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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a local tabular benchmark modification using the shared mlp_tiny architecture, identical data, epochs, batch size, and learning-rate union; baseline Adam included a weight-decay sweep, while the idea used decreasing Tikhonov damping. On 8 paired seeds, baseline MSE was 9.6423 and the best idea MSE was 9.9093, delta +0.2670 with permutation p=0.04035, therefore the idea was significantly worse. The trained-model CG signature was also not confirmed: observed maximum residual 80.76 versus predicted tolerance 0.000507.", "metrics": { "baseline": "Best cfg lr=0.006, wd=0.001; full 8-seed test MSE mean=9.6423278451, std=1.0473264846; per-seed=[9.8919839859,10.5964794159,8.6700820923,9.0212182999,7.6159310341,10.5873336792,9.9360847473,10.8195095062].", "idea": "Best cfg lr=0.006, eps0=0.01, eps_min=1e-6, decay=0.7; full 8-seed test MSE mean=9.9093276858, std=1.2741297958; delta_mean=+0.2669998407, idea_wins=1/8, permutation p=0.04035. Signature observed_max_residual=80.7649307251 versus predicted_max_residual=0.0005065036; confirmed=false." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_tikhonov.py", "files": [ "bench_tikhonov.py", "bench_report.json" ], "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "wd": 0.001 }, "sweep": [ { "cfg": { "lr": 0.001, "wd": 0.0 }, "mean": 17.79831099510193 }, { "cfg": { "lr": 0.001, "wd": 0.0001 }, "mean": 17.804412841796875 }, { "cfg": { "lr": 0.001, "wd": 0.001 }, "mean": 17.81385827064514 }, { "cfg": { "lr": 0.003, "wd": 0.0 }, "mean": 13.153547525405884 }, { "cfg": { "lr": 0.003, "wd": 0.0001 }, "mean": 13.136067628860474 }, { "cfg": { "lr": 0.003, "wd": 0.001 }, "mean": 13.114951848983765 }, { "cfg": { "lr": 0.006, "wd": 0.0 }, "mean": 9.905518293380737 }, { "cfg": { "lr": 0.006, "wd": 0.0001 }, "mean": 9.729963302612305 }, { "cfg": { "lr": 0.006, "wd": 0.001 }, "mean": 9.544940948486328 } ], "full": { "mean": 9.642327845096588, "std": 1.047326484601666, "per_seed": [ 9.891983985900879, 10.596479415893555, 8.670082092285156, 9.021218299865723, 7.615931034088135, 10.587333679199219, 9.936084747314453, 10.819509506225586 ], "n": 8 } }, "idea": { "mean": 9.909327685832977, "std": 1.2741297958180666, "per_seed": [ 10.186660766601562, 11.613065719604492, 8.699080467224121, 9.08471965789795, 7.6164374351501465, 10.533278465270996, 10.165548324584961, 11.37583065032959 ], "n": 8 }, "comparison": { "delta_mean": 0.26699984073638916, "idea_wins": 1, "n_pairs": 8, "per_seed_diffs": [ 0.2946767807006836, 1.0165863037109375, 0.028998374938964844, 0.06350135803222656, 0.0005064010620117188, -0.054055213928222656, 0.2294635772705078, 0.5563211441040039 ], "p_value": 0.04035, "mde": 0.30311716666322375, "mde_rel_pct": 3.143609837093103, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "track_choice": "tabular matches optimizer/regularizer ideas; shared MLP.", "selected_idea_cfg": { "lr": 0.006, "eps0": 0.01, "eps_min": 1e-06, "decay": 0.7 }, "signature": { "prediction": "damped CG residual <= 1e-5 max(1, ||b||) on trained network", "predicted_max_residual": 0.0005065035629272461, "observed_max_residual": 80.76493072509766, "epsilon_pair_relative_change_norm_proxy": 0.0031547897960990667, "trained_model_parameter_norm": 8.515052795410156, "confirmed": false } } }, "limitations": "The fixed built-in tracks do not expose a true outer hyperparameter optimization problem, so the practical intervention was represented as Tikhonov continuation during ordinary MLP training rather than an end-to-end implicit hypergradient through an inner/outer split. No vision, sequence, or dynamics tracks were run because tabular is the prescribed structural match for optimizer/regularizer ideas; no width scaling, unrolled-reference cosine study, wall-clock comparison, or nonsmooth generalized-Hessian case was tested.", "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }