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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a local biclique-free hierarchical-attention Transformer and evaluated it against the identical dense Transformer on the structurally matched sequence track. The idea reduced attention edges from 1024 to 94 (10.89x reduction) and maintained observed maximum shared keys of 1, but test MSE improved only from 0.48255 to 0.47450 (paired delta -0.00805) with permutation p=0.57185, so there was no significant neural-training win.", "metrics": { "baseline": "8-seed dense transformer: mean test MSE 0.4825485, std 0.0335456; best lr=0.001, epochs=10; 1024 attention edges.", "idea": "8-seed biclique-free hierarchical mask: mean test MSE 0.4744987, std 0.0526747; best lr=0.001, epochs=10; 94 active edges, 10.8936x fewer than dense; paired delta -0.0080498, p=0.57185." }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py", "files": [ "bench_experiment.py", "bench_report.json" ], "limitations": "The benchmark used the built-in 400-training/200-test sequence subset and 10 epochs, rather than long-context language modeling. The mask was fixed from token positions rather than learned from representations, and the reported edge-scaling exponent was based on the fixed 32-token construction rather than a multi-length trained-model sweep. Wall-clock sparse-kernel speed and KV-cache behavior were not measured.", "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": 10 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 10 }, "mean": 0.482548501342535 }, { "cfg": { "lr": 0.003, "epochs": 10 }, "mean": 0.6393650658428669 }, { "cfg": { "lr": 0.01, "epochs": 10 }, "mean": 1.4253769740462303 } ], "full": { "mean": 0.482548501342535, "std": 0.03354559876849172, "per_seed": [ 0.4842072129249573, 0.42341384291648865, 0.4566255807876587, 0.5167749524116516, 0.49487417936325073, 0.4824545681476593, 0.5386028289794922, 0.4634348452091217 ], "n": 8 } }, "idea": { "best_cfg": { "lr": 0.001, "epochs": 10 }, "sweep": [ { "cfg": { "lr": 0.001, "epochs": 10 }, "mean": 0.4744987264275551 }, { "cfg": { "lr": 0.003, "epochs": 10 }, "mean": 0.5909863151609898 }, { "cfg": { "lr": 0.01, "epochs": 10 }, "mean": 1.119819551706314 } ], "mean": 0.4744987264275551, "std": 0.052674658740946755, "per_seed": [ 0.49082696437835693, 0.3496098220348358, 0.49417421221733093, 0.4901917278766632, 0.519122302532196, 0.44667643308639526, 0.5286210179328918, 0.47676733136177063 ], "n": 8 }, "comparison": { "delta_mean": -0.008049774914979935, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ 0.006619751453399658, -0.07380402088165283, 0.03754863142967224, -0.026583224534988403, 0.024248123168945312, -0.03577813506126404, -0.009981811046600342, 0.013332486152648926 ], "p_value": 0.57185, "mde": 0.030369881877730317, "mde_rel_pct": 6.293643394028983, "verdict": "no significant win", "system_worked": false }, "track_match": "sequence has multi-token correlations and transformer attention; therefore it is structurally matched", "mechanism_signature": { "prediction": "hierarchical repaired attention has linear active-edge count and K2,2 maximum common-neighbor count <=1", "observed_edges": 94, "observed_dense_edges": 1024, "edge_reduction": 10.893617021276595, "observed_edge_scaling_exponent": 1.0, "observed_max_shared_keys": 1, "trained_model_test_mse_mean": 0.4744987264275551, "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }