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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 10, "verdict": "Built a local decision-aware training loop for the structurally matched actuated-pendulum dynamics track using the same rnn_small architecture as the canonical MSE baseline. Across 8 paired seeds, the baseline achieved mean test MSE 0.002106 while the best decision-aware configuration achieved 0.763324; the paired delta was +0.761218 with permutation p=0.0081, a significant loss. The trained-model decision mechanism signature was not quantitatively confirmed, and the idea is therefore a clear negative result on this benchmark.", "metrics": { "baseline": "dynamics/rnn_small, swept lr {0.001, 0.003, 0.006} at 12 epochs; best lr=0.006; 8-seed mean test MSE=0.0021058194, std=0.0003894366; per-seed=[0.0020043950, 0.0014378825, 0.0015748648, 0.0022957840, 0.0024020642, 0.0024898043, 0.0020851335, 0.0025566272]", "idea": "Decision-aware unrolled projected multistart loss, same rnn_small/lr/epochs grid; best lr=0.001; 8-seed mean test MSE=0.7633235902, std=0.0525785229; per-seed=[0.7467728257, 0.7365002632, 0.8016765714, 0.8869475126, 0.7358804345, 0.7473666072, 0.7420515418, 0.7093929648]", "paired_comparison": "delta_mean=+0.7612177707 (idea minus baseline; lower is better), idea_wins=0/8, permutation_p=0.0081, verdict='idea worse (significant)'", "idea_sweep": "lr {0.001, 0.003, 0.006}, all 12 epochs; means={0.001:0.7633235902, 0.003:0.7637511864, 0.006:0.7635712028}" }, "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": 12 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 12 }, "mean": 0.004298779065720737 }, { "cfg": { "lr": 0.003, "epochs": 12 }, "mean": 0.0034615940821822733 }, { "cfg": { "lr": 0.006, "epochs": 12 }, "mean": 0.0018282315868418664 } ], "full": { "mean": 0.002105819425196387, "std": 0.000389436566226016, "per_seed": [ 0.002004395006224513, 0.001437882543541491, 0.0015748648438602686, 0.002295783953741193, 0.0024020641576498747, 0.0024898042902350426, 0.0020851334556937218, 0.0025566271506249905 ], "n": 8 } }, "idea": { "mean": 0.7633235901594162, "std": 0.05257852286312429, "per_seed": [ 0.746772825717926, 0.7365002632141113, 0.8016765713691711, 0.886947512626648, 0.735880434513092, 0.7473666071891785, 0.7420515418052673, 0.7093929648399353 ], "n": 8 }, "comparison": { "delta_mean": 0.7612177707342198, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.7447684307117015, 0.7350623806705698, 0.8001017065253109, 0.8846517286729068, 0.7334783706705698, 0.7448768028989434, 0.7399664083495736, 0.7068363376893103 ], "p_value": 0.0081, "mde": 0.04703323792024009, "mde_rel_pct": 2233.4886532758533, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "prediction": "decision-aware training reduces surrogate optimum displacement relative to MSE-only while preserving trajectory fit", "measured_on_trained_models": true, "decision_probe": { "contexts": 32, "multistart": 5, "bounds": [ -1.5, 1.5 ] }, "confirmed": false, "note": "Standard task test MSE was used as the primary metric; the decision probe was diagnostic only." }, "idea_sweep": [ { "cfg": { "lr": 0.001, "epochs": 12 }, "result": { "mean": 0.7633235901594162, "std": 0.05257852286312429, "per_seed": [ 0.746772825717926, 0.7365002632141113, 0.8016765713691711, 0.886947512626648, 0.735880434513092, 0.7473666071891785, 0.7420515418052673, 0.7093929648399353 ], "n": 8 } }, { "cfg": { "lr": 0.003, "epochs": 12 }, "result": { "mean": 0.7637511864304543, "std": 0.05106457675677259, "per_seed": [ 0.748903214931488, 0.7398125529289246, 0.802194356918335, 0.8831914663314819, 0.7348660230636597, 0.7470683455467224, 0.7429152727127075, 0.7110582590103149 ], "n": 8 } }, { "cfg": { "lr": 0.006, "epochs": 12 }, "result": { "mean": 0.7635712027549744, "std": 0.050801253101102256, "per_seed": [ 0.7507007122039795, 0.7397510409355164, 0.8021869659423828, 0.8825896382331848, 0.7341564297676086, 0.7471616268157959, 0.7382707595825195, 0.7137524485588074 ], "n": 8 } } ], "selected_idea_cfg": { "lr": 0.001, "epochs": 12 } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the built-in dynamics track was tested; no custom track, longer training budget, alternative decision-loss weights, or more extensive decision-probe reporting was run. The benchmark's standard task MSE is the primary result, and the decision objective used a nominal one-step pendulum cost rather than a full multi-step economic rollout.", "system_verdict": "failed", "practical_verdict": "harms", "mechanism_ok": 0, "system_judged": true }