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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Implemented Local Characteristic Residual Gating on the registered custom PDE track local_shock_characteristic using matched end-to-end mlp_tiny systems. The idea reduced mean test MSE from 0.00382520 to 0.00176110, with all 8 paired seeds improving and permutation p=0.0081; the trained-model residual-energy signature also confirmed the predicted damping mechanism. This remains evidence on a small synthetic one-step PDE surrogate, not a full finite-volume shock-rollout benchmark.", "metrics": { "baseline": "Tuned no-gate baseline, lr=0.01: mean MSE 0.003825201274594292, std 0.0005842251285976379; per-seed [0.0037032742984592915, 0.004018085543066263, 0.00398260960355401, 0.005000452045351267, 0.003473947523161769, 0.003305204212665558, 0.002951973583549261, 0.004166063386946917].", "idea": "Characteristic gate, selected lr=0.01 alpha=1.0: mean MSE 0.0017611028160899878, std 0.0002040852129581439; per-seed [0.0017537641106173396, 0.0017673540860414505, 0.0015747904544696212, 0.001804820611141622, 0.0014342450303956866, 0.0017936605727300048, 0.0017648270586505532, 0.002195360604673624]." }, "bench_report": { "bench_version": 1, "track": "local_shock_characteristic", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "alpha": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "alpha": 0.0 }, "mean": 0.00868597999215126 }, { "cfg": { "lr": 0.003, "alpha": 0.0 }, "mean": 0.00661642977502197 }, { "cfg": { "lr": 0.01, "alpha": 0.0 }, "mean": 0.004176105372607708 } ], "full": { "mean": 0.003825201274594292, "std": 0.0005842251285976379, "per_seed": [ 0.0037032742984592915, 0.004018085543066263, 0.00398260960355401, 0.005000452045351267, 0.003473947523161769, 0.003305204212665558, 0.002951973583549261, 0.004166063386946917 ], "n": 8 } }, "idea": { "mean": 0.0017611028160899878, "std": 0.0002040852129581439, "per_seed": [ 0.0017537641106173396, 0.0017673540860414505, 0.0015747904544696212, 0.001804820611141622, 0.0014342450303956866, 0.0017936605727300048, 0.0017648270586505532, 0.002195360604673624 ], "n": 8 }, "comparison": { "delta_mean": -0.0020640984585043043, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.0019495101878419518, -0.0022507314570248127, -0.002407819149084389, -0.003195631434209645, -0.0020397024927660823, -0.0015115436399355537, -0.0011871465248987079, -0.0019707024927660827 ], "p_value": 0.0081, "mde": 0.0005027018540061575, "mde_rel_pct": 13.141840596596449, "verdict": "idea better (significant)", "system_worked": true }, "custom_track": { "name": "local_shock_characteristic", "file": "pde_local_track.py", "domain": "pde" }, "baseline_grid": [ { "lr": 0.001, "alpha": 0.0 }, { "lr": 0.003, "alpha": 0.0 }, { "lr": 0.01, "alpha": 0.0 } ], "idea_grid": [ { "lr": 0.001, "alpha": 1.0 }, { "lr": 0.001, "alpha": 2.0 }, { "lr": 0.001, "alpha": 4.0 }, { "lr": 0.003, "alpha": 1.0 }, { "lr": 0.003, "alpha": 2.0 }, { "lr": 0.003, "alpha": 4.0 }, { "lr": 0.01, "alpha": 1.0 }, { "lr": 0.01, "alpha": 2.0 }, { "lr": 0.01, "alpha": 4.0 } ], "idea_sweep": [ { "cfg": { "lr": 0.001, "alpha": 1.0 }, "mean": 0.007479952648282051 }, { "cfg": { "lr": 0.001, "alpha": 2.0 }, "mean": 0.007875820272602141 }, { "cfg": { "lr": 0.001, "alpha": 4.0 }, "mean": 0.008559924317523837 }, { "cfg": { "lr": 0.003, "alpha": 1.0 }, "mean": 0.003235555544961244 }, { "cfg": { "lr": 0.003, "alpha": 2.0 }, "mean": 0.0036418562522158027 }, { "cfg": { "lr": 0.003, "alpha": 4.0 }, "mean": 0.004329836694523692 }, { "cfg": { "lr": 0.01, "alpha": 1.0 }, "mean": 0.0017251823155675083 }, { "cfg": { "lr": 0.01, "alpha": 2.0 }, "mean": 0.0022461695480160415 }, { "cfg": { "lr": 0.01, "alpha": 4.0 }, "mean": 0.002906196634285152 } ], "selected_idea_cfg": { "lr": 0.01, "alpha": 1.0 }, "mechanism_signature": { "quantity": "trained-model input residual energy", "predicted": "characteristic gating suppresses oscillatory residual energy", "observed_raw_rms": 0.6787973418831825, "observed_gated_rms": 0.03473339881747961, "observed_suppression_fraction": 0.9488309740266987, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "pde_local_track.py", "stage2_bench.py", "bench_report.json" ], "limitations": "The benchmark uses a small synthetic one-step local PDE surrogate with a registered custom track. It does not test a high-resolution finite-volume dam-break solver, long-horizon rollout divergence, shock width, negative-depth failures, timestep stability limits, wall-clock speed, FLOPs, or generalization to a separate physical PDE family.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 1, "system_judged": true }