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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented a Coxeter folding recurrent model and evaluated it on the structurally matched dynamics track using the required paired 8-seed protocol, baseline sweep, and shared learning-rate union. The fold reversibility signature was confirmed quantitatively, but task MSE was dramatically worse than the tuned GRU: 0.631347 versus 0.000416, with significant degradation (delta_mean 0.630931, p=0.0081).", "metrics": { "baseline": "dynamics/rnn_small, best lr=0.01: test MSE mean 0.00041624879668233916, std 0.00018933904234880896", "idea": "CoxeterFoldRNN, best lr=0.01: test MSE mean 0.6313471160829067, std 0.09825334037840372", "comparison": "idea minus baseline mean delta 0.6309308672862244; 0/8 idea wins; permutation p-value 0.0081; verdict idea worse (significant)" }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.001552814821479842 }, { "cfg": { "lr": 0.003 }, "mean": 0.0009541053732391447 }, { "cfg": { "lr": 0.01 }, "mean": 0.0004885061644017696 } ], "full": { "mean": 0.00041624879668233916, "std": 0.00018933904234880896, "per_seed": [ 0.0003217894700355828, 0.00039041441050358117, 0.0003688229189720005, 0.0008729978580959141, 0.00046312736230902374, 0.00017730507533997297, 0.00040199048817157745, 0.0003335427900310606 ], "n": 8 } }, "idea": { "mean": 0.6313471160829067, "std": 0.09825334037840372, "per_seed": [ 0.5460999608039856, 0.6500953435897827, 0.7589279413223267, 0.6426548957824707, 0.5884038805961609, 0.44923338294029236, 0.7648789286613464, 0.6504825949668884 ], "n": 8 }, "comparison": { "delta_mean": 0.6309308672862244, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.54577817133395, 0.6497049291792791, 0.7585591184033548, 0.6417818979243748, 0.5879407532338519, 0.4490560778649524, 0.7644769381731749, 0.6501490521768574 ], "p_value": 0.0081, "mde": 0.08777660086106652, "mde_rel_pct": 21087.532639296336, "verdict": "idea worse (significant)", "system_worked": false }, "idea_sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.8622342199087143 }, { "cfg": { "lr": 0.003 }, "mean": 0.6572596728801727 }, { "cfg": { "lr": 0.01 }, "mean": 0.6313471160829067 } ], "mechanism_signature": { "prediction": "trained fold transition remains approximately reversible away from singularity", "predicted_reconstruction_error": "near numerical precision (ideal fixed fold)", "observed_reconstruction_error": 5.6109079310529e-07, "observed_state_scale": 1.3907469511032104, "observed_reverse_forward_norm_ratio": 1.000000074505806, "confirmed": true }, "custom_track": null }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the built-in dynamics task was tested. The custom reversible backward pass and constant-memory training were not implemented; the benchmark used ordinary autograd. Learned schedule selection, sequential MNIST, and long-horizon memory/speed measurements were not tested.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }