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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 8, "verdict": "Built the Critical-Tail Multiscale Mixer as a replacement for self-attention in transformer_tiny and evaluated it on the mandated sequence forecast track. Across 8 paired seeds, test MSE improved from 0.391339 for the tuned standard Transformer baseline to 0.229231 for the mixer; the paired permutation test gave delta_mean=-0.162108 and p=0.0081, so the effect is significant. A trained-model finite-perturbation signature also confirmed stronger distant influence for the mixer.", "metrics": { "baseline": "Standard transformer_tiny self-attention; tuned lr=0.001, epochs=18; mean test MSE 0.391339, std 0.034275.", "idea": "Critical-tail dyadic mixer; tuned lr=0.003, epochs=18; mean test MSE 0.229231, std 0.030839; paired delta=-0.162108; 8/8 seed wins; permutation p=0.0081.", "mechanism_signature": "Baseline trained-model far/near influence ratio=4.4649; idea=12.6454; confirmed=true.", "math_sanity": "Tail ratio to 1/log(R+2): R=8 0.97896, R=32 0.99586, R=128 0.99921, R=512 0.99984." }, "bench_report": { "bench_version": 1, "track": "sequence", "model": "transformer_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.001, "epochs": 18 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 18 }, "mean": 0.379792 }, { "cfg": { "lr": 0.003, "epochs": 18 }, "mean": 0.435053 }, { "cfg": { "lr": 0.006, "epochs": 18 }, "mean": 0.529817 } ], "full": { "mean": 0.391339, "std": 0.034275, "per_seed": [ 0.392898, 0.318179, 0.389735, 0.418355, 0.385423, 0.412657, 0.440995, 0.372472 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.003, "epochs": 18 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 18 }, "mean": 0.261787 }, { "cfg": { "lr": 0.003, "epochs": 18 }, "mean": 0.232135 }, { "cfg": { "lr": 0.006, "epochs": 18 }, "mean": 0.237128 } ], "full": { "mean": 0.229231, "std": 0.030839, "per_seed": [ 0.254002, 0.194349, 0.226438, 0.253751, 0.20074, 0.204638, 0.287997, 0.211936 ], "n": 8 } }, "comparison": { "delta_mean": -0.162108, "idea_wins": 8, "n_pairs": 8, "p_value": 0.0081, "mde": 0.0217, "mde_rel_pct": 5.545, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "prediction": "The dyadic critical-tail mixer should preserve stronger distant-input influence than the standard Transformer system.", "baseline_observed": { "near_influence": 0.013202, "far_influence": 0.058944, "far_near_ratio": 4.4649 }, "idea_observed": { "near_influence": 0.006817, "far_influence": 0.086205, "far_near_ratio": 12.6454 }, "predicted_far_near_ratio_idea_gt_baseline": true, "confirmed": true } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py", "files": [ "stage2_bench.py", "bench_report.json" ], "limitations": "Only the built-in sequence forecast track was tested; CIFAR-10, tabular, and dynamics tracks were not tested. The comparison used the fixed tiny 32-token benchmark and 18 training epochs, not long-context extrapolation, peak-memory scaling, wall-clock scaling, copy/retrieval tasks, Long Range Arena, or a larger Transformer.", "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 1, "system_judged": true }