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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the input-aware contracting metric as a training-loss intervention on the structurally matched dynamics/rnn_small benchmark, including a positive-definite learned metric and total derivative contribution from state and control rate. Across 8 paired seeds, test MSE improved slightly from 0.0850381 to 0.0812273, but the permutation p-value was 0.3924, so there was no significant win. The trained certificate remained violated on nearly all probe points, and the NN-scale quantitative mechanism prediction was not confirmed.", "metrics": { "baseline": "dynamics/rnn_small, tuned lr=0.006, test MSE mean=0.0850381, std=0.0185276", "idea": "input-aware metric penalty, lr=0.006, rho=0.05, test MSE mean=0.0812273, std=0.0218786; paired delta=-0.00381088, p=0.3924; idea wins=4/8" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "The benchmark used a reduced but equal-budget dynamics dataset of 240 training and 120 test samples with 4 epochs because higher-order autograd through the GRU was expensive. The local certificate is a scalar approximation applied to the final theta/control slice of rnn_small rather than a full two-state NODE vector-field certificate; robust disturbance terms were omitted. The analytical scalar derivative check passed, but it is not counted as NN-scale mechanism confirmation; a baseline trained-model certificate aggregate was not retained in the final report.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "rho": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "rho": 0.0 }, "mean": 0.5870515555143356 }, { "cfg": { "lr": 0.003, "rho": 0.0 }, "mean": 0.19268612749874592 }, { "cfg": { "lr": 0.006, "rho": 0.0 }, "mean": 0.0888435747474432 } ], "full": { "mean": 0.08503814833238721, "std": 0.01852758683212337, "per_seed": [ 0.08398815989494324, 0.09531869739294052, 0.0957954153418541, 0.08027202636003494, 0.06067569926381111, 0.11256278306245804, 0.09799148142337799, 0.053700923919677734 ], "n": 8 } }, "idea": { "mean": 0.08122727181762457, "std": 0.021878604412979827, "per_seed": [ 0.06868152320384979, 0.08553639054298401, 0.10347606986761093, 0.057179175317287445, 0.059706274420022964, 0.11696434020996094, 0.0995219275355339, 0.05875247344374657 ], "n": 8, "cfg": { "lr": 0.006, "rho": 0.05 } }, "comparison": { "delta_mean": -0.00381087651476264, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ -0.015306636691093445, -0.009782306849956512, 0.007680654525756836, -0.023092851042747498, -0.000969424843788147, 0.004401557147502899, 0.0015304461121559143, 0.005051549524068832 ], "p_value": 0.3924, "mde": 0.00923697491590414, "mde_rel_pct": 10.862154335486855, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "math_check": { "frozen_mu": -1.2, "total_mu": 0.3, "predicted_rate_slope": 1.5, "observed_rate_slope": 1.5, "confirmed": true }, "trained_model": { "mu_mean": 0.7908481732010841, "rate_effect": 0.24924359563738108, "positive_definite_min_eig": 0.4535291790962219 }, "confirmed": false } }, "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }