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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built and ran a structurally matched custom masked-blur inverse-imaging track with identical MLP systems and sensitivity-weighted TV as the only intervention. The exact sensitivity check passed with maximum relative error 4.64e-16. Weighted TV slightly improved test MSE, but the paired permutation p-value was 0.1159, so the effect was not significant.", "metrics": { "baseline": "Best baseline, lr=0.003, no TV: mean test MSE 0.0213770662 ± 0.0003192543 over 8 seeds.", "idea": "Best sensitivity-weighted TV, lr=0.003 and lambda=0.002: mean test MSE 0.0213048055 ± 0.0003312502; paired delta -0.0000722606, 6/8 wins, p=0.1159." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_bench.py", "files": [ "custom_inverse_track.py", "run_bench.py", "bench_report.json" ], "limitations": "The custom task is a small 16x16 linear masked-blur inverse problem, not a full PDE solver or 64x64 CNN. Only 8 seeds, 24 epochs, one weighted-TV strength, and a compact MLP were tested; SSIM, nonlinear Jacobians, changing geometries, boundary sparsity, and larger architectures were not evaluated.", "bench_report": { "bench_version": 1, "track": "masked_blur_inverse", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.003, "lambda": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "lambda": 0.0 }, "mean": 0.0279180221 }, { "cfg": { "lr": 0.003, "lambda": 0.0 }, "mean": 0.0213770662 }, { "cfg": { "lr": 0.01, "lambda": 0.0 }, "mean": 0.0215127163 } ], "full": { "mean": 0.0213770662, "std": 0.0003192543, "per_seed": [ 0.0217458587, 0.0216330737, 0.021614654, 0.0210507959, 0.0209858194, 0.0208887085, 0.0215625912, 0.0215350278 ], "n": 8 } }, "idea": { "mean": 0.0213048055, "std": 0.0003312502, "per_seed": [ 0.0217088312, 0.0217094067, 0.0215664487, 0.0211236067, 0.0208953973, 0.0207754783, 0.0213462021, 0.0213130731 ], "n": 8 }, "comparison": { "delta_mean": -7.22606e-05, "idea_wins": 6, "n_pairs": 8, "per_seed_diffs": [ -3.70275e-05, 7.63331e-05, -4.82053e-05, 7.28108e-05, -9.04221e-05, -0.0001132302, -0.0002163891, -0.0002219547 ], "p_value": 0.1159, "mde": 9.48823e-05, "mde_rel_pct": 0.44385, "verdict": "no measurable effect", "system_worked": false }, "custom_track": { "name": "masked_blur_inverse", "file": "custom_inverse_track.py", "domain": "pde_inverse" }, "mathcheck": { "max_relative_column_fd_error": 4.637162e-16, "weight_min": 0.6099013, "weight_max": 1.5739334, "weight_mean": 1.0 }, "idea_sweep": [ { "cfg": { "lr": 0.001, "lambda": 0.002 }, "mean": 0.027870018 }, { "cfg": { "lr": 0.003, "lambda": 0.002 }, "mean": 0.0213048055 }, { "cfg": { "lr": 0.01, "lambda": 0.002 }, "mean": 0.0215245492 } ], "mechanism_signature": { "predicted": "Weighted TV should alter spatial gradient allocation in proportion to cached sensitivity.", "observed_mean_horizontal_gradient": 0.09227585, "baseline_mean_horizontal_gradient": 0.09304057, "relative_change": -0.00821926, "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }