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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented a local variance-aware reduction-tree training modification on the optimizer-matched tabular/Friedman#1 track with the shared mlp_tiny architecture. The mechanism produced Huffman trees with observed minimum depth 1 versus balanced depth 3, but baseline and idea had identical test MSE on all 8 paired seeds (delta_mean=0, p=1.0), so the promised training benefit was not observed. The mechanism signature is not treated as quantitatively confirmed because the proxy reduction operation becomes idempotent after one float16 cast and therefore does not faithfully expose topology-dependent accumulated rounding.", "metrics": { "baseline": "Best lr=0.006, 12 epochs; full 8-seed test MSE mean 14.8093005419, std 2.0570786188.", "idea": "Best lr=0.006, 12 epochs; full 8-seed test MSE mean 14.8093005419, std 2.0570786188; paired delta 0.0, p=1.0, 0/8 wins.", "mechanism": "Trained runs selected Huffman minimum depth mean 1.0 versus balanced depth 3; observed proxy reduction MSE mean 1.0762555611e-07, but this proxy did not produce different model behavior." }, "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 12 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12 }, "mean": 130.1314926147461 }, { "cfg": { "lr": 0.003, "epochs": 12 }, "mean": 17.59502100944519 }, { "cfg": { "lr": 0.006, "epochs": 12 }, "mean": 13.973259925842285 } ], "full": { "mean": 14.809300541877747, "std": 2.0570786187742542, "per_seed": [ 14.058874130249023, 15.062494277954102, 13.770842552185059, 13.000828742980957, 12.002371788024902, 15.041543960571289, 16.459678649902344, 19.077770233154297 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.006, "epochs": 12 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12 }, "mean": 130.1314926147461 }, { "cfg": { "lr": 0.003, "epochs": 12 }, "mean": 17.59502100944519 }, { "cfg": { "lr": 0.006, "epochs": 12 }, "mean": 13.973259925842285 } ], "full": { "mean": 14.809300541877747, "std": 2.0570786187742542, "per_seed": [ 14.058874130249023, 15.062494277954102, 13.770842552185059, 13.000828742980957, 12.002371788024902, 15.041543960571289, 16.459678649902344, 19.077770233154297 ], "n": 8 } }, "comparison": { "delta_mean": 0.0, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], "p_value": 1.0, "mde": 0.0, "mde_rel_pct": 0.0, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "predicted": "high-variance chunks have shallower Huffman depth", "observed_mean_min_depth": 1.0, "balanced_depth": 3, "confirmed": false } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "This MVP did not run real multi-GPU distributed all-reduce, hardware BF16/FP16 accumulation, communication throughput, overflow frequency, or CIFAR/ResNet. Its shape-preserving low-precision gradient proxy was insufficient to model topology-dependent accumulated rounding; the exact equality of baseline and idea indicates that a faithful distributed reduction implementation is needed before drawing conclusions about numerical stability.", "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }