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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented Double-Geometric Layerwise ES as a layerwise integer-controlled optimizer for the matched tabular MLP benchmark and ran the full 8-seed paired protocol. The baseline Adam sweep achieved test MSE 9.258 ± 0.795, while the idea achieved 10.168 ± 1.859; paired delta was +0.910 with permutation p=0.129, so there was no significant win and the system did not work on this benchmark.", "metrics": { "baseline": "Adam, best lr=0.006 and weight_decay=0.0001: test MSE 9.257637917995453 ± 0.7946203059794043", "idea": "Double-geometric layerwise optimizer, selected lr=0.006, rho=0.12, beta=0.85, K=6: test MSE 10.167674660682678 ± 1.8586175283157877" }, "how_to_run": "cd /home/maxwelhelp/all/math2nn/experiments/exp107_double-geometric-layerwise-es && /home/maxwelhelp/main/bin/python3 bench_run.py", "files": [ "bench_run.py", "bench_report.json" ], "limitations": "The mechanism signature did not confirm the stage-1 quantitative prediction because per-candidate covariance/update diagnostics were not persisted in the report. The idea sweep contains a duplicated lr=0.006 configuration rather than three distinct learning rates, although all tested idea learning rates were present in the baseline sweep. Only the structurally matched tabular track and mlp_tiny model were tested; no quantization-specific deployment or larger layerwise search was evaluated.", "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "weight_decay": 0.0001 }, "sweep": [ { "cfg": { "lr": 0.0015, "weight_decay": 0.0 }, "mean": 16.004083156585693 }, { "cfg": { "lr": 0.0015, "weight_decay": 0.0001 }, "mean": 16.01387643814087 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 12.535736083984375 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0001 }, "mean": 12.430302143096924 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0 }, "mean": 9.091599702835083 }, { "cfg": { "lr": 0.006, "weight_decay": 0.0001 }, "mean": 8.875346541404724 } ], "full": { "mean": 9.257637917995453, "std": 0.7946203059794043, "per_seed": [ 9.313394546508789, 7.957194805145264, 8.725268363952637, 9.505528450012207, 8.830371856689453, 9.170703887939453, 10.88531494140625, 9.67332649230957 ], "n": 8 } }, "idea": { "mean": 10.167674660682678, "std": 1.8586175283157877, "per_seed": [ 12.230741500854492, 8.306159973144531, 8.560102462768555, 9.36517333984375, 8.156329154968262, 9.683719635009766, 13.33909797668457, 11.7000732421875 ], "n": 8 }, "comparison": { "delta_mean": 0.9100367426872253, "idea_wins": 3, "n_pairs": 8, "per_seed_diffs": [ 2.917346954345703, 0.3489651679992676, -0.16516590118408203, -0.14035511016845703, -0.6740427017211914, 0.5130157470703125, 2.4537830352783203, 2.0267467498779297 ], "p_value": 0.129, "mde": 1.1351211014956983, "mde_rel_pct": 12.261454936460563, "verdict": "no significant win", "system_worked": false }, "idea_sweep": [ { "cfg": { "lr": 0.006, "rho": 0.12, "beta": 0.85, "epochs": 12, "K": 6 }, "mean": 9.615544319152832 }, { "cfg": { "lr": 0.0015, "rho": 0.12, "beta": 0.85, "epochs": 12, "K": 6 }, "mean": 15.944299936294556 }, { "cfg": { "lr": 0.006, "rho": 0.12, "beta": 0.85, "epochs": 12, "K": 6 }, "mean": 9.615544319152832 } ], "track_justification": "Optimizer intervention matches tabular MLP training; both systems share architecture and data.", "budget": { "epochs": 12, "batch": 64, "candidates_per_epoch": 6, "seeds": 8 }, "mechanism_signature": { "predicted": "utility-|z| covariance should determine q update direction", "observed_note": "trained-model candidate losses and DG q updates were collected during each run", "predicted_vs_observed": "no fixed quantitative tolerance claimed", "confirmed": false } }, "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }