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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "A custom endpoint-mixture regression track and paired mlp_tiny benchmark were implemented, and the generated report shows a large nominal improvement (baseline MSE 1.4371 versus idea MSE 0.1639, delta -1.2732, permutation p=0.0081). However, the idea implementation altered the training and test targets, so the systems were not evaluated on the same task; this protocol violation invalidates the apparent win. The trained-model mechanism signature was also not confirmed.", "metrics": { "baseline": "mlp_tiny, best lr=0.01, 8-seed test MSE mean=1.437147; sweep learning rates [0.001, 0.003, 0.01]", "idea": "mlp_tiny, lr=0.01, epsilon=0.1, tau=3.0, 8-seed test MSE mean=0.163934", "nominal_comparison": "delta_mean=-1.273213, p_value=0.0081, all 8 paired seeds won; invalid because targets differed", "mechanism_signature": "predicted=0.074823, observed=0.061721, confirmed=false" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_runner.py", "files": [ "custom_track.py", "bench_runner.py", "bench_report.json" ], "limitations": "The custom track is synthetic rather than a real VAE latent or diffusion benchmark. The idea-side target transformation makes the primary comparison invalid and must be corrected before claiming a neural-network win; no valid same-target retraining result was obtained. Full bridge dynamics, EM fitting, Riccati maps, FID, Wasserstein metrics, and runtime/FLOP comparisons were not tested.", "bench_report": { "bench_version": 1, "track": "latent_mixture_transport", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "full_mean": 1.4371472299098969, "sweep_lrs": [ 0.001, 0.003, 0.01 ] }, "idea": { "config": { "lr": 0.01, "epsilon": 0.1, "tau": 3.0 }, "mean": 0.16393440403044224 }, "comparison": { "delta_mean": -1.2732128258794546, "p_value": 0.0081, "verdict": "idea better (significant) in the invalid nominal report", "system_worked": true }, "mechanism_signature": { "predicted": 0.07482332631479949, "observed": 0.06172130233608186, "confirmed": false }, "custom_track": { "name": "latent_mixture_transport", "file": "custom_track.py", "domain": "diffusion-sampling" } }, "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }