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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented a matched dynamics benchmark for the self-supervised probability-flow consistency intervention using identical rnn_small models, paired seeds, baseline learning-rate sweep, and an equal-sized idea sweep. The trained-model mechanism signature was confirmed: prediction sensitivity decreased by 0.33% (0.0339321 to 0.0338214), but standard test MSE became significantly worse, 0.00183970 versus 0.00183620, with paired delta +3.50e-6 and permutation p=0.0081. Therefore the idea does not win the required benchmark metric.", "metrics": { "baseline": "Dynamics rnn_small, best lr=0.006: test MSE 0.0018362020 ± 0.0005092447 across 8 seeds.", "idea": "Probability-flow consistency loss, best lr=0.006 and weight=0.03: test MSE 0.0018397023 ± 0.0005083829; paired delta +3.5003e-6, p=0.0081; significant worse.", "mechanism_signature": "Trained-model absolute prediction sensitivity under score-transport perturbation: baseline 0.0339321275, idea 0.0338214338, ratio 0.9967378, confirmed=true." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_stage2.py", "files": [ "bench_stage2.py", "bench_report.json" ], "limitations": "Only the built-in 8-step actuated-pendulum dynamics track was tested; no PDE/custom mean-field distribution track, longer horizons, non-Gaussian particle distributions, Heun integration, supervised imitation, or amortized inference-time wall-clock study was evaluated. The intervention is a dynamics-track consistency regularizer inspired by probability flow rather than a full particle rollout controller because the fixed benchmark exposes supervised next-angle regression targets.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "score_weight": 0.0 }, "sweep": [ { "cfg": { "lr": 0.001, "score_weight": 0.0 }, "mean": 0.004586851398926228 }, { "cfg": { "lr": 0.003, "score_weight": 0.0 }, "mean": 0.0030439750116784126 }, { "cfg": { "lr": 0.006, "score_weight": 0.0 }, "mean": 0.0019343624007888138 } ], "full": { "mean": 0.0018362020200584084, "std": 0.0005092446787647265, "per_seed": [ 0.0017339912010356784, 0.0023557941894978285, 0.0013064503436908126, 0.002341213868930936, 0.001163388486020267, 0.001496884971857071, 0.0026453696191310883, 0.0016465234803035855 ], "n": 8 } }, "idea": { "mean": 0.0018397023231955245, "std": 0.0005083828899465245, "per_seed": [ 0.0017356511671096087, 0.0023582950234413147, 0.001307925907894969, 0.0023415545001626015, 0.0011693481355905533, 0.0015009039780125022, 0.0026489796582609415, 0.0016549602150917053 ], "n": 8 }, "comparison": { "delta_mean": 3.500303137116134e-06, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 1.6599660739302635e-06, 2.5008339434862137e-06, 1.4755642041563988e-06, 3.4063123166561127e-07, 5.959649570286274e-06, 4.019006155431271e-06, 3.6100391298532486e-06, 8.436734788119793e-06 ], "p_value": 0.0081, "mde": 2.2160150645813298e-06, "mde_rel_pct": 0.12068470900118276, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "idea_cfg": { "lr": 0.006, "score_weight": 0.03 }, "signature": { "prediction": "probability-flow consistency reduces prediction sensitivity to score transport", "baseline_abs_sensitivity_mean": 0.0339321275241673, "idea_abs_sensitivity_mean": 0.033821433782577515, "ratio_idea_over_baseline": 0.9967377895032464, "n_models": 8, "confirmed": true } }, "protocol_notes": "Dynamics chosen because controlled pendulum rollout is explicitly a stability/control task. Both systems use rnn_small, same datasets, epochs and Adam; only the self-supervised score-transport consistency loss differs." }, "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }