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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the Bregman-Projection Polyak intervention as a positive-parameter log-geometry update on a gated MLP and evaluated it on the structurally matched tabular Friedman#1 track. Across 8 paired seeds, tuned Adam achieved MSE 12.7702 versus 14.7852 for the idea, with delta +2.0150 and permutation p=0.4921, so there was no significant win. The mechanism signature was not confirmed because root residuals and failures were nontrivial and observed versus predicted scaling disagreed.", "metrics": { "baseline": "Adam, lr sweep {0.001, 0.003, 0.01}, best lr=0.01; full 8-seed test MSE mean 12.7702126503, std 1.1689042660", "idea": "Positive log-mirror Polyak, same lr sweep, best lr=0.001; full 8-seed test MSE mean 14.7852387428", "paired_delta": "idea - baseline = +2.0150260925 MSE; 4/8 idea wins; permutation p=0.4921", "signature": "observed lambda/delta mean 0.2710435168 versus predicted inverse weighted gradient-square mean 1e12; max root residual 0.1692199707; root failure rate 0.1453125; confirmed=false" }, "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": 146.7281608581543 }, { "cfg": { "lr": 0.003 }, "mean": 17.104151725769043 }, { "cfg": { "lr": 0.01 }, "mean": 12.445883750915527 } ], "full": { "mean": 12.770212650299072, "std": 1.168904265972683, "per_seed": [ 12.32522201538086, 12.587058067321777, 14.008906364440918, 10.862348556518555, 11.249069213867188, 13.969073295593262, 13.09931755065918, 14.06070613861084 ], "n": 8 } }, "idea": { "mean": 14.78523874282837, "per_seed": [ 17.6944580078125, 12.148554801940918, 14.37415885925293, 26.43087387084961, 8.728538513183594, 12.932696342468262, 13.736586570739746, 12.236042976379395 ], "cfg": { "lr": 0.001 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 14.78523874282837 }, { "cfg": { "lr": 0.003 }, "mean": 25.065056443214417 }, { "cfg": { "lr": 0.01 }, "mean": 16.26691508293152 } ] }, "comparison": { "delta_mean": 2.015026092529297, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ 5.369235992431641, -0.4385032653808594, 0.3652524948120117, 15.568525314331055, -2.5205307006835938, -1.036376953125, 0.6372690200805664, -1.8246631622314453 ], "p_value": 0.4921, "mde": 4.999106670375855, "mde_rel_pct": 39.14662039914252, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "track_choice": "tabular/Friedman#1: optimizer intervention is structurally matched", "prediction": "small-gap positive-log lambda/delta approximates inverse weighted gradient square", "observed_lambda_over_delta_mean": 0.2710435168226066, "predicted_inverse_variance_mean": 1000000000000.0, "root_residual_max": 0.169219970703125, "root_failure_rate_mean": 0.14531249999999998, "clip_or_safeguard_rate_mean": 0.0, "confirmed": false } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "Only the tabular Friedman#1 track and mlp_tiny were tested; CIFAR vision, sequence, and dynamics tracks were not tested. The local implementation used a positive gated MLP rather than a simplex router, and the root safeguard capped difficult positive-geometry steps, so the result does not establish behavior for simplex-valued MoE routing.", "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }