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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a local nonreversible latent-instanton-inspired training intervention for the structurally matched actuated-pendulum dynamics track, using the shared rnn_small architecture and a mobility-weighted Freidlin–Wentzell-style residual penalty. The baseline achieved mean test MSE 0.001420, while the idea achieved 0.027814; paired delta was +0.026394 (higher is worse) with permutation p=0.0081, so the idea was significantly worse. The trained-model mechanism signature did confirm the predicted weighting direction, but this does not override the negative task-metric result.", "metrics": { "baseline": "best lr=0.01; sweep means: lr=0.001 -> 0.004299, lr=0.003 -> 0.003462, lr=0.01 -> 0.001487; full 8-seed mean MSE=0.001420, std=0.000398", "idea": "best lr=0.01 with action penalty coefficient 0.02; full 8-seed mean MSE=0.027814, std=0.005122", "paired_delta": "idea - baseline = +0.026394 MSE", "permutation_p_value": 0.0081, "mechanism_signature": { "observed_residual_mse": 5.364831447601318, "observed_mobility_weighted_residual": 6.362071514129639, "weighted_to_unweighted_ratio": 1.1858846967082626, "predicted_direction": "weighted residual exceeds unweighted residual for mobility below one", "confirmed": true } }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.004298779065720737 }, { "cfg": { "lr": 0.003 }, "mean": 0.0034615940821822733 }, { "cfg": { "lr": 0.01 }, "mean": 0.0014865802077110857 } ], "full": { "mean": 0.0014198302378645167, "std": 0.00039839114901047844, "per_seed": [ 0.0009327387670055032, 0.0018641706556081772, 0.0010785290505737066, 0.0020708823576569557, 0.0009260809747502208, 0.0014055331703275442, 0.0016443432541564107, 0.001436363672837615 ], "n": 8 } }, "idea": { "mean": 0.02781403553672135, "std": 0.005121785167552724, "per_seed": [ 0.02624191716313362, 0.019634274765849113, 0.030885715037584305, 0.03626050800085068, 0.020821113139390945, 0.029457034543156624, 0.030522819608449936, 0.028688902035355568 ], "n": 8 }, "comparison": { "delta_mean": 0.026394205298856832, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.025309178396128118, 0.017770104110240936, 0.029807185987010598, 0.03418962564319372, 0.019895032164640725, 0.02805150137282908, 0.028878476354293525, 0.027252538362517953 ], "p_value": 0.0081, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "prediction": "mobility-weighted action should scale residual energy by inverse positive mobility", "observed_residual_mse": 5.364831447601318, "observed_mobility_weighted_residual": 6.362071514129639, "observed_weighted_to_unweighted_ratio": 1.1858846967082626, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "Tested only the built-in 8-step actuated-pendulum regression track, one shared rnn_small architecture, 12 training epochs, and a three-point learning-rate sweep. The intervention used a hand-designed local physics proxy and diagonal mobility surrogate rather than a learned reverse drift or learned mobility network; no latent encoder-decoder, rare-transition success-rate, diversity, or generation-time trajectory benchmark was tested.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }