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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "The quadratic ordered-lag Volterra compensator significantly improved the shared rnn_small system on the registered dynamics track. Mean test MSE decreased from 0.0003940513 to 0.0002314957, all 8 paired seeds improved, and the permutation p-value was 0.0081. The trained-model mechanism signature did not confirm ideal quadratic scaling, and the idea used more parameters.", "metrics": { "baseline": "Tuned rnn_small baseline: mean test MSE 0.0003940512724511791 over 8 seeds; best lr=0.006; parameters=13313.", "idea": "Same rnn_small GRU plus zero-initialized causal ordered-lag quadratic compensator: mean test MSE 0.00023149568187363911 over 8 seeds; best lr=0.006; parameters=15617; 8/8 paired wins; delta=-0.00016255559057754; p=0.0081." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the registered dynamics track was tested. The comparison was not parameter-matched because the compensator adds 2304 parameters. Cubic order, long-horizon rollout beyond the built-in target, wall-clock/FLOP measurements, and matched-width controls were not tested. The trained-model mechanism signature was not confirmed.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "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": 0.0012006517936242744 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.0006976972363190725 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 0.00046024548646528274 } ], "full": { "mean": 0.0003940512724511791, "std": 0.00011379624857509869, "per_seed": [ 0.00040922293555922806, 0.0005125142051838338, 0.0002987775078509003, 0.0006204672972671688, 0.00030420164694078267, 0.00036726761027239263, 0.0002520524722058326, 0.00038790650432929397 ], "n": 8 } }, "idea": { "mean": 0.00023149568187363911, "std": 6.189006390488855e-05, "per_seed": [ 0.0002818358188960701, 0.00030632453854195774, 0.00019047960813622922, 0.00032223673770204186, 0.00021427450701594353, 0.00020972064521629363, 0.00012757130025420338, 0.00019952229922637343 ], "n": 8 }, "comparison": { "delta_mean": -0.00016255559057754, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.00012738711666315794, -0.0002061896666418761, -0.00010829789971467108, -0.0002982305595651269, -8.992713992483914e-05, -0.000157546965056099, -0.00012448117195162922, -0.00018838420510292053 ], "p_value": 0.0081, "mde": 5.645396435641711e-05, "mde_rel_pct": 14.326552990235925, "verdict": "idea better (significant)", "system_worked": true }, "math_check": { "ordered_pairs_L8": 36, "remainder_over_quadratic_loglog_slope": 1.0000000000000004, "stable_linear_radius": true }, "idea_sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.0008330114796990529 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.00044643200089922175 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 0.00027521917581907474 } ], "idea_best_cfg": { "lr": 0.006, "weight_decay": 0.0 }, "parameter_counts": { "baseline": 13313, "idea": 15617 }, "custom_track": null, "mechanism_signature": { "predicted_power": 2.0, "observed_power_from_trained_model": 1.0769089683295592, "scale_ratio_observed": 2.1095115232492603, "hidden_feature_correlation": 0.9850645661354065, "test_metric_for_signature_run": 0.0002818358188960701, "confirmed": false } }, "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 0, "system_judged": true }