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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Implemented a matched delayed recurrent dynamics system with explicit lag taps and a periodic/antiperiodic bifurcation-margin penalty, using the bench dynamics track and identical training budgets. Across 8 paired seeds, the idea reduced test MSE from 0.006980 to 0.006769 (delta -0.000211, permutation p=0.0311), satisfying the benchmark's significant-win criterion. The core analytical scaling check passed extremely strongly, but the trained-model behavioral signature was not confirmed quantitatively, so the practical mechanism interpretation remains tentative.", "metrics": { "baseline": "Dynamics rnn_small delayed-RNN baseline: best lr 0.006; 8-seed test MSE mean 0.0069797673, std 0.00379324.", "idea": "Bifurcation-monitored delayed RNN: lr 0.006; 8-seed test MSE mean 0.0067691735; paired delta -0.0002105938; 6/8 wins; permutation p=0.0311.", "math_check": "Periodic root 1.8691588785; residual smallest singular value 5.32e-16; inverse tap-scale prediction max relative error 6.0e-14 across scales 0.5, 1, and 2.", "mechanism_signature": "Predicted trained-model margin mean 0.8796 versus observed input sensitivity mean 0.00762; correlation 0.1926; confirmed=false." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json", "bench_run.log", "math_check.json" ], "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": 0.0015, "weight_decay": 0.0 }, "mean": 0.0805496373 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.0233472821 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 0.0081357223 } ], "full": { "mean": 0.0069797673, "std": 0.0037932413, "per_seed": [ 0.003800632, 0.0048443531, 0.0075699734, 0.0163279306, 0.0046874899, 0.0047464455, 0.0080394745, 0.0058218394 ], "n": 8 } }, "idea": { "per_seed": [ 0.003616119, 0.0048443531, 0.0075232326, 0.0153941177, 0.0045921714, 0.004602334, 0.0077592204, 0.0058218394 ], "mean": 0.0067691735 }, "comparison": { "delta_mean": -0.0002105938, "idea_wins": 6, "n_pairs": 8, "p_value": 0.0311, "mde": 0.0002571057, "mde_rel_pct": 3.6836, "verdict": "idea better (significant)", "system_worked": true }, "idea_sweep": [ { "cfg": { "lr": 0.0015, "weight_decay": 0.0 }, "mean": 0.0689709377 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.021789389 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 0.0078444556 } ], "mechanism_signature": { "window": 8, "predicted_margin_mean": 0.8796176, "observed_input_sensitivity_mean": 0.00762107, "predicted_vs_observed_correlation": 0.19264, "confirmed": false }, "protocol_notes": "8 paired seeds; baseline sweep on seeds 0-3 plus full best-configuration evaluation; idea used the same three learning rates and epochs." }, "limitations": "The benchmark used the built-in 8-step actuated-pendulum dynamics task rather than long-horizon sequence memory. The practical monitor uses a zero-reference linearized augmented transition rather than full trajectory-dependent JVP/Lanczos estimation, and the mechanism signature did not meet the confirmation threshold. Only three learning rates, one penalty weight, one tap configuration, and 18 epochs were tested; no FLOP-normalized or long-horizon oscillation study was performed.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 0, "system_judged": true }