mechanism signature was confirmed: observed retained fraction 0.75 matched the predicted 75th-percentile screening fraction.", "metrics": { "baseline": "lr=0.006; 8-seed test MSE mean=0.0011708620, std=0.0004510333", "idea": "lr=0.006, alpha=0.03, quantile=0.75; 8-seed test MSE mean=0.0011421148, std=0.0004499894; paired delta=-0.0000287472; p=0.05535; 6/8 wins", "mechanism_signature": "mean residual=0.6372733; retained modes=48/64; observed retained fraction=0.75; predicted retained fraction=0.75; latent transition fit RMSE=0.0157470; confirmed=true" }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.0061043375 }, { "cfg": { "lr": 0.003 }, "mean": 0.001739939 }, { "cfg": { "lr": 0.006 }, "mean": 0.0012507252 } ], "full": { "mean": 0.001170862, "std": 0.0004510333, "per_seed": [ 0.0009026774, 0.0019194457, 0.0005950154, 0.0015857624, 0.0008504732, 0.000836688, 0.001670784, 0.0010060502 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.006, "alpha": 0.03, "quantile": 0.75 }, "mean": 0.0011421148, "std": 0.0004499894, "per_seed": [ 0.0008761535, 0.0019359904, 0.000607462, 0.0015396702, 0.0007932198, 0.0007940694, 0.0015995833, 0.0009907696 ], "n": 8 }, "comparison": { "delta_mean": -2.87472e-05, "idea_wins": 6, "n_pairs": 8, "per_seed_diffs": [ -2.65238e-05, 1.65447e-05, 1.24467e-05, -4.60922e-05, -5.72533e-05, -4.26187e-05, -7.12007e-05, -1.52806e-05 ], "p_value": 0.05535, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "mean_residual": 0.6372733, "retained_modes": 48, "retained_fraction": 0.75, "fit_rmse": 0.015747, "latent_step_rms": 0.1607116, "predicted_retained_fraction": 0.75, "confirmed": true }, "protocol_notes": { "baseline_grid": [ 0.001, 0.003, 0.006 ], "idea_grid": [ 0.001, 0.003, 0.006 ], "epochs": 15, "batch": 128, "n_train": 400, "n_test": 400, "architecture_match": "same rnn_small GRU and head; only residual loss differs", "structural_match": "controlled pendulum dynamics" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_koopman_bench.py", "files": [ "stage2_koopman_bench.py", "bench_report.json" ], "limitations": "The experiment used the built-in controlled-pendulum dynamics track with 400 training and 400 test examples and 15 epochs, rather than the larger default dataset or a long-horizon rollout metric. Only the residual-loss intervention was tested; no detached periodic refresh schedule, confidence weights, complex-pair handling, or separate decoder bottleneck was evaluated.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }