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

(универсальный)

Ответ агента:

{ "worked": true, "confidence": 7, "verdict": "Implemented partial Gromov-Wasserstein cross-attention as a trainable replacement inside transformer_tiny on the structurally matched sequence forecasting track. Across 8 paired seeds, the idea obtained lower test MSE than the swept baseline (0.42486 vs 0.62292; delta -0.19806) with permutation p=0.0081, satisfying the bench criterion for a significant win. The mechanism signature was not confirmed quantitatively, so the result is task-level evidence rather than proof of the proposed attention behavior.", "metrics": { "baseline": "transformer_tiny baseline, best lr=0.0015: mean test MSE 0.6229188442, std 0.0693105116", "idea": "partial GW attention, best lr=0.0015 beta=0.2 eps=0.15: mean test MSE 0.4248598143", "comparison": "delta_mean=-0.1980590299; 8/8 paired wins; permutation p=0.0081; relative improvement=31.8%" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_pgw.py", "files": [ "bench_pgw.py", "bench_report.json" ], "limitations": "The full default-sized protocol did not fit within the runtime budget, so this run used 600 training samples, 300 test samples, and 5 epochs; eight paired seeds were retained. Only the sequence track was tested. The implementation uses detached structural costs and a reduced number of projection/GW iterations. The saved mechanism signature has confirmed=false because it lacks a separately measured trained-model correlation between structural compatibility and attention mass.", "bench_report": { "bench_version": 1, "track": "sequence", "model": "transformer_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.0015 }, "sweep": [ { "cfg": { "lr": 0.0015 }, "mean": 0.6402924656867981 }, { "cfg": { "lr": 0.003 }, "mean": 0.7056357711553574 }, { "cfg": { "lr": 0.006 }, "mean": 0.9283334761857986 } ], "full": { "mean": 0.6229188442230225, "std": 0.06931051155059516, "per_seed": [ 0.7044264674, 0.5437753201, 0.5856346488, 0.7273334265, 0.5151234269, 0.6567925215, 0.610052824, 0.6402121186 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.0015, "beta": 0.2, "eps": 0.15 }, "sweep": [ { "cfg": { "lr": 0.0015, "beta": 0.2, "eps": 0.15 }, "mean": 0.4291221723 }, { "cfg": { "lr": 0.003, "beta": 0.3, "eps": 0.15 }, "mean": 0.4798029736 }, { "cfg": { "lr": 0.006, "beta": 0.4, "eps": 0.2 }, "mean": 0.6943782419 } ], "per_seed": [ 0.4900712073, 0.3184103072, 0.4649355114, 0.4430716634, 0.3909056485, 0.4021309018, 0.4942366779, 0.3951165974 ] }, "comparison": { "delta_mean": -0.1980590299, "idea_wins": 8, "n_pairs": 8, "per_seed_diffs": [ -0.2143552601, -0.2253650129, -0.1206991374, -0.2842617631, -0.1242177784, -0.2546616197, -0.1158161461, -0.2450955212 ], "p_value": 0.0081, "verdict": "idea better (significant)", "system_worked": true }, "mechanism_signature": { "prediction": "relational compatibility reduces incompatible-token attention", "predicted": { "partial_row_upper_bound": 0.03125, "beta": 0.2 }, "observed": { "baseline_test_mse_mean": 0.6229188442, "idea_test_mse_mean": 0.4248598143 }, "confirmed": false }, "protocol_notes": { "track_reason": "The sequence track contains multi-token temporal correlations and transformer attention, directly matching the proposed mechanism.", "reduced_budget": "600 train/300 test and 5 epochs due to partial GW attention cost; paired seeds remain 8 and baseline/idea learning-rate search spaces share the same union.", "elapsed_sec": 37.5958292484 } }, "system_verdict": "worked", "practical_verdict": "helps", "mechanism_ok": 0, "system_judged": true }