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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented action-calibrated cycle-hopping-inspired recurrent stabilization as a hidden-state contraction regularizer on the structurally matched dynamics track. Across 8 paired seeds, the idea had higher test MSE than the tuned baseline (0.00144999 vs 0.00138748; delta +0.00006251) with permutation p=0.82325, and the trained-model mechanism signature was not confirmed because hidden gain increased slightly (0.56313 vs 0.56012).", "metrics": { "baseline": "rnn_small dynamics, tuned lr=0.01 and weight_decay=0.0001; 8-seed test MSE mean=0.0013874755, std=0.000377963", "idea": "rnn_small plus recurrent contraction penalty lambda=0.02, best tested lr=0.01 and weight_decay=0.0001; 8-seed test MSE mean=0.0014499884, std=0.000625824", "delta_mean": "+0.0000625129 MSE (idea worse; lower is better)", "permutation_p_value": 0.82325, "idea_wins": 4, "n_pairs": 8, "mechanism_signature": { "baseline_hidden_gain": 0.5601233, "idea_hidden_gain": 0.5631289, "relative_gain_change": 0.005366, "confirmed": false } }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "weight_decay": 0.0001 }, "sweep_means": [ { "lr": 0.001, "weight_decay": 0.0, "mean": 0.004103701 }, { "lr": 0.001, "weight_decay": 0.0001, "mean": 0.0041049906 }, { "lr": 0.003, "weight_decay": 0.0, "mean": 0.0033861722 }, { "lr": 0.003, "weight_decay": 0.0001, "mean": 0.0033592699 }, { "lr": 0.01, "weight_decay": 0.0, "mean": 0.0014241317 }, { "lr": 0.01, "weight_decay": 0.0001, "mean": 0.0014055169 } ], "full_mean": 0.0013874755, "full_std": 0.000377963, "per_seed": [ 0.000841497, 0.001842196, 0.001026598, 0.001911777, 0.001014577, 0.001323072, 0.001677272, 0.001462815 ] }, "idea": { "cfg": { "lr": 0.01, "weight_decay": 0.0001, "lambda": 0.02 }, "mean": 0.0014499884, "std": 0.000625824, "per_seed": [ 0.001108559, 0.001156428, 0.000963121, 0.002061812, 0.000951198, 0.00178341, 0.000857345, 0.002718034 ] }, "comparison": { "delta_mean": 6.25129e-05, "idea_wins": 4, "n_pairs": 8, "p_value": 0.82325, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": "cycle-stabilizing intervention lowers local recurrent hidden-state gain", "baseline_hidden_gain": 0.5601233, "idea_hidden_gain": 0.5631289, "confirmed": false } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_stage2.py", "files": [ "bench_stage2.py", "bench_report.json" ], "limitations": "The built-in dynamics task is an actuated damped-pendulum one-step-through-horizon regression, not a dataset with explicit multimodal recurring limit-cycle modes or labeled transition hazards. The implemented intervention is a local hidden-state contraction proxy rather than a learned Freidlin-Wentzell action estimator, explicit noise calibration, or long-horizon stochastic mode-switching objective; only 12 training epochs, one lambda value, and the standard 8-seed protocol were tested.", "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }