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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a local Hysteretic Multiscale Sequence Router on the structurally matching sequence track, using identical transformer encoders and expert heads for soft-routing baseline and hysteretic routing. The baseline achieved MSE 0.90604 versus 0.91059 for the idea, with paired delta +0.00455 and permutation p=0.7749; therefore there was no significant win. The observed switch counts varied substantially, but the dwell-bound signature was not confirmed because the implementation recorded derivative magnitudes without per-example dwell intervals.", "metrics": { "baseline": "MSE 0.9060439914; best config lr=0.0015, temperature=0.7; 8 seeds", "idea": "MSE 0.9105898216; delta=0.12, tau=2.0; 8 seeds; 3 idea settings evaluated; 3/8 paired wins", "paired_delta": "idea - baseline = +0.0045458302 MSE", "permutation_p_value": 0.7749, "mechanism_signature": { "predicted": "dwell >= delta_eta / L_h", "delta_eta": 0.12, "observed_max_L_h": 0.2711498141, "predicted_min_dwell": 0.4425597723, "switch_counts": [ 0, 191, 0, 322, 0, 174, 0, 0 ], "confirmed": false } }, "bench_report": { "bench_version": 1, "track": "sequence", "model": "routed_transformer", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.0015, "temperature": 0.7 }, "sweep": [ { "cfg": { "lr": 0.0015, "temperature": 0.7 }, "mean": 0.9060439914464951 }, { "cfg": { "lr": 0.003, "temperature": 1.0 }, "mean": 0.9140874296426773 }, { "cfg": { "lr": 0.006, "temperature": 1.4 }, "mean": 0.934324100613594 } ], "full": { "mean": 0.9060439914464951, "std": 0.051469292649903194, "per_seed": [ 0.8886425495147705, 0.8025451302528381, 0.9336193799972534, 0.9243031740188599, 0.9763924479484558, 0.8652306199073792, 0.9555761218070984, 0.9020425081253052 ], "n": 8 } }, "idea": { "mean": 0.9105898216366768, "std": 0.05785899431586753, "per_seed": [ 0.888859212398529, 0.8320866227149963, 0.8571396470069885, 0.9573379158973694, 1.0206561088562012, 0.8799188733100891, 0.9532520771026611, 0.8954681158065796 ], "n": 8, "sweep": [ { "cfg": { "lr": 0.0015, "temperature": 0.7, "delta": 0.08, "tau": 1.5 }, "mean": 0.910589799284935 }, { "cfg": { "lr": 0.0015, "temperature": 0.7, "delta": 0.12, "tau": 2.0 }, "mean": 0.9105898216366768 }, { "cfg": { "lr": 0.0015, "temperature": 0.7, "delta": 0.2, "tau": 3.0 }, "mean": 0.9105898290872574 } ] }, "comparison": { "delta_mean": 0.004545830190181732, "idea_wins": 3, "n_pairs": 8, "p_value": 0.7749, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "prediction": "dwell >= delta/L_h", "delta_eta": 0.12, "L_h_observed": 0.27114981412887573, "predicted_min_dwell": 0.4425597722997692, "confirmed": false } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_hysteretic.py", "files": [ "bench_hysteretic.py", "bench_report.json" ], "limitations": "The benchmark used 400 training and 200 test examples and 10 epochs rather than the larger default dataset/training budget. The router implementation exposes switch counts and maximum finite-difference hysteresis-state derivatives but does not record individual dwell intervals, so the theoretical dwell bound could not be directly tested at neural-network scale. The idea sweep was evaluated, but all three settings were extremely close and the reported idea result used the central setting rather than an independently selected best configuration.", "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": 0, "system_judged": true }