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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Implemented Degree-Calibrated Stable Residual Flow as a local residual recurrent architecture on the structurally matched actuated-pendulum dynamics track. The trained-model radial derivative matched -ar^(m+1) with relative absolute error 1.43e-7 and correlation 0.9999999999999342, confirming the mechanism quantitatively. However, the idea's best result was significantly worse than the tuned baseline (MSE 0.00041295 vs 0.00025840, paired delta +0.00015455, permutation p=0.0163), so there was no neural-task accuracy win.", "metrics": { "baseline": "Best unconstrained residual RNN, lr=0.006, dt=0.25: test MSE mean 0.0002584023, std 0.0000950261 across 8 seeds.", "idea": "Best degree-calibrated residual RNN setting among tested settings, lr=0.006, dt=0.25, m=0: test MSE mean 0.0004129522, std 0.0001071165 across 8 seeds. Paired delta idea-baseline +0.0001545499; 1/8 idea wins; permutation p=0.0163; verdict idea worse (significant). Mechanism signature: predicted radial mean -4.6938066444, observed -4.6938066473, mean absolute error 5.94e-7, relative error 1.43e-7, correlation 0.9999999999999342, confirmed=true." }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "residual_rnn_shared", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "dt": 0.25, "m": 0 }, "sweep": [ { "cfg": { "lr": 0.0015, "dt": 0.25, "m": 0 }, "mean": 0.0012726996355922893 }, { "cfg": { "lr": 0.003, "dt": 0.25, "m": 0 }, "mean": 0.0005508951580850407 }, { "cfg": { "lr": 0.006, "dt": 0.25, "m": 0 }, "mean": 0.0002624734515848104 } ], "full": { "mean": 0.00025840229682216886, "std": 9.502614806341116e-05, "per_seed": [ 0.00021290902805048972, 0.00015823994181118906, 0.00023945324937812984, 0.00043929158709943295, 0.00016651710029691458, 0.0003356700181029737, 0.000337076693540439, 0.00017806075629778206 ], "n": 8 } }, "idea": { "mean": 0.00041295221308246255, "std": 0.00010711652432851176, "per_seed": [ 0.000396863033529371, 0.00026599428383633494, 0.0004898399929516017, 0.0006024055765010417, 0.00039211593684740365, 0.00029558956157416105, 0.0005119965644553304, 0.00034881275496445596 ], "n": 8 }, "comparison": { "delta_mean": 0.00015454991626029368, "idea_wins": 1, "n_pairs": 8, "per_seed_diffs": [ 0.0001839540054788813, 0.00010775434202514589, 0.0002503867435734719, 0.0001631139894016087, 0.00022559883655048907, -4.008045652881265e-05, 0.00017491987091489136, 0.0001707519986666739 ], "p_value": 0.0163, "mde": 7.473365296214683e-05, "mde_rel_pct": 28.92143525085543, "verdict": "idea worse (significant)", "system_worked": false }, "idea_sweep": [ { "cfg": { "lr": 0.006, "dt": 0.25, "m": 0 }, "result": { "mean": 0.00041295221308246255, "std": 0.00010711652432851176, "per_seed": [ 0.000396863033529371, 0.00026599428383633494, 0.0004898399929516017, 0.0006024055765010417, 0.00039211593684740365, 0.00029558956157416105, 0.0005119965644553304, 0.00034881275496445596 ], "n": 8 } }, { "cfg": { "lr": 0.0015, "dt": 0.25, "m": 1 }, "result": { "mean": 0.011022639810107648, "std": 0.0020031189115314024, "per_seed": [ 0.011321489699184895, 0.008100657723844051, 0.01179476361721754, 0.01137582492083311, 0.011110322549939156, 0.010274713858962059, 0.015252543613314629, 0.008950802497565746 ], "n": 8 } }, { "cfg": { "lr": 0.006, "dt": 0.25, "m": 1 }, "result": { "mean": 0.0010292112237948459, "std": 0.00026172876255944693, "per_seed": [ 0.0011666412465274334, 0.00048204700578935444, 0.001413801102899015, 0.0010645093861967325, 0.0009550628019496799, 0.0008412864408455789, 0.0012185709783807397, 0.0010917708277702332 ], "n": 8 } } ], "custom_track": null, "selection_note": "m=1 idea; baseline sweep and idea sweep use identical lr union and epochs", "mechanism_signature": { "quantity": "trained radial derivative vs -ar^(m+1)", "predicted_mean": -4.693806644385415, "observed_mean": -4.693806647272514, "mean_abs_error": 5.941758198397501e-07, "relative_abs_error": 1.4333428362199376e-07, "correlation": 0.9999999999999342, "n_samples": 22400, "hidden_norm_mean": 2.9794825143207397, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_degree_flow.py", "files": [ "bench_degree_flow.py", "bench_report.json" ], "limitations": "Only the structurally matched built-in dynamics track was tested; CIFAR, sequence, and tabular tracks were not run. The experiment used a small residual RNN and 18 epochs with dt fixed at 0.25; explicit Euler instability boundaries, adaptive ODE solvers, longer horizons, spectral-normalization baseline, FLOPs, and hidden-state decay after input removal were not evaluated. The m=1 configuration was much less accurate than m=0, and the baseline/idea architecture was a local residual-RNN implementation rather than the harness rnn_small GRU.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }