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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented Energy-Gradient Neural Flow on the structurally matched dynamics track with 8 paired seeds, a shared learning-rate union, and equal 18-epoch budgets. The trained energy-gradient model showed monotone energy on the evaluated rollout (fraction 1.0) and reduced gradient norm, confirming the qualitative mechanism, but its test MSE was significantly worse than the matched unconstrained recurrent vector-field baseline. Therefore the stability effect is real in this benchmark, but there is no task-performance win.", "metrics": { "baseline": "Matched VectorCell baseline: test MSE mean 0.0001647120, std 0.0000448917; best lr 0.002; 8 seeds.", "idea": "Energy-gradient recurrent model: test MSE mean 0.0007074636, std 0.0000586212; best lr 0.003; 8 seeds; energy nonincreasing fraction 1.0; energy mean -2.29325 -> -3.06860; gradient norm 1.05077 -> 0.85472.", "paired_comparison": "Idea minus baseline MSE delta +0.0005427515; 0/8 idea wins; permutation p=0.0081; verdict idea worse (significant)." }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.002 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.00028059784381184727 }, { "cfg": { "lr": 0.002 }, "mean": 0.0001786892862583045 }, { "cfg": { "lr": 0.003 }, "mean": 0.00018342551447858568 } ], "full": { "mean": 0.000164712047080684, "std": 4.489169459326852e-05, "per_seed": [ 0.00020323635544627905, 0.00021873260266147554, 0.00013271479110699147, 0.0001600733958184719, 0.0001428304531145841, 0.00023414976021740586, 0.00011625964543782175, 0.00010969937284244224 ], "n": 8 } }, "idea": { "mean": 0.000707463565049693, "std": 5.862115619486818e-05, "per_seed": [ 0.000648840912617743, 0.0008204824407584965, 0.0006285406416282058, 0.0007532027084380388, 0.0007002238417044282, 0.0007392585976049304, 0.0007051959400996566, 0.0006639634375460446 ], "n": 8 }, "comparison": { "delta_mean": 0.000542751517969009, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.00044560455717146397, 0.000601749838097021, 0.0004958258505212143, 0.0005931293126195669, 0.0005573933885898441, 0.0005051088373875245, 0.0005889362946618348, 0.0005542640647036023 ], "p_value": 0.0081, "mde": 4.639948729729596e-05, "mde_rel_pct": 28.17006303404585, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "predicted": "energy should not increase under sufficiently small Euler steps", "observed_energy_nonincreasing_fraction": 1.0, "observed_energy_first_last": [ -2.293245553970337, -3.0686049461364746 ], "observed_grad_norm_first_last": [ 1.0507688522338867, 0.8547213077545166 ], "predicted_boundary_eta_L": 2.0, "observed_local_energy_boundary": "not estimated (learned Hessian unavailable in budget)", "confirmed": true }, "protocol_notes": { "track_choice": "dynamics matches stability/control/Lyapunov structure", "shared_lr_union": [ 0.001, 0.002, 0.003 ], "epochs": 18, "batch": 128, "baseline_architecture": "matched VectorCell recurrent scaffold (vector field)", "idea_architecture": "matched recurrent hidden width with scalar energy gradient" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 energy_gradient_bench.py", "files": [ "energy_gradient_bench.py", "bench_report.json", "bench_run.log", "bench_run_matched.log" ], "limitations": "Only the built-in dynamics rollout task was tested. The local Hessian Lipschitz constant and empirical Euler instability boundary were not estimated; the mechanism signature evaluated one trained idea model on a 64-example test subset. Wall-clock speed, longer horizons, alternative energy parameterizations, and larger models were not tested.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }