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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Built a diagonal scalable Projection-Regularized Gradient optimizer and evaluated it against matched Adam on the Friedman#1 tabular track using the same mlp_tiny architecture, data, epochs, and learning-rate union. Across 8 paired seeds, tuned Adam achieved test MSE 7.9055 while the best projection setting achieved 11.2195, with delta_mean +3.3140 and permutation p=0.0081; therefore the idea was significantly worse. The trained-model signature showed nonzero empirical support-dependent metric modulation, but this mechanism signal did not translate into a task-metric win.", "metrics": { "baseline": "Tuned Adam, mlp_tiny, lr=0.01: test MSE 7.905456 +/- 0.733317 over 8 seeds", "idea": "Diagonal projection optimizer, mlp_tiny, lr=0.01, m=8, beta=0.9, lambda=0.1, rho=0.15, alpha=0.08: test MSE 11.219475 +/- 0.933073 over 8 seeds", "paired_delta": "idea - baseline = +3.314019 MSE", "permutation_p": "0.0081", "idea_wins": "0/8", "mechanism_signature": "Observed support_fraction=0.514455, q_mean=0.473345, grad_norm=44.269749; support-dependent shrinkage was observed, but the original report's confirmed flag is not treated as evidence of task improvement." }, "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 18.323792934417725 }, { "cfg": { "lr": 0.003 }, "mean": 12.731719493865967 }, { "cfg": { "lr": 0.01 }, "mean": 7.733822226524353 } ], "full": { "mean": 7.905456006526947, "std": 0.7333165741197974, "per_seed": [ 7.984025478363037, 6.387940406799316, 7.856008529663086, 8.707314491271973, 8.079574584960938, 7.502847671508789, 8.969817161560059, 7.756119728088379 ], "n": 8 }, "all_union_rates": { "0.001": { "mean": 19.335745573043823 }, "0.003": { "mean": 13.304376482963562 }, "0.01": { "mean": 7.905456006526947 } } }, "idea": { "mean": 11.219475030899048, "std": 0.9330729672086647, "per_seed": [ 11.737688064575195, 9.196637153625488, 10.628690719604492, 11.08810806274414, 11.466021537780762, 11.766623497009277, 11.28360366821289, 12.588427543640137 ], "n": 8 }, "comparison": { "delta_mean": 3.314019024372101, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 3.753662586212158, 2.808696746826172, 2.7726821899414062, 2.380793571472168, 3.386446952819824, 4.263775825500488, 2.313786506652832, 4.832307815551758 ], "p_value": 0.0081, "mde": 0.7627905507582391, "mde_rel_pct": 9.648912727215984, "verdict": "idea worse (significant)", "system_worked": false }, "idea_all_union_rates": { "0.001": { "mean": 198.77429580688477 }, "0.003": { "mean": 39.026103258132935 }, "0.01": { "mean": 11.219475030899048 } }, "idea_best_lr": 0.01, "mechanism_signature": { "prediction": "weakly supported coordinates receive smaller update metric", "observed": { "support_fraction": 0.514455258846283, "q_mean": 0.4733452796936035, "grad_norm": 44.26974844932556 }, "confirmed": false } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_projection.py", "files": [ "bench_projection.py", "bench_report.json" ], "limitations": "Only the mandated tabular track was tested; vision, sequence, and dynamics were not applicable to this optimizer idea. The implementation uses a diagonal approximation rather than the exact dense/low-rank matrix formulation, and no wall-clock/FLOP comparison, Hessian-vector-product stability sweep, AdamW, clipping, or alternative projection hyperparameter sweep beyond the fixed idea settings was performed.", "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }