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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the mean-reverting symmetric alpha-stable optimizer as a local PyTorch training-loop intervention on the structurally matched tabular Friedman regression track with the identical mlp_tiny architecture for baseline and idea. The idea achieved a small lower mean MSE (6.8102 vs 6.9206; delta -0.1103, 5/8 paired wins), but p=0.39255, so there was no significant benchmark win. The trained-model mechanism signature was quantitatively confirmed: predicted drift multiplier 0.99 versus observed 0.99000001, and observed characteristic-function power 1.661 versus alpha 1.5.", "metrics": { "baseline": "Adam baseline, best lr=0.01: mean test MSE 6.920556, std 0.571610, per-seed [7.096156, 6.974558, 6.666646, 6.943192, 5.700762, 7.199984, 6.888844, 7.894307]. Baseline sweep means: lr=0.001 -> 14.000885, lr=0.003 -> 7.579636, lr=0.01 -> 6.920138.", "idea": "Mean-reverting Levy optimizer, best of three parity settings (lr=0.01, lambda=1.0, beta=0.95, jump scale=0.001, alpha=1.5): mean test MSE 6.810218, std 0.341144, per-seed [6.926017, 6.900168, 6.710284, 6.759966, 6.147102, 6.569409, 7.093952, 7.374846]. Paired delta=-0.110338, 5/8 wins, permutation p=0.39255, verdict='no significant win'. Idea sweep means: lr=0.01 -> 6.824109, lr=0.001 -> 13.783733, lr=0.003 -> 7.443580." }, "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "wd": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "wd": 0.0 }, "mean": 14.000884532928467 }, { "cfg": { "lr": 0.003, "wd": 0.0 }, "mean": 7.579635977745056 }, { "cfg": { "lr": 0.01, "wd": 0.0 }, "mean": 6.920138120651245 } ], "full": { "mean": 6.9205562472343445, "std": 0.5716103719461259, "per_seed": [ 7.096155643463135, 6.974558353424072, 6.666646480560303, 6.943192005157471, 5.7007622718811035, 7.199983596801758, 6.8888444900512695, 7.8943071365356445 ], "n": 8 } }, "idea": { "mean": 6.810217976570129, "std": 0.34114415830274575, "per_seed": [ 6.926016807556152, 6.900168418884277, 6.7102837562561035, 6.759965896606445, 6.147101879119873, 6.569408893585205, 7.09395170211792, 7.374846458435059 ], "n": 8 }, "comparison": { "delta_mean": -0.11033827066421509, "idea_wins": 5, "n_pairs": 8, "per_seed_diffs": [ -0.17013883590698242, -0.07438993453979492, 0.04363727569580078, -0.1832261085510254, 0.44633960723876953, -0.6305747032165527, 0.2051072120666504, -0.5194606781005859 ], "p_value": 0.39255, "mde": 0.29668947868390905, "mde_rel_pct": 4.2870756061331745, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "signature_type": "trained_nn_optimizer_dynamics", "alpha": 1.5, "lambda": 1.0, "lr": 0.01, "q": 0.01, "predicted_drift_multiplier": 0.99, "observed_median_drift_multiplier": 0.9900000095367432, "observed_nn_jump_cf_power": 1.6607667261616632, "jump_mad": 0.0009690423030406237, "confirmed": true, "n_observations": 5914800 }, "idea_sweep": [ { "cfg": { "lr": 0.01, "wd": 0.0, "lambda": 1.0, "beta": 0.95, "noise": 0.001 }, "mean": 6.824108719825745 }, { "cfg": { "lr": 0.001, "wd": 0.0, "lambda": 1.0, "beta": 0.95, "noise": 0.001 }, "mean": 13.783732652664185 }, { "cfg": { "lr": 0.003, "wd": 0.0, "lambda": 1.0, "beta": 0.95, "noise": 0.001 }, "mean": 7.443580269813538 } ], "protocol": { "paired_seeds": [ 0, 1, 2, 3, 4, 5, 6, 7 ], "epochs": 15, "n_train": 1200, "n_test": 400, "structural_match": "optimizer intervention on tabular regression" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_levy.py", "files": [ "bench_levy.py", "bench_report.json" ], "limitations": "Only the structurally matched tabular Friedman regression track and mlp_tiny model were tested; vision, sequence, and dynamics tracks were not tested. The benchmark used 15 epochs and reduced data sizes (1200 train, 400 test) for runtime, tested one alpha, one lambda/beta/noise combination with three learning rates, and did not test sharp-basin escape, gradient-spike robustness, wall-clock speed, or larger neural networks.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }