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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the positive-cycle Jacobian penalty on the structurally matched dynamics track using the shared rnn_small architecture and a differentiable two-block hidden-state Jacobian with tr(K²) regularization. The exact core identity tr(K²)=2ab passed, and the trained-model signature showed a tiny 0.0103% reduction in measured closed-walk gain. However, the task metric did not improve significantly: paired delta=-2.224e-6 with permutation p=0.21825, so this benchmark does not establish a real win.", "metrics": { "baseline": "Dynamics/rnn_small; best config lr=0.006, weight_decay=0.0001, epochs=3; test MSE 0.02110967 ± 0.01360108 over 8 seeds.", "idea": "Positive-cycle penalty; best config lr=0.006, weight_decay=0.0001, lambda=0.02, tau=1.0, epochs=3; test MSE 0.02110744 ± 0.01359941; paired delta=-0.0000022243; 7/8 wins; permutation p=0.21825; trace2 0.00261571 versus baseline 0.00261598." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json", "bench_stdout.txt" ], "limitations": "The test used the small built-in dynamics dataset with 200 training and 100 test samples, 3 epochs, two hidden blocks, r=2 only, and tau=1.0. The signature used one representative final-timestep state per trained model. No separate spectral-norm baseline was run; the required standard unregularized baseline was swept over learning rate and weight decay. The mechanism signal is extremely small and should not be treated as strong confirmation.", "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": 0.0001, "epochs": 3 }, "full": { "mean": 0.021109665918629616, "std": 0.013601078749505634, "per_seed": [ 0.03851161524653435, 0.010536076501011848, 0.011643605306744576, 0.015708284452557564, 0.045241355895996094, 0.007054212968796492, 0.011020926758646965, 0.029161250218749046 ], "n": 8 } }, "idea": { "cfg": { "lr": 0.006, "wd": 0.0001, "epochs": 3, "lam": 0.02, "tau": 1.0 }, "mean": 0.02110744162928313, "std": 0.013599407708771062, "per_seed": [ 0.03850656375288963, 0.010544697754085064, 0.011639888398349285, 0.01570560224354267, 0.045238953083753586, 0.007051908411085606, 0.011017857119441032, 0.029154062271118164 ], "n": 8 }, "comparison": { "delta_mean": -2.22428934648633e-06, "idea_wins": 7, "n_pairs": 8, "per_seed_diffs": [ -5.0514936447143555e-06, 8.621253073215485e-06, -3.7169083952903748e-06, -2.682209014892578e-06, -2.4028122425079346e-06, -2.3045577108860016e-06, -3.069639205932617e-06, -7.187947630882263e-06 ], "p_value": 0.21825, "mde": 3.914864826646565e-06, "mde_rel_pct": 0.018545366097867208, "verdict": "no measurable effect", "system_worked": false }, "mechanism_signature": { "prediction": "Training the positive-cycle penalty suppresses measured short closed-walk gain in the recurrent hidden Jacobian.", "observed_baseline_mean_trace2": 0.002615979639813304, "observed_idea_mean_trace2": 0.0026157096144743264, "relative_change_pct": -0.010322149869512742, "math_sanity": { "trace2": 2.8800000000000003, "expected_2ab": 2.8800000000000003, "abs_error": 0.0, "rho": 1.2, "critical_gain": 0.8333333333333334 }, "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }