Projected Absolute-Residual Compensation for Neural State-Space Models / report_bench_2026-08-31T192705.md
Beats tuned baseline
Стенд-проверка (stage-2) · промт оператора:
(универсальный)
Ответ агента:
{ "worked": true, "confidence": 7, "verdict": "Implemented projected absolute-residual compensation on the structurally matched actuated-pendulum dynamics track using paired eight-seed training and the benchmark sweep/report protocol. The idea achieved lower test MSE on all 8 seeds: 4.956e-05 versus 1.530e-03 for the baseline, with paired delta -1.481e-03 and permutation p=0.0081. The incremental residual was substantially smaller than the absolute residual in trained-model behavior, although the built-in track does not explicitly inject persistent actuator bias.", "metrics": { "baseline": "mean test MSE 0.0015303929612855427; std 0.0006243748661918005; best lr 0.01; 8 paired seeds", "idea": "mean test MSE 0.00004955958911523339; std 0.000022175200246237042; best lr 0.01, gamma 0.8; 8 paired seeds", "paired_delta": -0.0014808333721703093, "permutation_p_value": 0.0081, "idea_wins": "8/8", "mechanism_signature": "absolute residual norm 0.7707589045166969; incremental residual norm 0.0819682078436017; disagreement norm 0.8208163678646088; confirmed=true" }, "bench_report": { "bench_version": 1, "track": "dynamics", "model": "rnn_small", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "gamma": 0.2 }, "sweep_means": [ { "lr": 0.001, "gamma": 0.2, "mean": 0.004167803970631212 }, { "lr": 0.001, "gamma": 0.5, "mean": 0.004167803970631212 }, { "lr": 0.001, "gamma": 0.8, "mean": 0.004167803970631212 }, { "lr": 0.003, "gamma": 0.2, "mean": 0.0035580022376962006 }, { "lr": 0.003, "gamma": 0.5, "mean": 0.0035580022376962006 }, { "lr": 0.003, "gamma": 0.8, "mean": 0.0035580022376962006 }, { "lr": 0.01, "gamma": 0.2, "mean": 0.0017142038705060259 }, { "lr": 0.01, "gamma": 0.5, "mean": 0.0017142038705060259 }, { "lr": 0.01, "gamma": 0.8, "mean": 0.0017142038705060259 } ], "full_mean": 0.0015303929612855427, "full_std": 0.0006243748661918005 }, "idea": { "best_cfg": { "lr": 0.01, "gamma": 0.8 }, "sweep_best_mean": 4.955958911523339e-05, "full_mean": 4.955958911523339e-05, "full_std": 2.2175200246237042e-05 }, "comparison": { "delta_mean": -0.0014808333721703093, "idea_wins": 8, "n_pairs": 8, "p_value": 0.0081, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "n_models": 8, "mean_abs_residual_norm": 0.7707589045166969, "mean_incremental_residual_norm": 0.0819682078436017, "mean_abs_inc_disagreement": 0.8208163678646088, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_stage2.py", "files": [ "bench_stage2.py", "bench_report.json" ], "limitations": "Only the built-in actuated-pendulum dynamics track was tested. It does not explicitly inject persistent actuator bias, dead-zone, quantization, measurement noise, or test the stability boundary under controlled mismatch sweeps.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 1, "system_judged": true }