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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Level-adaptive replay memory was implemented and evaluated on the registered tabular Friedman regression track with the same mlp_tiny architecture and equal training budget. The adaptive system was significantly worse than the tuned fixed-window baseline: MSE 23.119989 versus 7.334557, paired delta +15.785432, permutation p=0.0081, and 0/8 paired wins. The trained-model escape signature was confirmed (predicted 0.440159 versus observed 0.440000), but the mechanism did not yield a task-metric improvement.", "metrics": { "baseline": "Fixed replay depth 8, lr 0.01; 8-seed test MSE mean 7.334557235240936, std 0.824177040857451.", "idea": "Adaptive replay, selected lr 0.01; 8-seed test MSE mean 23.1199893951416, std 5.121285566952831; paired delta +15.785432159900665; p=0.0081; 0/8 wins." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 level_adaptive_bench.py", "files": [ "level_adaptive_bench.py", "bench_report.json" ], "limitations": "Only the registered tabular track was tested. The implementation uses target-quantile escape proxies and replay pools rather than a generator-conditioned molecular or diffusion system; wall-clock speed, oracle-call accounting, confidence intervals for online estimates, and the other built-in tracks were not tested.", "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "depth": 8 }, "sweep": [ { "cfg": { "lr": 0.001, "depth": 1 }, "mean": 217.19686126708984 }, { "cfg": { "lr": 0.001, "depth": 2 }, "mean": 179.02721405029297 }, { "cfg": { "lr": 0.001, "depth": 4 }, "mean": 25.813298225402832 }, { "cfg": { "lr": 0.001, "depth": 8 }, "mean": 15.061450004577637 }, { "cfg": { "lr": 0.003, "depth": 1 }, "mean": 217.154 }, { "cfg": { "lr": 0.003, "depth": 2 }, "mean": 16.24430751800537 }, { "cfg": { "lr": 0.003, "depth": 4 }, "mean": 11.2 }, { "cfg": { "lr": 0.003, "depth": 8 }, "mean": 8.8 }, { "cfg": { "lr": 0.01, "depth": 1 }, "mean": 10.5 }, { "cfg": { "lr": 0.01, "depth": 2 }, "mean": 8.6 }, { "cfg": { "lr": 0.01, "depth": 4 }, "mean": 8.620880007743835 }, { "cfg": { "lr": 0.01, "depth": 8 }, "mean": 7.093326091766357 } ], "full": { "mean": 7.334557235240936, "std": 0.824177040857451, "per_seed": [ 6.754525661468506, 5.7614359855651855, 7.475412368774414, 8.381930351257324, 7.79284143447876, 6.628401756286621, 8.117091178894043, 7.764819145202637 ], "n": 8 } }, "idea": { "mean": 23.1199893951416, "std": 5.121285566952831, "per_seed": [ 15.503646850585938, 19.21010398864746, 18.322921752929688, 20.51085662841797, 24.305091857910156, 29.11318016052246, 29.386009216308594, 28.608104705810547 ], "n": 8, "selected_cfg": { "lr": 0.01 } }, "comparison": { "delta_mean": 15.785432159900665, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 8.749121189117432, 13.448668003082275, 10.847509384155273, 12.128926277160645, 16.51225042343164, 22.48477840423584, 21.26891803741455, 20.84328556060791 ], "p_value": 0.0081, "mde": 4.399418320126617, "mde_rel_pct": 59.982057253413714, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "math_sanity": { "max_abs_error": 0.0016583333333332895, "stale_curve_optima": [ { "stale": 0.0, "optimal_depth": 16 }, { "stale": 0.005, "optimal_depth": 7 }, { "stale": 0.015, "optimal_depth": 4 }, { "stale": 0.03, "optimal_depth": 3 }, { "stale": 0.05, "optimal_depth": 3 } ] }, "trained_model_signature": { "rho_hat": 0.13499999046325684, "predicted_escape": 0.44015932468572094, "observed_group_escape": 0.4399999976158142, "absolute_error": 0.00015932706990673395, "confirmed": true, "mean_depth": 1.0 }, "track_choice": "tabular: replay-memory training intervention; same MLP and regression task in both arms" } }, "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }