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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented adaptive conformal error-radius weighting as a training-loss intervention on the structurally matched controlled-pendulum dynamics track, using the same rnn_small architecture and paired eight-seed protocol. The trained-model signature confirmed the target exceedance quantitatively (0.197 observed versus alpha=0.2), and recursion sanity checks passed, but the idea had higher test MSE than the swept baseline (2.46837e-05 versus 2.28229e-05; delta=+1.86082e-06, permutation p=0.1888), so there was no significant win.", "metrics": { "baseline": "Best lr=0.006; 8-seed mean test MSE=2.282289460708853e-05, std=2.305330446600017e-06", "idea": "Best lr=0.006, alpha=0.2, gamma=0.05, strength=0.5; 8-seed mean test MSE=2.4683711671968922e-05, std=2.8119869264952184e-06", "delta_mean": 1.8608170648803934e-06, "permutation_p_value": 0.1888, "idea_wins": 3, "mechanism_signature": { "trained_model_retested_exceedance": 0.197, "target_alpha": 0.2, "confirmed": true, "stationary_abs_error": 0.00028000000000000247, "ramp_observed_vs_predicted_steps": "19 vs 19", "recovery_observed_vs_predicted_steps": "88 vs 88" } }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.0005152521189302206 }, { "cfg": { "lr": 0.003 }, "mean": 4.9621185098658316e-05 }, { "cfg": { "lr": 0.006 }, "mean": 2.345215943932999e-05 } ], "full": { "mean": 2.282289460708853e-05, "std": 2.305330446600017e-06, "per_seed": [ 2.4922346710809506e-05, 2.6788266040966846e-05, 1.934224746946711e-05, 2.2755777536076494e-05, 2.3468126528314315e-05, 2.385292282269802e-05, 2.0721481632790528e-05, 2.073198811558541e-05 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.006, "alpha": 0.2, "gamma": 0.05, "strength": 0.5 }, "mean": 2.4683711671968922e-05, "std": 2.8119869264952184e-06, "per_seed": [ 2.5581410227459855e-05, 2.4755079721217044e-05, 1.9225306459702558e-05, 2.4516248231520876e-05, 2.3418831915478222e-05, 2.4992359612951986e-05, 2.4735531042097136e-05, 3.0244926165323704e-05 ], "n": 8 }, "comparison": { "delta_mean": 1.8608170648803934e-06, "idea_wins": 3, "n_pairs": 8, "p_value": 0.1888, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "math_sanity": { "stationary_target_alpha": 0.2, "stationary_observed_exceedance": 0.20028, "stationary_abs_error": 0.00028000000000000247, "ramp_observed_steps": 19, "ramp_predicted_steps": 19, "recovery_observed_steps": 88, "recovery_predicted_steps": 88, "quantile_check": 0.85 }, "trained_model_signature": { "test_abs_error_mean": 0.003862829878926277, "test_abs_error_q90": 0.007725757360458375, "retested_exceedance": 0.197, "target_alpha": 0.2, "final_radius": 0.0, "confirmed": true, "radius_training_start_end": [ 0.19, 0.0 ] }, "intervention": "adaptive conformal exceedance-weighted MSE", "structural_match": "controlled pendulum multi-step dynamics" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "Tested one built-in matched dynamics task and one adaptive-loss formulation; did not test PPO/action masking, explicit multi-agent collision rollouts, FIFO delayed prediction buffers, ensemble estimator quantiles in the neural benchmark, or broader gamma/alpha/strength sweeps.", "system_verdict": "failed", "practical_verdict": "inconclusive", "mechanism_ok": 0, "system_judged": true }