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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Implemented the Information-Budgeted Reverse-Dynamics Controller on the structurally matched dynamics track using paired GRU systems, a stochastic bottleneck, information regularization, and a reverse-dynamics surrogate. The idea was significantly worse than the tuned baseline: test MSE 0.777351 versus 0.000354, paired delta +0.776997, permutation p=0.0081. The negative information estimate was rejected as invalid evidence, so the mechanism signature is unconfirmed.", "metrics": { "baseline": "rnn_small, best lr=0.006: mean test MSE 0.00035416, std 0.00009526, 8 seeds", "idea": "stochastic bottleneck GRU, lr=0.006 beta=0.01 gamma=0.02: mean test MSE 0.77735104, std 0.04344097, 8 seeds", "paired_delta": "idea - baseline = +0.77699687 MSE; idea wins 0/8; permutation p=0.0081" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py > bench_report.json", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the built-in dynamics pendulum track was tested. The implementation used a short 10-epoch budget and a surrogate reverse-kernel penalty rather than an exact learned transition-kernel KL; no target-distribution mismatch sweep, longer-memory controller, or alternative information estimators were tested.", "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": 0.00115439 }, { "cfg": { "lr": 0.003 }, "mean": 0.00067305 }, { "cfg": { "lr": 0.006 }, "mean": 0.00040645 } ], "full": { "mean": 0.00035416, "std": 9.526e-05, "per_seed": [ 0.00040177, 0.00043161, 0.0002936, 0.00049882, 0.00031439, 0.00029283, 0.00018065, 0.00041964 ], "n": 8 } }, "idea": { "mean": 0.77735104, "std": 0.04344097, "per_seed": [ 0.73138875, 0.81126297, 0.80644882, 0.85207552, 0.79810846, 0.71747315, 0.75193274, 0.7501179 ], "n": 8, "cfg": { "lr": 0.006, "beta": 0.01, "gamma": 0.02 }, "sweep": [ { "cfg": { "lr": 0.001, "beta": 0.01, "gamma": 0.02 }, "mean": 0.80528866 }, { "cfg": { "lr": 0.003, "beta": 0.01, "gamma": 0.02 }, "mean": 0.8011865 }, { "cfg": { "lr": 0.006, "beta": 0.01, "gamma": 0.02 }, "mean": 0.80029401 } ] }, "comparison": { "delta_mean": 0.77699687, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.73098698, 0.81083136, 0.80615522, 0.8515767, 0.79779407, 0.71718032, 0.75175209, 0.74969826 ], "p_value": 0.0081, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "prediction": "A stochastic bottleneck should reduce information with a useful reverse prior.", "predicted_info_nats": -0.08583152, "observed_reverse_mse": 0.82862904, "observed_noise_sigma": 0.24910032, "confirmed": false } }, "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }