# Стенд-проверка (stage-2) · промт оператора: (универсальный) Ответ агента: { "worked": false, "confidence": 9, "verdict": "The full stage-2 bench was completed on the registered custom PDE track pde_patch_consensus with matched mlp_tiny systems, a tuned baseline sweep, and 8 paired seeds. Patch-consensus weak residual training produced no significant improvement: test MSE delta was +0.0000288114 with permutation p=0.7982, and the trained-model mechanism signature was not confirmed.", "metrics": { "baseline": "Full mean test MSE 0.2986701727; best config lr=0.001, weight=0.15, epochs=20.", "idea": "Mean test MSE 0.2986989841; paired delta +0.0000288114; permutation p=0.7982; 6/8 paired wins but no significant win; mechanism signature confirmed=false." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_run.py", "files": [ "pde_consensus_track.py", "bench_run.py", "bench_report.json" ], "limitations": "Only the registered small 1D PDE regression track was tested. No 2D PDE, boundary accuracy, operator coefficient F1, FLOP accounting, gradient variance, or broader architectures were evaluated; local sparse estimation was a detached ridge-plus-soft-threshold proxy rather than a full differentiable LASSO.", "bench_report": { "bench_version": 1, "track": "pde_patch_consensus", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001, "weight": 0.15, "epochs": 20, "lam": 0.002, "tau": 0.08 }, "sweep": [ { "cfg": { "lr": 0.001, "weight": 0.15, "epochs": 20, "lam": 0.002, "tau": 0.08 }, "mean": 0.3015064224600792 }, { "cfg": { "lr": 0.003, "weight": 0.3, "epochs": 20, "lam": 0.002, "tau": 0.08 }, "mean": 0.3022902384400368 }, { "cfg": { "lr": 0.01, "weight": 0.15, "epochs": 20, "lam": 0.002, "tau": 0.08 }, "mean": 0.3018767833709717 } ], "full": { "mean": 0.2986701726913452, "std": 0.01948377339633572, "per_seed": [ 0.2805998921394348, 0.3210130035877228, 0.2944689989089966, 0.3099437952041626, 0.3145343065261841, 0.2926834225654602, 0.26018649339675903, 0.3159314692020416 ], "n": 8 } }, "idea": { "mean": 0.2986989840865135, "std": 0.01941700283178657, "per_seed": [ 0.28059789538383484, 0.3210127651691437, 0.29445162415504456, 0.30993232131004333, 0.31450358033180237, 0.2926560342311859, 0.2604662775993347, 0.31597137451171875 ], "n": 8 }, "comparison": { "delta_mean": 2.8811395168304443e-05, "idea_wins": 6, "n_pairs": 8, "per_seed_diffs": [ -1.996755599975586e-06, -2.384185791015625e-07, -1.7374753952026367e-05, -1.1473894119262695e-05, -3.072619438171387e-05, -2.7388334274291992e-05, 0.0002797842025756836, 3.9905309677124023e-05 ], "p_value": 0.7982, "mde": 8.675913930736897e-05, "mde_rel_pct": 0.029048477966706267, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "prediction": "weak residual integration plus support consensus should reduce noisy residual variability and increase regional support agreement", "observed_trained_models": { "baseline_pointwise_pde_rms_mean": 0.022820161539129913, "idea_pointwise_pde_rms_mean": 0.029661827720701694, "baseline_observation_rms_mean": 0.5462089078210038, "idea_observation_rms_mean": 0.5462374777264867, "idea_local_support_rate_mean": 0.0625, "idea_modal_terms_mean": 0.0 }, "confirmed": false }, "custom_track": { "name": "pde_patch_consensus", "file": "pde_consensus_track.py", "domain": "pde" }, "idea_sweep": [ { "cfg": { "lr": 0.001, "weight": 0.15, "epochs": 20, "lam": 0.002, "tau": 0.08 }, "mean": 0.2986989840865135 }, { "cfg": { "lr": 0.003, "weight": 0.3, "epochs": 20, "lam": 0.002, "tau": 0.08 }, "mean": 0.2990812510251999 }, { "cfg": { "lr": 0.01, "weight": 0.15, "epochs": 20, "lam": 0.002, "tau": 0.08 }, "mean": 0.29942547529935837 } ] }, "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }