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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 7, "verdict": "Built and trained a four-expert stable spectral memory-lift recurrent model on the structurally matched actuated-pendulum dynamics track using the canonical bench training loop and eight paired seeds. It achieved mean test MSE 1.8605e-05 versus 4.3304e-04 for the GRU baseline; all 8 paired differences favored the idea and the permutation p-value was 0.0081, so the harness verdict is a significant win. The trained-model signature confirmed a predicted slowest memory mode of 0.3353 and bounded observed state norm 2.5836.", "metrics": { "baseline": "GRU, lr=0.01 selected by sweep: mean MSE 0.0004330402, std 0.0001868057", "idea": "SpectralMemoryLift, lr=0.01 selected by matched sweep: mean MSE 0.0000186052, std 0.0000283193", "paired_delta_mean": -0.00041443498906801324, "permutation_p_value": 0.0081, "idea_wins": "8/8", "mechanism_signature": { "predicted_slowest_memory_mode": 0.3352634608745575, "observed_max_state_norm": 2.583609104156494, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "The test used the built-in 8-step dynamics window, 1000 training examples, 300 test examples, and 12 epochs rather than long-horizon rollout or language/SSM benchmarks. The idea model had 16077 parameters versus 3585 for the GRU, so parameter-count equality and throughput were not tested; only optimizer and learning-rate search budgets were matched. Explicit spectral penalties/projection were not measured.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.004247799690347165 }, { "cfg": { "lr": 0.003 }, "mean": 0.0018907261837739497 }, { "cfg": { "lr": 0.01 }, "mean": 0.0005508567701326683 } ], "full": { "mean": 0.00043304020073264837, "std": 0.00018680572581609252, "per_seed": [ 0.000348469679011032, 0.00044166683801449835, 0.0005423855036497116, 0.0008709050598554313, 0.00039059933624230325, 0.0003389842459000647, 0.0002937567769549787, 0.00023755416623316705 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 5.264711217023432e-05 }, { "cfg": { "lr": 0.003 }, "mean": 1.748438717186218e-05 }, { "cfg": { "lr": 0.01 }, "mean": 9.75380351064814e-06 } ], "full": { "mean": 1.860521166463513e-05, "std": 2.831933136541298e-05, "per_seed": [ 1.025223082251614e-05, 5.093114396004239e-06, 3.275605195085518e-06, 2.0394263628986664e-05, 5.965866421320243e-06, 7.376182111329399e-06, 9.230343130184337e-05, 4.180999439995503e-06 ], "n": 8 } }, "comparison": { "delta_mean": -0.00041443498906801324, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.00033821744818851585, -0.0004365737236184941, -0.0005391098984546261, -0.0008505107962264447, -0.000384633469820983, -0.0003316080637887353, -0.00020145334565313533, -0.00023337316679317155 ], "p_value": 0.0081, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "predicted_slowest_memory_mode": 0.3352634608745575, "observed_max_state_norm": 2.583609104156494, "bounded_observed_state": true, "confirmed": true }, "track_justification": "Dynamics is structurally matched: the task is actuated pendulum forecasting and the intervention is a stable recurrent memory state.", "search_space_parity": { "baseline_grid": [ 0.001, 0.003, 0.01 ], "idea_grid": [ 0.001, 0.003, 0.01 ], "selected_idea_lr": 0.01 }, "parameterization": { "baseline_params": 3585, "idea_params": 16077, "epochs": 12, "n_train": 1000, "n_test": 300 } }, "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 1, "system_judged": true }