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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented and benchmarked a port-Hamiltonian recurrent system on the registered dynamics track, which is structurally matched to stability/control ideas. The trained-model mechanism signature was confirmed: skew residual 0, minimum dissipation eigenvalue 0.0300000086, and energy identity residual 1.49e-08. However, the idea significantly worsened test MSE versus the tuned GRU baseline (0.80123 vs 0.50968; paired delta +0.29155; p=0.0081), so there is no demonstrated transfer win.", "metrics": { "baseline": "Tuned rnn_small GRU, lr=0.005: 8-seed test MSE mean 0.5096767992, std 0.0985190744.", "idea": "Port-Hamiltonian recurrent model, best shared-grid lr=0.005: 8-seed test MSE mean 0.8012284115, std 0.2042050819; paired delta +0.2915516123; p=0.0081; 0/8 wins." }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.005, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "mean": 0.7906209975481033 }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "mean": 0.6789352595806122 }, { "cfg": { "lr": 0.005, "weight_decay": 0.0 }, "mean": 0.5756678730249405 } ], "full": { "mean": 0.5096767991781235, "std": 0.09851907442454794, "per_seed": [ 0.49524950981140137, 0.5737435817718506, 0.5768014788627625, 0.6568769216537476, 0.31393858790397644, 0.5531363487243652, 0.47284379601478577, 0.4348241686820984 ], "n": 8 } }, "idea": { "mean": 0.8012284114956856, "std": 0.20420508185800218, "per_seed": [ 0.7726240158081055, 1.0544251203536987, 1.041422724723816, 0.9839093685150146, 0.5845463275909424, 0.789264976978302, 0.7381134629249573, 0.4455212950706482 ], "n": 8 }, "comparison": { "delta_mean": 0.2915516123175621, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.2773745059967041, 0.48068153858184814, 0.46462124586105347, 0.3270324468612671, 0.27060773968696594, 0.23612862825393677, 0.2652696669101715, 0.010697126388549805 ], "p_value": 0.0081, "mde": 0.12250781647639007, "mde_rel_pct": 24.03637298655528, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "track_match": "stability/control -> dynamics", "math_sanity": { "max_skew": 0.0, "min_R_eig": 0.03000270389020443, "max_identity_residual": 3.725290298461914e-09, "max_dHdt": -0.000846284267026931 }, "trained_model_samples": 8, "max_skew_residual": 0.0, "min_R_eigenvalue": 0.030000008642673492, "max_energy_identity_residual": 1.4901161193847656e-08, "max_observed_dHdt": -0.0005119751440361142, "predicted": { "skew_zero": true, "R_eigenvalue_ge_epsilon": true, "dHdt_le_zero": true }, "confirmed": true }, "idea_sweep": [ { "cfg": { "lr": 0.001, "weight_decay": 0.0 }, "result": { "mean": 0.8644205369055271, "std": 0.20784380092323357, "per_seed": [ 0.8139765858650208, 1.1354857683181763, 1.0707145929336548, 1.0692869424819946, 0.6739808320999146, 0.8699936866760254, 0.7972943186759949, 0.48463156819343567 ], "n": 8 } }, { "cfg": { "lr": 0.003, "weight_decay": 0.0 }, "result": { "mean": 0.8342878073453903, "std": 0.20629487017615392, "per_seed": [ 0.792961597442627, 1.0980685949325562, 1.055668830871582, 1.029300332069397, 0.6329599618911743, 0.8318073749542236, 0.7700064182281494, 0.4635293483734131 ], "n": 8 } }, { "cfg": { "lr": 0.005, "weight_decay": 0.0 }, "result": { "mean": 0.8012284114956856, "std": 0.20420508185800218, "per_seed": [ 0.7726240158081055, 1.0544251203536987, 1.041422724723816, 0.9839093685150146, 0.5845463275909424, 0.789264976978302, 0.7381134629249573, 0.4455212950706482 ], "n": 8 } } ], "parameter_counts": { "baseline": 13313, "idea": 3142 } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "The run used a reduced MVP budget after the initial full-scale attempt exceeded the time budget: 2 training epochs and 100/60 train/test samples. Only the built-in dynamics track was tested; adaptive ODE solvers, longer training, larger PH hidden states, forced energy-balance losses, and a separate Hamiltonian baseline were not evaluated. The recurrent update is a discrete approximation, so the continuous-time energy guarantee does not automatically ensure discrete-step energy monotonicity.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }