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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Forcing-Consistency Training Constraint was implemented with the shared rnn_small model on the registered dynamics track. The math check passed and the trained-model signature confirmed lower consistency proxy and safety hinge, but test MSE was significantly worse: 0.00250149 versus 0.00105418, paired delta +0.00144731 and permutation p=0.01485.", "metrics": { "baseline": "mean test MSE 0.0010541816263867076 over 8 seeds; best_cfg={lr:0.03,weight_decay:0.0}.", "idea": "mean test MSE 0.002501493159797974 over 8 seeds; best_cfg={lr:0.01,weight_decay:0.0,fc_weight:0.08,safety_weight:0.08}.", "comparison": "delta_mean=0.0014473115334112663, p_value=0.01485, idea_wins=1/8, verdict=idea worse (significant)." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_fc_dynamics.py", "files": [ "bench_fc_dynamics.py", "bench_report.json", "run3.log" ], "limitations": "The registered dynamics track lacks explicit latent-state and forcing-mask labels, so the safety term uses coarse theta observation groups and angular correction candidates. The experiment used 12 epochs and 400/200 train/test examples; actor-critic training, held-out hidden-state permutations, and real control safety were not tested.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.03, "weight_decay": 0.0 }, "sweep": [ { "cfg": { "lr": 0.01, "weight_decay": 0.0 }, "mean": 0.0014865802077110857 }, { "cfg": { "lr": 0.01, "weight_decay": 0.0001 }, "mean": 0.0014641905727330595 }, { "cfg": { "lr": 0.03, "weight_decay": 0.0 }, "mean": 0.0012827248574467376 }, { "cfg": { "lr": 0.03, "weight_decay": 0.0001 }, "mean": 0.0013233663485152647 }, { "cfg": { "lr": 0.1, "weight_decay": 0.0 }, "mean": 0.006263854738790542 }, { "cfg": { "lr": 0.1, "weight_decay": 0.0001 }, "mean": 0.014768014312721789 } ], "full": { "mean": 0.0010541816263867076, "std": 0.0005985854110248448, "per_seed": [ 0.0023917919024825096, 0.0013993943575769663, 0.0006620299536734819, 0.0006776832160539925, 0.0012405221350491047, 0.001023401622660458, 0.000684684026055038, 0.0003539457975421101 ], "n": 8 } }, "idea": { "mean": 0.002501493159797974, "std": 0.000829447102954745, "per_seed": [ 0.0022331809159368277, 0.001679917797446251, 0.0021533623803406954, 0.003017958952113986, 0.0014837837079539895, 0.003271000226959586, 0.0020722420886158943, 0.0041004992090165615 ], "n": 8 }, "comparison": { "delta_mean": 0.0014473115334112663, "idea_wins": 1, "n_pairs": 8, "per_seed_diffs": [ -0.00015861098654568195, 0.00028052343986928463, 0.0014913324266672134, 0.0023402757360599935, 0.00024326157290488482, 0.002247598604299128, 0.0013875580625608563, 0.0037465534114744514 ], "p_value": 0.01485, "mde": 0.0011002418315854009, "mde_rel_pct": 104.36928552402951, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "math_check": { "js_equal": 0.0, "js_unequal": 0.27539611524877045, "js_symmetric_error": 0.0, "intersection_feasible": true, "intersection_empty": true, "passed": true }, "prediction": "FC should lower observational kappa on trained recurrent models", "baseline_kappa_mean": 0.005694788647815585, "idea_kappa_mean": 0.00544616701794967, "baseline_safety_hinge_mean": 0.2168501764535904, "idea_safety_hinge_mean": 0.1869419850409031, "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }