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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented exact categorical Composite Density-Power Loss as an end-to-end replacement for cross-entropy on the structurally matching vision track, using the same cnn_small architecture, corrupted CIFAR-10 subsets, optimizer, batch size, and paired-seed protocol. The trained-model mechanism signature confirmed the predicted observed-score gradient ratio: 0.487819 observed versus 0.487819 predicted, maximum relative error 3.01e-7. DPD was slightly worse on independent CIFAR-10 test error (0.890000 vs 0.887500; paired delta +0.002500; permutation p=0.8775), so the required significant-win criterion was not met.", "metrics": { "baseline": "vision/cnn_small, CE, 20% training-label corruption; best lr=0.001; 8-seed test error mean=0.887500, std=0.025481.", "idea": "vision/cnn_small, DPD alpha=0.5, lr=0.001; 8-seed test error mean=0.890000, std=0.027285; paired delta=+0.002500, 2/8 wins, permutation p=0.8775. Mechanism: observed ratio=0.487819, predicted ratio=0.487819, max relative error=3.01e-7, confirmed=true.", "baseline_sweep": "lr=0.001: mean 0.886667; lr=0.003: mean 0.895000; lr=0.01: mean 0.908333.", "idea_sweep": "alpha=0.3: mean 0.891667; alpha=0.5: mean 0.890000; alpha=0.7: mean 0.890000, all at baseline-best lr=0.001." }, "bench_report": { "bench_version": 1, "track": "vision", "model": "cnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.8866666704416275 }, { "cfg": { "lr": 0.003 }, "mean": 0.8950000107288361 }, { "cfg": { "lr": 0.01 }, "mean": 0.908333346247673 } ], "full": { "mean": 0.8875000029802322, "std": 0.02548147140610766, "per_seed": [ 0.9133333563804626, 0.8999999761581421, 0.8666666746139526, 0.8666666746139526, 0.8733333349227905, 0.8933333158493042, 0.9333333373069763, 0.8533333539962769 ], "n": 8 } }, "idea": { "mean": 0.890000008046627, "std": 0.027284505974877197, "per_seed": [ 0.9133333563804626, 0.8999999761581421, 0.8533333539962769, 0.8600000143051147, 0.9066666960716248, 0.8933333158493042, 0.9333333373069763, 0.8600000143051147 ], "n": 8 }, "comparison": { "delta_mean": 0.002500005066394806, "idea_wins": 2, "n_pairs": 8, "per_seed_diffs": [ 0.0, 0.0, -0.013333320617675781, -0.006666660308837891, 0.03333336114883423, 0.0, 0.0, 0.006666660308837891 ], "p_value": 0.8775, "mde": 0.011515956518988579, "mde_rel_pct": 1.2975725611625806, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "prediction": "observed-label score gradient ratio equals (1+alpha)*q^alpha", "alpha": 0.5, "n_corrupted": 48, "mean_predicted_ratio": 0.48781922459602356, "mean_observed_ratio": 0.48781922459602356, "max_relative_error": 3.0113764637462737e-07, "corrupted_mean_q": 0.10800278186798096, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "The full initial 9-configuration vision sweep exceeded the time budget; the completed protocol used 4 epochs, 300 training samples, 150 test samples, an equal three-value baseline learning-rate sweep, three DPD alpha values at the selected baseline learning rate, and all 8 paired seeds. No CIFAR-100, clean-data-only long training, calibration, speed, or multi-component architecture evaluation was performed.", "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }