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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the BDD intervention on the structurally matched dynamics track using the shared rnn_small architecture, paired seeds, baseline sweep, and equal learning-rate union grid. The trained-model signature confirmed the mechanism: recurrent cross-block q fell from 2.9283 to 0.7977, near the 0.8 target. However, task MSE worsened significantly from 2.2823e-5 to 4.7575e-5 (paired delta +2.4752e-5, permutation p=0.0081), so the idea did not win the benchmark.", "metrics": { "baseline": "Dynamics rnn_small; best lr=0.006, weight_decay=0.0; 8-seed test MSE mean=2.2822894607e-05, std=2.3053304466e-06.", "idea": "BDD-regularized rnn_small; best tested lr=0.006, lambda=0.1, delta=0.2; 8-seed test MSE mean=4.7575331337e-05, std=2.1643697598e-05.", "paired_delta": "idea - baseline = +2.4752436730e-05; idea wins 0/8; permutation p=0.0081.", "mechanism": "Trained-model recurrent cross-block q mean decreased from 2.9282636810 to 0.7976552317; target=0.8; confirmed=true." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_bdd.py", "files": [ "bench_bdd.py", "bench_report.json", "bench_stdout.txt" ], "limitations": "The intervention uses a recurrent-weight block-Jacobian proxy rather than the full nonlinear GRU residual Jacobian, and constrains only the candidate recurrent gate block. The idea sweep held lambda at 0.1 and varied learning rate; larger architectures, implicit equilibrium solving, rollout-horizon robustness, wall-clock cost, and alternative stability baselines 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": 0.006, "wd": 0.0 }, "sweep_means": [ { "lr": 0.0015, "wd": 0.0, "mean": 0.00032828053372213617 }, { "lr": 0.0015, "wd": 0.0001, "mean": 0.0004147848885622807 }, { "lr": 0.003, "wd": 0.0, "mean": 4.9621185098658316e-05 }, { "lr": 0.003, "wd": 0.0001, "mean": 0.00020817978656850755 }, { "lr": 0.006, "wd": 0.0, "mean": 2.345215943932999e-05 }, { "lr": 0.006, "wd": 0.0001, "mean": 0.00010142060364159988 } ], "full_mean": 2.282289460708853e-05, "full_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 ] }, "idea": { "best_cfg": { "lr": 0.006, "lam": 0.1, "wd": 0.0 }, "mean": 4.757533133670222e-05, "std": 2.1643697598220244e-05, "per_seed": [ 9.76939991232939e-05, 3.8501457311213017e-05, 3.3765736588975415e-05, 2.9315116989891976e-05, 6.082628169679083e-05, 5.370224607759155e-05, 3.0736955523025244e-05, 3.606085738283582e-05 ] }, "comparison": { "delta_mean": 2.475243672961369e-05, "idea_wins": 0, "n_pairs": 8, "p_value": 0.0081, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "baseline_q_mean": 2.9282636810198936, "idea_q_mean": 0.7976552316857172, "observed_reduction": 2.1306084493341766, "target_q": 0.8, "confirmed": true }, "protocol_notes": "Dynamics is structurally matched: controlled pendulum rollout and recurrent state coupling. Baseline and idea share rnn_small, data, Adam, epochs, batch, and learning-rate/weight-decay search space." }, "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }