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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Implemented a wavelet-domain multiscale training loss with the same flattened mlp_tiny architecture, data, epochs, batch size, and learning-rate union as the baseline. Across 8 paired seeds, the idea was significantly worse: test MSE 0.0588950552 versus 0.0006244598 for baseline, paired delta +0.0582705954, permutation p=0.0081. The NN-scale mechanism signature was not confirmed because the observed detail-to-pixel residual MSE ratio was 2.29393845, above the predicted threshold of 1.", "metrics": { "baseline": "Best config lr=0.01, weight_decay=0.0; 8-seed test MSE mean 0.0006244598, std 0.0001381612.", "idea": "Best config lr=0.01, weight_decay=0.0, detail_weight=2.0; 8-seed test MSE mean 0.0588950552, std 0.0075814336.", "paired_delta": "delta_mean=+0.0582705954; idea_wins=0/8; permutation p-value=0.0081; verdict=idea worse (significant).", "mechanism_signature": "Mean trained-model detail residual MSE divided by pixel residual MSE = 2.29393845; confirmed=false." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_wavelet_bench.py", "files": [ "stage2_wavelet_bench.py", "bench_report.json", "stage2_run.log" ], "limitations": "Only the matched multiscale_diffusion_pde track was tested, with 400 training examples, 100 test examples, 20 epochs, three configurations, and an mlp_tiny backbone. The implementation tests a wavelet-domain training loss rather than a learned conditional sampler at inference; CNN image EBMs, finite-step Langevin correction, likelihood, FID, wall-clock sampling, and larger-resolution scaling were not tested.", "bench_report": { "bench_version": 1, "track": "multiscale_diffusion_pde", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0 }, "full": { "mean": 0.0006244597789191175, "std": 0.00013816121674020655, "per_seed": [ 0.0005983817973174155, 0.0007256286917254329, 0.0006302266265265644, 0.00048197503201663494, 0.0006429867353290319, 0.00040344681474380195, 0.000893245916813612, 0.0006197866168804467 ] }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.03494249051436782 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.0028896931326016784 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0 }, "mean": 0.0006090530368965119 } ] }, "idea": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0, "detail_weight": 2.0 }, "mean": 0.0588950552046299, "std": 0.007581433628093914, "per_seed": [ 0.048095669597387314, 0.06269505620002747, 0.060969557613134384, 0.0601036362349987, 0.04443567618727684, 0.06491053104400635, 0.06635342538356781, 0.06359688937664032 ] }, "comparison": { "delta_mean": 0.05827059542571078, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.0474972878000699, 0.06196942750830203, 0.06033933098660782, 0.05962166120298207, 0.04379268945194781, 0.06450708422926255, 0.0654601794667542, 0.06297710275975987 ], "p_value": 0.0081, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "claim": "multiscale objective reduces fine/detail residuals at NN scale", "predicted": "detail residual is not worse than pixel residual", "mean_detail_to_pixel_ratio": 2.2939384522012953, "confirmed": false }, "custom_track": { "name": "multiscale_diffusion_pde", "file": "bench/custom_tracks/multiscale_diffusion_pde.py", "domain": "pde" } }, "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }