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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented and evaluated lag-compensated learning-rate scheduling on the registered dynamics track with the shared rnn_small architecture. Mean test MSE improved from 0.00228095 to 0.00207082, but the paired permutation p-value was 0.1702, so this is not a significant win. The analytic 1/kappa check passed, while the trained-model mechanism signature was not confirmed.", "metrics": { "baseline": "best lr=0.006, weight_decay=0.0; mean MSE=0.00228094776684884; std=0.0008615682293713639", "idea": "best lr=0.006, weight_decay=0.0, kappa=8.0; mean MSE=0.002070821574307047; std=0.0007668985617216761; delta=-0.000210126192541793; p=0.1702" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 lag_scheduler_bench.py", "files": [ "lag_scheduler_bench.py", "bench_report.json", "bench_run.log" ], "limitations": "Only the registered dynamics track was tested. The trained-model diagnostic used an update-norm proxy rather than an explicit Jacobian/eigenvalue estimate, and the NN-scale 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": 0.0720269184675999 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.0028564660606207326 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 0.00228094776684884 } ], "full": { "mean": 0.00228094776684884, "std": 0.0008615682293713639, "per_seed": [ 0.002221147296950221, 0.001970731420442462, 0.0013024760410189629, 0.0042177895084023476, 0.0020735678263008595, 0.0015317687066271901, 0.002943465718999505, 0.0019866356160491705 ], "n": 8 } }, "idea": { "cfg": { "lr": 0.006, "weight_decay": 0.0, "kappa": 8.0 }, "mean": 0.002070821574307047, "std": 0.0007668985617216761, "per_seed": [ 0.0024689047131687403, 0.0014718936290591955, 0.0017854231409728527, 0.0037137719336897135, 0.0013458451721817255, 0.0012669704155996442, 0.0025451004039496183, 0.0019686631858348846 ], "n": 8 }, "comparison": { "delta_mean": -0.000210126192541793, "delta_std": 0.0003877800102460661, "p_value": 0.1702, "diffs": [ 0.0002477574162185192, -0.0004988377913832664, 0.00048294709995388985, -0.0005040175747126341, -0.000727722654119134, -0.00026479829102754593, -0.0003983653150498867, -1.797243021428585e-05 ] }, "mechanism_signature": { "math_check": { "kappas": [ 1, 2, 4, 8, 16 ], "fit_slope": 0.9997204301075259, "r2": 0.9999997339246215, "passed": true }, "trained_model_measurements": [ { "kappa": 2.0, "baseline_cross_step": 1.0, "idea_cross_step": 1.0, "observed_mean_effective_gap": 0.0 }, { "kappa": 4.0, "baseline_cross_step": 1.0, "idea_cross_step": 1.0, "observed_mean_effective_gap": 0.0 }, { "kappa": 8.0, "baseline_cross_step": 1.0, "idea_cross_step": 1.0, "observed_mean_effective_gap": 0.0 } ], "confirmed": false } }, "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }