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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 7, "verdict": "Built a differentiable equilibrium-projection wrapper around the benchmark rnn_small model on the structurally matched dynamics track. Across 8 paired seeds, test MSE decreased from 0.0002709502 to 0.0000083549, with delta_mean=-0.0002625953 and permutation p=0.0081; the trained-model residual signature decreased from 0.71496 to 0.0 and was confirmed. The projection uses a nominal kinematic one-step equality rather than the full latent pendulum equations, so the large gain may partly reflect an informative state-derived target.", "metrics": { "baseline": "dynamics/rnn_small; best lr=0.012, epochs=25; mean test MSE 0.00027095018594991416, std 0.00010329659269599283", "idea": "Projected dynamics/rnn_small; best lr=0.003, one projection step, epochs=25; mean test MSE 0.000008354870317361929, std 0.0000008429703290269694", "paired_delta_mean": -0.00026259531563255223, "permutation_p_value": 0.0081, "idea_wins": 8 }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.012, "epochs": 25, "steps": 0 }, "sweep": [ { "cfg": { "lr": 0.003, "epochs": 25, "steps": 0 }, "mean": 0.0008186178019968793 }, { "cfg": { "lr": 0.006, "epochs": 25, "steps": 0 }, "mean": 0.0005262107733869925 }, { "cfg": { "lr": 0.012, "epochs": 25, "steps": 0 }, "mean": 0.0003371372222318314 } ], "full": { "mean": 0.00027095018594991416, "std": 0.00010329659269599283, "per_seed": [ 0.0003306560392957181, 0.0003460621228441596, 0.00033211964182555676, 0.0003397110849618912, 0.0003853111120406538, 0.00018891783838625997, 7.910988642834127e-05, 0.00016571376181673259 ], "n": 8 } }, "idea": { "mean": 8.354870317361929e-06, "std": 8.429703290269694e-07, "per_seed": [ 9.509363735560328e-06, 7.033356723695761e-06, 8.578040251450147e-06, 7.191956683527678e-06, 9.002241313282866e-06, 9.134431820712052e-06, 8.445092134934384e-06, 7.944479875732213e-06 ], "n": 8 }, "comparison": { "delta_mean": -0.00026259531563255223, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.00032114667556015775, -0.00033902876612046384, -0.0003235416015741066, -0.0003325191282783635, -0.00037630887072737096, -0.00017978340656554792, -7.066479281340688e-05, -0.00015776928194100037 ], "p_value": 0.0081, "mde": 9.240409757793939e-05, "mde_rel_pct": 34.10370827088508, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "track_match": "dynamics: controlled pendulum rollout", "idea_config_sweep": [ { "cfg": { "lr": 0.003, "epochs": 25, "steps": 1 }, "mean": 8.078179348558479e-06 }, { "cfg": { "lr": 0.006, "epochs": 25, "steps": 2 }, "mean": 8.078180940174207e-06 }, { "cfg": { "lr": 0.012, "epochs": 25, "steps": 3 }, "mean": 8.078180940174207e-06 } ], "best_idea_cfg": { "lr": 0.003, "epochs": 25, "steps": 1 }, "mechanism": { "claim": "implicit equality projection removes the measured equilibrium residual", "trained_model_raw_residual_mean": 0.7149586826562881, "trained_model_projected_residual_mean": 0.0, "residual_ratio": 0.0, "confirmed": true } }, "protocol_notes": "8 paired seeds; baseline and idea share rnn_small, dataset sizes, lr/epoch union, and train_model." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_stage2.py", "files": [ "bench_stage2.py", "bench_report.json" ], "limitations": "Only the built-in controlled-pendulum dynamics track was tested; no AC 14-bus topology, sparse Jacobian solves, inequality restoration, multiple equilibria, outage robustness, wall-clock/FLOP accounting, or full latent pendulum equilibrium projection was evaluated.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 0, "system_judged": true }