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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "The correction-aware rooted-tree optimizer was implemented and evaluated on the registered tabular Friedman#1 regression track with the shared mlp_tiny architecture. FedAvg achieved mean test MSE 31.3721, while the best correction-aware configuration achieved 42.0916; paired delta_mean was +10.7194 with permutation p=0.2999, so there was no significant win. The trained-model mechanism signature was confirmed because corrections were nonzero and the mean consensus residual decreased, but this did not translate into better task performance.", "metrics": { "baseline": "FedAvg, tuned lr=0.01, 20 epochs: mean test MSE 31.372122287750244, std 9.015775011632964, per_seed [22.099992752075195,30.866003036499023,23.188148498535156,22.244491577148438,28.95732879638672,48.877689361572266,33.53181076049805,41.21151351928711]", "idea": "Correction-aware tree, best lr=0.01 rho=0.05 penalty=0.1, 20 epochs: mean test MSE 42.09156918525696, std 19.275065768488197, per_seed [72.97396087646484,40.06073760986328,67.2032470703125,53.84383010864258,21.086515426635742,30.523590087890625,33.312156677246094,17.728515625]", "delta_mean": 10.719446897506714, "p_value": 0.2999, "idea_wins": 4, "n_pairs": 8 }, "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "rho": 0.0, "penalty": 0.0, "epochs": 20 }, "sweep": [ { "cfg": { "lr": 0.001, "rho": 0.0, "penalty": 0.0, "epochs": 20 }, "mean": 215.17152404785156 }, { "cfg": { "lr": 0.001, "rho": 0.05, "penalty": 0.1, "epochs": 20 }, "mean": 215.17152404785156 }, { "cfg": { "lr": 0.001, "rho": 0.2, "penalty": 0.8, "epochs": 20 }, "mean": 215.17152404785156 }, { "cfg": { "lr": 0.003, "rho": 0.0, "penalty": 0.0, "epochs": 20 }, "mean": 72.50996971130371 }, { "cfg": { "lr": 0.003, "rho": 0.05, "penalty": 0.1, "epochs": 20 }, "mean": 72.50996971130371 }, { "cfg": { "lr": 0.003, "rho": 0.2, "penalty": 0.8, "epochs": 20 }, "mean": 72.50996971130371 }, { "cfg": { "lr": 0.01, "rho": 0.0, "penalty": 0.0, "epochs": 20 }, "mean": 24.599658966064453 }, { "cfg": { "lr": 0.01, "rho": 0.05, "penalty": 0.1, "epochs": 20 }, "mean": 24.599658966064453 }, { "cfg": { "lr": 0.01, "rho": 0.2, "penalty": 0.8, "epochs": 20 }, "mean": 24.599658966064453 } ], "full": { "mean": 31.372122287750244, "std": 9.015775011632964, "per_seed": [ 22.099992752075195, 30.866003036499023, 23.188148498535156, 22.244491577148438, 28.95732879638672, 48.877689361572266, 33.53181076049805, 41.21151351928711 ], "n": 8 }, "method": "FedAvg (central mean after local minibatch step)" }, "idea": { "mean": 42.09156918525696, "std": 19.275065768488197, "per_seed": [ 72.97396087646484, 40.06073760986328, 67.2032470703125, 53.84383010864258, 21.086515426635742, 30.523590087890625, 33.312156677246094, 17.728515625 ], "n": 8 }, "comparison": { "delta_mean": 10.719446897506714, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ 50.87396812438965, 9.194734573364258, 44.015098571777344, 31.59933853149414, -7.870813369750977, -18.35409927368164, -0.21965408325195312, -23.48299789428711 ], "p_value": 0.2999, "mde": 23.733515768986862, "mde_rel_pct": 75.65161053274996, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "mechanism": "trained four-worker tree residual and dual correction measurements", "predicted": "redistributed corrections should be nonzero and reduce stale-consensus error", "observed_summary": { "mean_initial_residual": 9.564312875270844, "mean_final_residual": 7.654884484410286, "mean_correction_norm": 0.3991429200768471 }, "confirmed": true }, "idea_sweep": [ { "cfg": { "lr": 0.01, "rho": 0.0, "penalty": 0.0, "epochs": 20 }, "mean": 47.336907148361206 }, { "cfg": { "lr": 0.01, "rho": 0.05, "penalty": 0.1, "epochs": 20 }, "mean": 42.09156918525696 }, { "cfg": { "lr": 0.01, "rho": 0.2, "penalty": 0.8, "epochs": 20 }, "mean": 43.77399754524231 } ] }, "how_to_run": "/home/maxwelhelp/main/bin/python3 tree_bench.py", "files": [ "tree_bench.py", "bench_report.json" ], "limitations": "Only the registered tabular optimizer track was tested. Real asynchronous execution, actual inter-worker bandwidth and latency, multi-GPU placement, and larger neural networks were not evaluated.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }