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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Built and evaluated a matched singular-value constitutive-energy benchmark with an invariant MLP baseline and a monotone ICNN trained using the lower-envelope penalty. The trained ICNN had 0% Jensen and monotonicity violations, confirming the structural prediction, but its test MSE was 0.4852 versus 0.01749 for the tuned baseline; the paired difference was +0.4677 with permutation p=0.0081, a significant loss. The mechanism works structurally but did not transfer into a task-metric win.", "metrics": { "baseline": "Invariant MLP width 16, tuned lr=0.006, 8-seed test MSE mean 0.0174905, std 0.0038865", "idea": "Monotone singular-value ICNN width 16 with lower-envelope weight 4.0, selected lr=0.006, 8-seed test MSE mean 0.4851573, std 0.0234776", "paired_delta": "idea - baseline = +0.4676668 MSE", "permutation_p_value": 0.0081, "baseline_jensen_violation": 0.4740000367, "idea_jensen_violation": 0.0, "baseline_monotonicity_violation": 0.0, "idea_monotonicity_violation": 0.0, "baseline_lower_violation": 0.1880000085, "idea_lower_violation": 1.0 }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "results/bench_report.json" ], "limitations": "The custom track uses sampled 2x2 deformation-like matrices and an analytic synthetic isotropic energy, not a finite-element or Saint Venant-Kirchhoff simulation. Stress accuracy, differentiable simulator optimization, larger matrix dimensions, FLOP-normalized inference, and alternative ICNN widths or lower-envelope weights were not tested. The benchmark implementation uses a local custom training loop for the proposed loss and bench.train_model for the baseline.", "bench_report": { "bench_version": 1, "track": "singular_value_constitutive_energy", "model": "shared_invariant_mlp_width16", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.022746754344552755 }, { "cfg": { "lr": 0.003 }, "mean": 0.01957778725773096 }, { "cfg": { "lr": 0.006 }, "mean": 0.018540473887696862 } ], "full": { "mean": 0.017490515718236566, "std": 0.003886538162934768, "per_seed": [ 0.015612582676112652, 0.01946304365992546, 0.019211620092391968, 0.01987464912235737, 0.022817162796854973, 0.015229465439915657, 0.009119716472923756, 0.01859588548541069 ], "n": 8 } }, "idea": { "mean": 0.48515734821558, "std": 0.02347761538492244, "per_seed": [ 0.4996644854545593, 0.48556891083717346, 0.44942617416381836, 0.46209976077079773, 0.4696982502937317, 0.5224891304969788, 0.5122184753417969, 0.4800935983657837 ], "n": 8 }, "comparison": { "delta_mean": 0.4676668324973434, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.4840519027784467, 0.466105867177248, 0.4302145540714264, 0.44222511164844036, 0.4468810874968767, 0.5072596650570631, 0.5030987588688731, 0.461497712880373 ], "p_value": 0.0081, "mde": 0.02367950289717046, "mde_rel_pct": 135.38481814164533, "verdict": "idea worse (significant)", "system_worked": false }, "custom_track": { "name": "singular_value_constitutive_energy", "file": "stage2_bench.py", "domain": "constitutive mechanics" }, "idea_sweep": [ { "cfg": { "lr": 0.001 }, "mean": 7.160933494567871 }, { "cfg": { "lr": 0.003 }, "mean": 2.1708776354789734 }, { "cfg": { "lr": 0.006 }, "mean": 0.4741898328065872 } ], "mechanism_signature": { "baseline_jensen_violation": 0.4740000367164612, "idea_jensen_violation": 0.0, "baseline_monotonicity_violation": 0.0, "idea_monotonicity_violation": 0.0, "baseline_lower_violation": 0.18800000846385956, "idea_lower_violation": 1.0, "prediction": "trained nonnegative ICNN should eliminate Jensen and coordinatewise monotonicity violations", "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }