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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the PDAS-filtered Adam update for the structurally matched tabular Friedman#1 regression track using the shared mlp_tiny architecture, 8 paired seeds, and a parity-preserving three-learning-rate sweep. The mechanism signature was confirmed: observed and predicted mean active constraints were both 3.9271, with 83.1% one-iteration solves. However, test MSE was significantly worse than baseline (paired delta +201.6398, permutation p=0.0081), so the idea did not win.", "metrics": { "baseline": "Best Adam lr=0.006, 12 epochs: mean test MSE 14.9502, std 1.5514.", "idea": "Best PDAS-filtered Adam lr=0.001, alpha=0.2, 12 epochs: mean test MSE 216.5900, std 10.7861; paired delta +201.6398, p=0.0081; 0/8 paired wins." }, "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 12 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12 }, "mean": 133.94248580932617 }, { "cfg": { "lr": 0.003, "epochs": 12 }, "mean": 16.007279753684998 }, { "cfg": { "lr": 0.006, "epochs": 12 }, "mean": 14.950186729431152 } ], "full": { "mean": 14.950186729431152, "std": 1.5514200913682417, "per_seed": [ 14.034903526306152, 11.691411018371582, 14.283523559570312, 15.042709350585938, 16.13408851623535, 17.11699104309082, 15.280345916748047, 16.017520904541016 ], "n": 8 } }, "idea": { "config": { "lr": 0.001, "alpha": 0.2, "epochs": 12 }, "mean": 216.5899887084961, "std": 10.786054269508782, "per_seed": [ 230.07789611816406, 217.97207641601562, 221.4623565673828, 199.12059020996094, 223.05921936035156, 202.85182189941406, 209.7367706298828, 228.43917846679688 ], "n": 8, "grid": [ { "config": { "lr": 0.001, "alpha": 0.2, "epochs": 12 }, "mean": 216.5899887084961 }, { "config": { "lr": 0.003, "alpha": 0.2, "epochs": 12 }, "mean": 234.6817684173584 }, { "config": { "lr": 0.006, "alpha": 0.2, "epochs": 12 }, "mean": 232.99076080322266 } ] }, "comparison": { "delta_mean": 201.63980197906494, "idea_wins": 0, "n_pairs": 8, "p_value": 0.0081, "mde": 10.077764526365558, "mde_rel_pct": 67.40895420741685, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "predicted_mean_active": 3.9270833333333335, "observed_mean_active": 3.9270833333333335, "mean_iterations": 1.3046875, "one_iteration_fraction": 0.8307291666666666, "active_set_changes": 65, "max_violation": 0.8067416300048826, "confirmed": true }, "search_space_parity": true }, "how_to_run": "/home/maxwelhelp/main/bin/python3 pdas_bench.py", "files": [ "pdas_bench.py", "bench_report.json" ], "limitations": "Only the tabular track and mlp_tiny model were tested; no vision, sequence, or dynamics transfer was evaluated. The MVP used low-dimensional layerwise norm barriers rather than full-parameter autodiff constraint rows, and did not compare against OSQP or benchmark wall-clock speed at neural-training scale. The measured post-update barrier violation was nonzero, so constraint enforcement requires further engineering.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }