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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Barrier-certified neural policy training was evaluated on the registered robust_cbf_pendulum_policy dynamics track with the same shared MLP architecture and an 8-seed paired protocol. The idea was not better: its test MSE was higher than the tuned baseline, the paired permutation p-value was 0.1407, and the mechanism signature was not quantitatively confirmed.", "metrics": { "baseline": "Best lr=0.006; full 8-seed mean MSE 0.0019978795608039945, std 0.0005495084055684182.", "idea": "Selected lr=0.003, barrier weight=2.0; full 8-seed mean MSE 0.002375210518948734, std 0.0007107137551610928.", "delta_mean": 0.00037733095814473927, "p_value": 0.1407, "idea_wins": 1, "n_pairs": 8 }, "bench_report": { "bench_version": 1, "track": "robust_cbf_pendulum_policy", "model": "shared_mlp_32", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 15, "barrier": false }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 15, "barrier": false }, "mean": 0.021794200874865055 }, { "cfg": { "lr": 0.003, "epochs": 15, "barrier": false }, "mean": 0.002334954886464402 }, { "cfg": { "lr": 0.006, "epochs": 15, "barrier": false }, "mean": 0.0018331437022425234 } ], "full": { "mean": 0.0019978795608039945, "std": 0.0005495084055684182, "per_seed": [ 0.0015628907131031156, 0.0015461408765986562, 0.0021467802580446005, 0.0020767629612237215, 0.0030877823010087013, 0.0011752876453101635, 0.0020312685519456863, 0.0023561231791973114 ], "n": 8 } }, "idea": { "mean": 0.002375210518948734, "std": 0.0007107137551610928, "per_seed": [ 0.0017161896685138345, 0.002388743683695793, 0.003441448090597987, 0.002218520501628518, 0.0035038175992667675, 0.0017992532812058926, 0.0024698558263480663, 0.0014638555003330112 ], "n": 8, "selection_sweep": [ { "cfg": { "lr": 0.001, "epochs": 15, "barrier": true, "weight": 1.0 }, "mean": 0.03862802591174841 }, { "cfg": { "lr": 0.003, "epochs": 15, "barrier": true, "weight": 2.0 }, "mean": 0.0024412254861090332 }, { "cfg": { "lr": 0.006, "epochs": 15, "barrier": true, "weight": 4.0 }, "mean": 0.003327691287267953 } ] }, "comparison": { "delta_mean": 0.00037733095814473927, "idea_wins": 1, "n_pairs": 8, "per_seed_diffs": [ 0.00015329895541071892, 0.000842602807097137, 0.0012946678325533867, 0.0001417575404047966, 0.0004160352982580662, 0.0006239656358957291, 0.00043858727440238, -0.0008922676788643003 ], "p_value": 0.1407, "mde": 0.0005327468319021694, "mde_rel_pct": 26.665613000604466, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": { "lower_bound": "epsilon - L_r delta", "delta": 0.0052734375 }, "observed_from_trained_models": { "min_residual_mean": -3.3228249847888947, "violation_fraction_mean": 0.2471875, "L_r_estimate_mean": 1.372166395187378, "bound_lower_mean": 0.07276396627537907, "n_models": 8 }, "confirmed": false } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 barrier_bench.py", "files": [ "barrier_bench.py", "bench_report.json" ], "limitations": "This used the registered pendulum policy track and 15 training epochs, with one smooth ellipsoidal barrier and three learning-rate/weight settings. It did not test longer training, multiple barrier geometries, formal global Lipschitz certification, larger policies, or real hardware deployment.", "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }