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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built an end-to-end Parabolic Riesz branch inside the standard transformer_tiny sequence model, using the same forecast task, optimizer, epochs, batch size, and paired seeds as the baseline. The baseline achieved mean test MSE 0.48255 versus 0.49494 for the best Riesz configuration, with paired delta +0.01239 and permutation p=0.42645, hence no significant win. The trained-model mechanism signature showed lower mean output perturbation ratio for the idea (0.1621 vs 0.1800), confirming the predicted robustness signal, but this did not translate into better task performance.", "metrics": { "baseline": "sequence/transformer_tiny; baseline lr sweep {0.001: 0.47026, 0.003: 0.56207, 0.006: 0.78261} on four tuning seeds; best lr=0.001; full 8-seed mean test MSE=0.4825485, std=0.0335456.", "idea": "Parabolic Riesz transformer; union-parity lr sweep full 8-seed means {0.001: 0.49494, 0.003: 0.61534, 0.006: 0.87921}; best lr=0.001; paired delta=+0.0123890 MSE, p=0.42645, 4/8 paired wins. Mechanism signature: mean trained-model output noise ratio 0.16206 vs baseline 0.17996, confirmed=true." }, "bench_report": { "bench_version": 1, "track": "sequence", "model": "transformer_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.47025539726018906 }, { "cfg": { "lr": 0.003 }, "mean": 0.5620733425021172 }, { "cfg": { "lr": 0.006 }, "mean": 0.7826127856969833 } ], "full": { "mean": 0.482548501342535, "std": 0.03354559876849172, "per_seed": [ 0.4842072129249573, 0.42341384291648865, 0.4566255807876587, 0.5167749524116516, 0.49487417936325073, 0.4824545681476593, 0.5386028289794922, 0.4634348452091217 ], "n": 8 } }, "idea": { "mean": 0.4949374906718731, "std": 0.053264005967373906, "per_seed": [ 0.48880892992019653, 0.4039829969406128, 0.4540945589542389, 0.5083529353141785, 0.5644499659538269, 0.5458484292030334, 0.5475071668624878, 0.4464549422264099 ], "n": 8 }, "comparison": { "delta_mean": 0.012388989329338074, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ 0.004601716995239258, -0.019430845975875854, -0.0025310218334198, -0.008422017097473145, 0.06957578659057617, 0.06339386105537415, 0.008904337882995605, -0.016979902982711792 ], "p_value": 0.42645, "mde": 0.02909483793289158, "mde_rel_pct": 6.029412142394932, "verdict": "no significant win", "system_worked": false }, "track_match": "sequence-level correlated multi-token forecast", "idea_best_cfg": { "lr": 0.001 }, "idea_grid": [ { "cfg": { "lr": 0.001 }, "full_mean": 0.4949374906718731 }, { "cfg": { "lr": 0.003 }, "full_mean": 0.6153423972427845 }, { "cfg": { "lr": 0.006 }, "full_mean": 0.8792074918746948 } ], "mechanism_signature": { "mean_baseline_output_noise_ratio": 0.17996257916092873, "mean_idea_output_noise_ratio": 0.16205820441246033, "predicted": "idea should attenuate feature perturbation", "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the built-in sequence track was tested; no PDE, graph, video, traffic, multi-scale, causal temporal-difference, conjugate-gradient approximation, FLOP measurement, or larger-data evaluation was performed. The mechanism signature used separately trained models at lr=0.003 as a robustness probe, while the primary paired comparison selected lr=0.001 via the required sweep.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }