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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Cut-aware augmentation filtering was implemented with the shared cnn_small model on the CIFAR-10 vision track. The idea had test error 0.821875 versus 0.821250 for the tuned uniform baseline, paired delta +0.000625 and permutation p-value 1.0; therefore it did not produce a significant win.", "metrics": { "baseline": "mean test error 0.8212500066, std 0.0441411053, best lr 0.003", "idea": "mean test error 0.8218750060, std 0.0427885403, paired delta +0.0006249994, p-value 1.0" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json", "bench_run.log" ], "limitations": "The run used the small standard vision subset with 400 training examples, 200 test examples, and 8 epochs. Confidence-threshold pseudo-labeling, EMA cut estimates, q_min, warmup, sparse graph storage, and larger-scale CIFAR experiments were not tested.", "bench_report": { "bench_version": 1, "track": "vision", "model": "cnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.003 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.8149999976 }, { "cfg": { "lr": 0.003 }, "mean": 0.7900000066 }, { "cfg": { "lr": 0.005 }, "mean": 0.8599999994 } ], "full": { "mean": 0.8212500066, "std": 0.0441411053, "per_seed": [ 0.8050000072, 0.7250000238, 0.8149999976, 0.8149999976, 0.8650000095, 0.875, 0.8550000191, 0.8149999976 ], "n": 8 } }, "idea": { "mean": 0.821875006, "std": 0.0427885403, "per_seed": [ 0.8050000072, 0.7300000191, 0.8149999976, 0.8149999976, 0.8650000095, 0.875, 0.8550000191, 0.8149999976 ], "n": 8 }, "comparison": { "delta_mean": 0.0006249994, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.0, 0.0049999952, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], "p_value": 1.0, "mde": 0.0014781236, "mde_rel_pct": 0.1799846062, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "prediction": "higher estimated cut policy receives lower q", "observed": [ { "seed": 0, "baseline": { "metric": 0.8050000072, "cuts": [ 0.834702716, 0.834702716, 0.9102364031 ], "q": [ 0.4159778407, 0.4159778407, 0.1680443187 ], "graph_reg": 0.0167583954 }, "idea": { "metric": 0.8050000072, "cuts": [ 0.834702716, 0.834702716, 0.9102364031 ], "q": [ 0.4159778407, 0.4159778407, 0.1680443187 ], "graph_reg": 0.0140078636 } }, { "seed": 1, "baseline": { "metric": 0.7250000238, "cuts": [ 0.8353256393, 0.8353256393, 0.9083690777 ], "q": [ 0.4138681685, 0.4138681685, 0.1722636631 ], "graph_reg": 0.0059237769 }, "idea": { "metric": 0.7300000191, "cuts": [ 0.8353256393, 0.8353256393, 0.9083690777 ], "q": [ 0.4138681685, 0.4138681685, 0.1722636631 ], "graph_reg": 0.0050633522 } } ], "confirmed": true }, "custom_track": null }, "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }