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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented rank-normalized nonlinear spectral whitening in a local training loop for the structurally matched tabular MLP track, using the same MLP and Adam baseline apart from the intervention. Across 8 paired seeds, baseline MSE was 7.2350 and idea MSE was 7.6756, with delta_mean +0.4406 and permutation p=0.086; this is not a significant win. The trained-model mechanism signature was not confirmed because the measured condition numbers remained very large and gradient spikes were not shown to decrease.", "metrics": { "baseline": "Best configuration lr=0.01, weight_decay=0.0; 8-seed mean test MSE 7.234962344169617.", "idea": "Best configuration lr=0.01, strength=0.5, eps=0.01; 8-seed mean test MSE 7.675570726394653; paired delta +0.4406083822250366; permutation p-value 0.086." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_rank_spectral.py", "files": [ "bench_rank_spectral.py", "bench_report.json", "bench_stdout.txt" ], "limitations": "Only the registered tabular/mlp_tiny track was tested. The implementation uses the permitted pilot spectral map rather than full Marcenko-Pastur fixed-point fitting, and does not test EMA refresh, larger models, or wall-clock overhead.", "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 14.369435787200928 }, { "cfg": { "lr": 0.001, "weight_decay": 0.0001 }, "mean": 14.339182376861572 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 8.496363878250122 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0001 }, "mean": 8.167794823646545 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0 }, "mean": 7.075780034065247 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0001 }, "mean": 7.193095445632935 } ], "full": { "mean": 7.234962344169617, "std": 0.7865574747580145, "per_seed": [ 6.9322123527526855, 5.762338638305664, 7.506187438964844, 8.102381706237793, 7.3106536865234375, 6.380474090576172, 8.19311237335205, 7.692338466644287 ], "n": 8 } }, "idea": { "mean": 7.675570726394653, "std": 1.006710258489254, "per_seed": [ 7.532199382781982, 6.862549304962158, 8.377254486083984, 9.171204566955566, 7.1806640625, 5.912852764129639, 8.827324867248535, 7.540516376495361 ], "n": 8 }, "comparison": { "delta_mean": 0.4406083822250366, "idea_wins": 3, "n_pairs": 8, "per_seed_diffs": [ 0.5999870300292969, 1.1002106666564941, 0.8710670471191406, 1.0688228607177734, -0.1299896240234375, -0.4676213264465332, 0.6342124938964844, -0.15182209014892578 ], "p_value": 0.086, "mde": 0.5075651661606405, "mde_rel_pct": 7.015450005343402, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": "rank normalization reduces activation covariance condition number and gradient spikes", "observed": [ { "seed": 0, "metric": 7.532199382781982, "median_condition": 49222378.0, "max_grad_norm": 139.02621459960938 }, { "seed": 1, "metric": 6.862549304962158, "median_condition": 46337590.0, "max_grad_norm": 272.9651794433594 }, { "seed": 2, "metric": 8.377254486083984, "median_condition": 50202086.0, "max_grad_norm": 117.48654174804688 }, { "seed": 3, "metric": 9.171204566955566, "median_condition": 47638106.0, "max_grad_norm": 147.75779724121094 } ], "idea_sweep": [ { "cfg": { "lr": 0.001, "strength": 0.25, "eps": 0.01 }, "mean": 166.07542037963867 }, { "cfg": { "lr": 0.001, "strength": 0.35, "eps": 0.01 }, "mean": 171.13961791992188 }, { "cfg": { "lr": 0.001, "strength": 0.5, "eps": 0.01 }, "mean": 175.3752212524414 }, { "cfg": { "lr": 0.003, "strength": 0.25, "eps": 0.01 }, "mean": 13.039084672927856 }, { "cfg": { "lr": 0.003, "strength": 0.35, "eps": 0.01 }, "mean": 12.977247714996338 }, { "cfg": { "lr": 0.003, "strength": 0.5, "eps": 0.01 }, "mean": 12.668333530426025 }, { "cfg": { "lr": 0.01, "strength": 0.25, "eps": 0.01 }, "mean": 8.61996614933014 }, { "cfg": { "lr": 0.01, "strength": 0.35, "eps": 0.01 }, "mean": 8.203945398330688 }, { "cfg": { "lr": 0.01, "strength": 0.5, "eps": 0.01 }, "mean": 7.985801935195923 } ], "confirmed": false }, "elapsed_sec": 252.72731709480286, "protocol_note": "Tabular is structurally matched because this is an optimizer/preconditioning intervention; both systems use the same MLP and Adam apart from activation whitening." }, "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }