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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built and ran sensitivity-conditioned hidden-unit pruning on the structurally matched dynamics/rnn_small benchmark using the canonical training path, 8 paired seeds, and a parity-compliant three-value learning-rate sweep. The idea's best MSE was 0.00127219 versus the tuned magnitude baseline's 0.00041839 (paired delta +0.00085380, permutation p=0.0081), making the idea significantly worse. The trained-model information-to-ablation correlation averaged below the confirmation threshold, so the proposed mechanism was not quantitatively confirmed at neural-network scale.", "metrics": { "baseline": "dynamics/rnn_small, magnitude pruning, best lr=0.006, 32/64 hidden units retained, full 8-seed mean MSE=0.00041839 (exact value recorded in bench_report.json)", "idea": "dynamics/rnn_small, sensitivity information plus leave-one-group residual pruning, best lr=0.006, 32/64 hidden units retained, full 8-seed mean MSE=0.0012721884; paired delta=+0.0008537985, p=0.0081, 0/8 wins", "mechanism_signature": { "mean_importance_ablation_correlation": 0.4416, "confirmed": false, "interpretation": "sensitivity information was only moderately correlated with observed hidden-unit ablation impact and did not support the claimed selection benefit" } }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "epochs": 15, "keep": 32, "method": "magnitude" }, "sweep": [ { "cfg": { "lr": 0.0015, "epochs": 15, "keep": 32, "method": "magnitude" }, "mean": 0.0010415702417958528 }, { "cfg": { "lr": 0.003, "epochs": 15, "keep": 32, "method": "magnitude" }, "mean": 0.0007543887477368116 }, { "cfg": { "lr": 0.006, "epochs": 15, "keep": 32, "method": "magnitude" }, "mean": 0.0004434 } ], "full_mean": 0.00041839 }, "idea": { "best_cfg": { "lr": 0.006, "epochs": 15, "keep": 32, "method": "sensitivity" }, "sweep": [ { "cfg": { "lr": 0.0015, "epochs": 15, "keep": 32, "method": "sensitivity" }, "mean": 0.0051941196143161505 }, { "cfg": { "lr": 0.003, "epochs": 15, "keep": 32, "method": "sensitivity" }, "mean": 0.0029561036499217153 }, { "cfg": { "lr": 0.006, "epochs": 15, "keep": 32, "method": "sensitivity" }, "mean": 0.0012721883977064863 } ], "full_mean": 0.0012721883977064863 }, "comparison": { "delta_mean": 0.0008537985086150002, "idea_wins": 0, "n_pairs": 8, "p_value": 0.0081, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "confirmed": false, "per_seed_importance_ablation_correlations": [ 0.4777683942, 0.4219010441, 0.4756484583, 0.5436690158, 0.1848164402, 0.5280788239, 0.5366510031, 0.3844902532 ] } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_sensitivity_pruning.py", "files": [ "bench_sensitivity_pruning.py", "bench_report.json" ], "limitations": "Only the built-in dynamics track, one 50% retention level, 15 training epochs, and GRU head/hidden-unit groups were tested; 25% and 10% retention, iterative multi-round pruning, Fisher condition numbers, solver function evaluations, and continuous neural-ODE integration were not tested. The benchmark's rnn_small is a recurrent pendulum predictor rather than a continuous-time NODE.", "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }