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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a custom spatial image-denoising track and an end-to-end CNN comparison where the only intervention was the differentiable multi-threshold Euler-density auxiliary loss. Across 8 paired seeds, the baseline achieved mean test MSE 0.1141428 versus 0.1143862 for the idea, giving delta_mean +0.0002435 and permutation p=0.0917. The trained-model mechanism signature was confirmed, but there was no significant standard-metric improvement.", "metrics": { "baseline": "Best lr=0.006; 8-seed test MSE mean 0.1141427802, std 0.0198688870", "idea": "Euler lambda=0.2, T=0.1, lr=0.006; 8-seed test MSE mean 0.1143862335, std 0.0195904620; delta +0.0002434533; p=0.0917; 3/8 paired wins; mechanism confirmed=true" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_euler.py", "files": [ "bench_euler.py", "euler_custom_track.py", "bench_report.json", "run_experiment.py", "results.json", "REPORT.md" ], "limitations": "The built-in CIFAR vision track is classification and lacks a reconstructed image field, so a local custom denoising track was required. The custom benchmark used 160 training samples, 80 test samples, 5 epochs, a small CNN, and synthetic disks/rectangles rather than CIFAR, CelebA, diffusion, GAN, FID, or component/hole evaluations. The baseline and idea learning-rate union was swept, but only the Euler lambda and temperature were fixed rather than separately swept.", "bench_report": { "bench_version": 1, "track": "euler_morphology_denoising", "model": "small_denoiser", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.2358667068183422 }, { "cfg": { "lr": 0.003 }, "mean": 0.19127917662262917 }, { "cfg": { "lr": 0.006 }, "mean": 0.11789883300662041 } ], "full": { "mean": 0.11414278019219637, "std": 0.019868887010459576, "per_seed": [ 0.11202351003885269, 0.09799353033304214, 0.13151243329048157, 0.13006585836410522, 0.1398763358592987, 0.08686374127864838, 0.12790271639823914, 0.0869041159749031 ], "n": 8 } }, "idea": { "mean": 0.1143862335011363, "std": 0.01959046199703673, "per_seed": [ 0.11207777261734009, 0.09821636974811554, 0.1314822882413864, 0.1300397664308548, 0.13983863592147827, 0.08825309574604034, 0.12812229990959167, 0.0870596393942833 ], "n": 8 }, "comparison": { "delta_mean": 0.00024345330893993378, "idea_wins": 3, "n_pairs": 8, "per_seed_diffs": [ 5.426257848739624e-05, 0.00022283941507339478, -3.014504909515381e-05, -2.6091933250427246e-05, -3.769993782043457e-05, 0.0013893544673919678, 0.00021958351135253906, 0.000155523419380188 ], "p_value": 0.0917, "mde": 0.0003977750936249918, "mde_rel_pct": 0.3484890528820207, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "temperature": 0.1, "lambda": 0.2, "test_soft_mae": 0.04664662852883339, "test_hard_mae": 0.00214973883703351, "confirmed": true }, "custom_track": { "name": "euler_morphology_denoising", "file": "euler_custom_track.py", "domain": "vision" } }, "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }