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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the Differentiable Maximal-Attractor Trap as a sampled trapping loss on the registered dynamics/rnn_small benchmark. The best idea configuration improved mean test MSE from 0.0041100 to 0.0033520, but the paired permutation test gave p=0.22655, so this is not a significant win under the bench rule. The trained-model signature confirmed reduced one-step sampled violations, while the long-horizon mean violation reduction was modest.", "metrics": { "baseline": "Best tuned baseline: lr=0.01, epochs=12, trap_lambda=0.0; mean test MSE 0.0041099994 over 8 seeds.", "idea": "Best idea: lr=0.01, epochs=12, trap_lambda=0.3; mean test MSE 0.0033519628 over 8 seeds; paired delta -0.0007580366; 6/8 wins; p=0.22655.", "mechanism_signature": "Seed-0 trained-model measurement: one-step violation 0.821289 baseline versus 0.303223 idea; mean 100-step violation 0.998208 baseline versus 0.988950 idea; idea max one-step absolute coordinate 0.961669.", "math_check": "For a*tanh(x) with alpha=1 and epsilon=0.05, predicted boundary 1.2473835 and observed grid boundary 1.25, absolute error 0.0026165." }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "epochs": 12, "trap_lambda": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12, "trap_lambda": 0.0 }, "per_seed": [ 0.0279239714, 0.0415529609, 0.0303880926, 0.0313325115, 0.0559770875, 0.0222599022, 0.0773502588, 0.0189396422 ], "mean": 0.0382155534, "std": 0.0196353951 }, { "cfg": { "lr": 0.003, "epochs": 12, "trap_lambda": 0.0 }, "per_seed": [ 0.0095840758, 0.0137225268, 0.0080159707, 0.0119991573, 0.0140530299, 0.0077892658, 0.0131681459, 0.0078314673 ], "mean": 0.0107704549, "std": 0.0027585833 }, { "cfg": { "lr": 0.01, "epochs": 12, "trap_lambda": 0.0 }, "per_seed": [ 0.0024895063, 0.0038835888, 0.0036092955, 0.0039991504, 0.0032447325, 0.0038587754, 0.0083235679, 0.0034713787 ], "mean": 0.0041099994, "std": 0.001769344 } ] }, "idea": { "best_cfg": { "lr": 0.01, "epochs": 12, "trap_lambda": 0.3 }, "per_seed": [ 0.0020382919, 0.0027961093, 0.0035538839, 0.0032515128, 0.0039495188, 0.0033034747, 0.0037955872, 0.0041273241 ], "mean": 0.0033519628, "std": 0.0006806487 }, "comparison": { "delta_mean": -0.0007580366, "idea_wins": 6, "n_pairs": 8, "per_seed_diffs": [ -0.0004512144, -0.0010874795, -5.54116e-05, -0.0007476376, 0.0007047863, -0.0005553006, -0.0045279807, 0.0006559454 ], "p_value": 0.22655, "mde": 0.0013808021, "mde_rel_pct": 33.5962, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": "trap training should reduce one-step and long-horizon U violations", "observed_one_step_violation": 0.2570800781, "observed_100_step_violation": 0.0, "observed_max_one_step_abs_coordinate": 0.961669445, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json", "bench_run.log" ], "limitations": "The registered benchmark used 400 training and 200 test examples, a 32-unit RNN, 12 epochs, zero inputs, and sampled rather than interval-certified trapping constraints. No exact invariance certificate, larger models, longer training, nonzero bounded inputs, or additional tracks were tested.", "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }