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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "The Koopman Hankel dual autoencoder was implemented as a local training intervention for the registered dynamics/rnn_small benchmark. It showed a modest lower test MSE than the tuned baseline, but the paired permutation test was nonsignificant (p=0.36915), so the harness verdict is no significant win. The trained-model mechanism signature was confirmed: latent spectral radius was 0.9021 and the measured future and transition residuals were finite.", "metrics": { "baseline": "mean test MSE 0.0006831642931501847, std 0.00017069130263284838", "idea": "mean test MSE 0.0006341310436255299, std 0.00015214794700785093", "delta_mean": -4.903324952465482e-05, "p_value": 0.36915, "idea_wins": "6/8" }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0001 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.0061489523795899 }, { "cfg": { "lr": 0.001, "weight_decay": 0.0001 }, "mean": 0.006141080608358607 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.0013683813158422709 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0001 }, "mean": 0.0013369002845138311 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0 }, "mean": 0.0007866571540944278 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0001 }, "mean": 0.0007708658231422305 } ], "full": { "mean": 0.0006831642931501847, "std": 0.00017069130263284838, "per_seed": [ 0.000798072200268507, 0.0005629754159599543, 0.0006793037755414844, 0.0010431119007989764, 0.0005458718515001237, 0.0004460137861315161, 0.000679348420817405, 0.0007106169941835105 ], "n": 8 } }, "idea": { "mean": 0.0006341310436255299, "std": 0.00015214794700785093, "per_seed": [ 0.0008724817307665944, 0.00042524098535068333, 0.0005125870229676366, 0.0008023462723940611, 0.0004432320420164615, 0.000654117320664227, 0.0006684261607006192, 0.0006946168141439557 ], "n": 8, "sweep": [ { "cfg": { "lr": 0.01, "weight_decay": 0.0001, "alpha": 0.1 }, "mean": 0.0008600900182500482 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0001, "alpha": 0.3 }, "mean": 0.0006531640028697439 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0001, "alpha": 1.0 }, "mean": 0.0008107436296995729 }, { "cfg": { "lr": 0.001, "weight_decay": 0.0001, "alpha": 0.3 }, "mean": 0.006068007613066584 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0001, "alpha": 0.3 }, "mean": 0.0012003584706690162 } ] }, "comparison": { "delta_mean": -4.903324952465482e-05, "idea_wins": 6, "n_pairs": 8, "per_seed_diffs": [ 7.44095304980874e-05, -0.00013773443060927093, -0.00016671675257384777, -0.00024076562840491533, -0.00010263980948366225, 0.0002081035345327109, -1.0922260116785765e-05, -1.6000180039554834e-05 ], "p_value": 0.36915, "mde": 0.00012044817185346833, "mde_rel_pct": 17.630923199756488, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": "dual Hankel training yields finite future-block reconstruction and latent transition residual", "observed_future_mse": 0.27331745624542236, "observed_past_mse": 0.21698212623596191, "observed_transition_mse": 0.01214107871055603, "latent_spectral_radius": 0.9021337628364563, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the registered built-in dynamics track and rnn_small architecture were tested. The run used 400 training samples, 100 test samples, 15 epochs, and a small hyperparameter sweep; Lorenz-63 transfer, long-horizon rollout quality, anomaly detection, and broader delay-depth studies were not tested.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }