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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented an end-to-end disturbance-augmented GRU on the structurally matched controlled-pendulum dynamics track, with identical learning-rate sweep values and eight paired seeds against the standard rnn_small baseline. The idea was slightly worse: baseline MSE 5.3983e-05 versus idea MSE 5.9412e-05, paired delta +5.4291e-06, permutation p=0.2879. The trained-model constant-bias signature was also not confirmed, showing a 1.09% increase rather than the predicted 80% reduction.", "metrics": { "baseline": "rnn_small, best lr=0.01, full 8-seed mean MSE=5.398313965088164e-05, std=3.768574167715651e-05", "idea": "disturbance GRU, rho=0.99 and lr=0.01, full 8-seed mean MSE=5.941226436334546e-05, std=3.632683313745886e-05", "paired_delta": "idea-baseline = +5.4291247124638176e-06; idea wins 4/8; permutation p=0.2879", "math_sanity": "rho persistence max absolute error <=1.11e-16; scalar stability boundary analytic 2.43902439 versus grid observed 2.439", "mechanism_signature": "constant test bias 0.25: baseline MAE 0.2497041, idea MAE 0.2524243, reduction fraction -0.01089, confirmed=false" }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.0008744152291910723 }, { "cfg": { "lr": 0.003 }, "mean": 0.00035537157964427024 }, { "cfg": { "lr": 0.01 }, "mean": 7.588289918203373e-05 } ], "full": { "mean": 5.398313965088164e-05, "std": 3.768574167715651e-05, "per_seed": [ 0.00014210454537533224, 8.642885950393975e-05, 3.601220669224858e-05, 3.8985985156614333e-05, 3.007560189871583e-05, 2.608529757708311e-05, 3.445964102866128e-05, 3.7712979974457994e-05 ], "n": 8 } }, "idea": { "mean": 5.941226436334546e-05, "std": 3.632683313745886e-05, "per_seed": [ 0.00013875614968128502, 9.052902169059962e-05, 4.028089097118936e-05, 7.056006870698184e-05, 4.287416595616378e-05, 2.4914010282373056e-05, 3.082741386606358e-05, 3.65563937521074e-05 ], "n": 8 }, "comparison": { "delta_mean": 5.4291247124638176e-06, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ -3.3483956940472126e-06, 4.1001621866598725e-06, 4.268684278940782e-06, 3.1574083550367504e-05, 1.2798564057447948e-05, -1.1712872947100556e-06, -3.632227162597701e-06, -1.1565862223505974e-06 ], "p_value": 0.2879, "mde": 9.929129282104334e-06, "mde_rel_pct": 18.393019276606996, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "math_sanity": { "persistence": [ { "rho": 0.5, "max_abs_error": 0.0, "half_life_pred": 1.0, "half_life_observed_first_integer": 1 }, { "rho": 0.9, "max_abs_error": 1.1102230246251565e-16, "half_life_pred": 6.57881347852842, "half_life_observed_first_integer": 7 }, { "rho": 0.99, "max_abs_error": 1.1102230246251565e-16, "half_life_pred": 68.96756393652842, "half_life_observed_first_integer": null } ], "stability": { "analytic_upper_gain": 2.4390243902439024, "observed_grid_upper_gain": 2.439, "boundary_abs_error": 2.4390243902328024 } }, "idea_sweep": [ { "cfg": { "lr": 0.001, "rho": 0.99 }, "mean": 0.0008760937489569187 }, { "cfg": { "lr": 0.003, "rho": 0.99 }, "mean": 0.00036263404035707936 }, { "cfg": { "lr": 0.01, "rho": 0.99 }, "mean": 8.503153276251396e-05 } ], "selected_idea_cfg": { "lr": 0.01, "rho": 0.99 }, "trained_model": { "test_constant_bias": 0.25, "baseline_mean_abs_error": 0.24970409274101257, "idea_mean_abs_error": 0.25242429971694946, "bias_error_reduction_fraction": -0.010893722029451158, "confirmed": false, "note": "trained benchmark models evaluated on perturbed targets; not the primary metric" } }, "protocol_notes": { "structural_match": "dynamics: controlled pendulum rollout", "paired_seeds": [ 0, 1, 2, 3, 4, 5, 6, 7 ], "budget": { "epochs": 18, "batch": 128, "n_train": 1200, "n_test": 400 }, "baseline_and_idea_lr_union": [ 0.001, 0.003, 0.01 ] } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "Only the built-in controlled-pendulum dynamics track was tested. The intervention used a scalar output disturbance initialized from the final observation rather than a full latent Kalman observer, did not learn rho, and did not test sinusoidal disturbances, multidimensional disturbance ranks, observer-gain sweeps, rollout horizons beyond the benchmark target, FLOPs, or wall-clock speed.", "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }