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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 7, "verdict": "Implemented STL-robust MoE gating on the structurally matched dynamics track with paired end-to-end neural training and an equal-budget baseline learning-rate sweep. The idea achieved significantly lower test MSE across all 8 seeds (0.0003050 vs 0.0008321, delta -0.0005271, permutation p=0.0081). However, the trained-model mechanism signature did not confirm the expected positive robustness-to-gate sensitivity (observed slope -0.1122), so the accuracy win is real in this benchmark but the proposed mechanism was not quantitatively validated.", "metrics": { "baseline": "Test MSE mean 0.0008321261 ± 0.0002982995 over 8 seeds; best lr=0.006, epochs=20.", "idea": "Test MSE mean 0.0003050095 ± 0.0001339860 over 8 seeds; beta=1.5, lr=0.006, epochs=20; all 8 paired seeds improved.", "paired_delta": "-0.0005271166; idea wins 8/8; permutation p=0.0081; verdict idea better (significant)." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stl_bench.py", "files": [ "stl_bench.py", "bench_report.json" ], "limitations": "The benchmark mechanism signature was measured using a separately retrained CPU probe rather than persisted weights from the scored runs. The idea grid duplicated lr=0.006 instead of including the nearby lr=0.003 setting, although all idea learning rates were present in the baseline sweep. No long-horizon rollout metric beyond the built-in dynamics test MSE was evaluated, and the safety robustness proxy was a scalar one-step differentiable predicate rather than a full multi-step STL formula.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 20 }, "sweep": [ { "cfg": { "lr": 0.0015, "epochs": 20 }, "mean": 0.0038982966798357666 }, { "cfg": { "lr": 0.003, "epochs": 20 }, "mean": 0.0019887955859303474 }, { "cfg": { "lr": 0.006, "epochs": 20 }, "mean": 0.0010756378178484738 } ], "full": { "mean": 0.0008321261484525166, "std": 0.00029829948920216036, "per_seed": [ 0.0010384503984823823, 0.0007975056068971753, 0.0010105057153850794, 0.0014560895506292582, 0.0006677014753222466, 0.0005428922595456243, 0.0005925378645770252, 0.000551326316781342 ], "n": 8 } }, "idea": { "mean": 0.00030500950379064307, "std": 0.0001339859662227794, "per_seed": [ 0.0006193295703269541, 0.0002923139254562557, 0.00036500030546449125, 0.00028455446590669453, 0.00018328196892980486, 0.00024144898634403944, 0.00015765898569952697, 0.0002964878221973777 ], "n": 8 }, "comparison": { "delta_mean": -0.0005271166446618736, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.00041912082815542817, -0.0005051916814409196, -0.0006455054099205881, -0.0011715350847225636, -0.0004844195063924417, -0.0003014432732015848, -0.0004348788788774982, -0.00025483849458396435 ], "p_value": 0.0081, "mde": 0.00024002743133603362, "mde_rel_pct": 28.845077369868307, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "robustness_delta_mean": -0.09007573872804642, "posterior_minus_prior_delta_mean": -0.012609480880200863, "observed_sensitivity_slope": -0.11219390882712943, "predicted_positive_sensitivity": true, "confirmed": false }, "baseline_grid": [ { "lr": 0.0015, "epochs": 20 }, { "lr": 0.003, "epochs": 20 }, { "lr": 0.006, "epochs": 20 } ], "idea_grid": [ { "lr": 0.006, "epochs": 20 }, { "lr": 0.0015, "epochs": 20 }, { "lr": 0.006, "epochs": 20 } ], "selected_idea_cfg": { "lr": 0.006, "epochs": 20 }, "device": "cuda" }, "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 0, "system_judged": true }