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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented an end-to-end conditional Gaussian-copula probabilistic head with the same MLP trunk as a diagonal-Gaussian baseline on the structurally matched six-output correlated multitask regression track. The mechanism signature was confirmed: predicted mean off-diagonal residual correlation 0.1143 versus observed 0.1704 (absolute error 0.0561). However, test MSE worsened from 0.26368 to 0.27419, paired delta +0.01051, with permutation p=0.0552; therefore there is no significant win and the system did not work on this bench.", "metrics": { "baseline": "Best lr=0.006; full 8-seed test MSE mean 0.2636817899, std 0.0127073678; sweep means: lr=0.001 0.3392473, lr=0.003 0.2891158, lr=0.006 0.2687482.", "idea": "Best lr=0.006; full 8-seed test MSE mean 0.2741883807, std 0.0090002103; idea sweep means: lr=0.001 0.3560834, lr=0.003 0.3174563, lr=0.006 0.2741884; paired delta idea-baseline +0.0105066, p=0.0552, 2/8 idea wins.", "mechanism_signature": "Confirmed: predicted copula off-diagonal correlation mean 0.1142624 versus observed trained-model residual correlation mean 0.1704001; absolute error 0.0561378." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_copula_stage2.py", "files": [ "bench_copula_stage2.py", "bench_report.json" ], "limitations": "Only the correlated multitask regression custom track was tested, with 1,200 training samples, 500 test samples, 18 epochs, and a six-dimensional conditional Gaussian copula rather than monotone neural splines or non-Gaussian marginals. Energy score, CRPS, calibration histograms, tail-dependence coverage, parameter-count matching, and the built-in tabular/vision/sequence/dynamics tracks were not tested.", "bench_report": { "bench_version": 1, "track": "correlated_multitask_regression", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 18 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 18 }, "mean": 0.33924729377031326 }, { "cfg": { "lr": 0.003, "epochs": 18 }, "mean": 0.2891158163547516 }, { "cfg": { "lr": 0.006, "epochs": 18 }, "mean": 0.2687482498586178 } ], "full": { "mean": 0.26368178986012936, "std": 0.012707367791430902, "per_seed": [ 0.28817808628082275, 0.2641679346561432, 0.27657756209373474, 0.24606941640377045, 0.26166772842407227, 0.26569777727127075, 0.2544151544570923, 0.2526806592941284 ], "n": 8 } }, "idea": { "mean": 0.2741883806884289, "std": 0.00900021034383701, "per_seed": [ 0.28664544224739075, 0.2656421661376953, 0.2874869704246521, 0.2784552276134491, 0.273226797580719, 0.2612801492214203, 0.2741454243659973, 0.2666248679161072 ], "n": 8 }, "comparison": { "delta_mean": 0.010506590828299522, "idea_wins": 2, "n_pairs": 8, "per_seed_diffs": [ -0.0015326440334320068, 0.001474231481552124, 0.010909408330917358, 0.03238581120967865, 0.011559069156646729, -0.004417628049850464, 0.01973026990890503, 0.01394420862197876 ], "p_value": 0.0552, "mde": 0.010123642152049727, "mde_rel_pct": 3.839340652769324, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "predicted_mean_offdiag_corr": 0.11426236107945442, "observed_mean_offdiag_residual_corr": 0.17040012543923766, "abs_error": 0.05613776435978324, "confirmed": true, "prediction": "copula dependence on uniform/standardized scale captures positive cross-output residual dependence" } }, "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }