# Стенд-проверка (stage-2) · промт оператора: (универсальный) Ответ агента: { "worked": false, "confidence": 9, "verdict": "Implemented a matched dynamics benchmark using the standard rnn_small architecture and canonical train_model path, with a Farkas-certified reduction from 64 redundant interval halfspaces to 2 retained constraints. The mechanism signature confirmed zero full/reduced output disagreement and exact certificates, but the shielded system had substantially worse test MSE (0.0523780 vs 0.0001147401), with paired delta +0.0522633 and permutation p=0.0081. The geometric mechanism transferred, but it did not improve the independent neural task metric.", "metrics": { "baseline": "dynamics/rnn_small; 8-seed test MSE mean 0.0001147401, std 0.0000402690; baseline sweep selected lr=0.00665.", "idea": "dynamics/rnn_small with certified interval shield; 8-seed best test MSE mean 0.0523780, std 0.0088756; paired delta +0.0522633, 0/8 wins, permutation p=0.0081; 64 constraints reduced to 2 (3.125%)." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_farkas.py", "files": [ "bench_farkas.py", "bench_report.json", "bench_stdout.txt" ], "limitations": "The built-in dynamics track predicts a future pendulum angle rather than directly training a closed-loop policy, so this tests transfer of the safety projection rather than full multi-agent RL. Constraints were redundant scalar interval halfspaces, not heterogeneous obstacle geometry. End-to-end constraint-construction and solver timing were not benchmarked. The hard output clamp changes the supervised target range and caused the observed task-metric degradation.", "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.00665 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 0.0009722943 }, { "cfg": { "lr": 0.002 }, "mean": 0.0006509074 }, { "cfg": { "lr": 0.003 }, "mean": 0.0004595853 }, { "cfg": { "lr": 0.004 }, "mean": 0.0003482927 }, { "cfg": { "lr": 0.005 }, "mean": 0.0002485937 }, { "cfg": { "lr": 0.00067 }, "mean": 0.0011799606 }, { "cfg": { "lr": 0.00134 }, "mean": 0.0008329209 }, { "cfg": { "lr": 0.00268 }, "mean": 0.0005083114 }, { "cfg": { "lr": 0.00402 }, "mean": 0.0003466854 }, { "cfg": { "lr": 0.00536 }, "mean": 0.0002244893 }, { "cfg": { "lr": 0.00665 }, "mean": 0.0001395188 } ], "full": { "mean": 0.0001147401, "std": 4.0269e-05, "per_seed": [ 0.0001932993, 0.000143844, 8.01379e-05, 0.0001407938, 0.0001187587, 8.25471e-05, 6.3313e-05, 9.52268e-05 ], "n": 8 } }, "idea": { "mean": 0.0523780384, "std": 0.008875649, "per_seed": [ 0.041060634, 0.058054667, 0.0539846234, 0.0682321563, 0.0553050973, 0.0380525403, 0.0531917065, 0.0511428826 ], "n": 8 }, "comparison": { "delta_mean": 0.0522632983, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.0408673346, 0.0579108229, 0.0539044855, 0.0680913626, 0.0551863386, 0.0379699931, 0.0531283936, 0.0510476558 ], "p_value": 0.0081, "mde": 0.007932782, "mde_rel_pct": 6913.6975, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "constraint_count_full": 64, "constraint_count_retained": 2, "retained_fraction": 0.03125, "certificate_max_residual": 0.0, "certificate_max_offset_violation": 0.0, "trained_test_samples": 400, "full_reduced_max_output_difference": 0.0, "full_reduced_decision_disagreement": 0.0, "predicted_zero_disagreement_observed": true, "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }