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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a custom attributed relational-graph classification track and matched GraphNet systems; the only intervention was detached Sinkhorn-based lifted node alignment during training. The idea achieved mean test error 0.009375 versus baseline 0.010156, but the paired permutation p-value was 1.0 and the harness verdict was "no significant win". The trained-model mechanism signature was confirmed for reduced permutation prediction variance, yet this did not translate into a statistically significant task-metric improvement.", "metrics": { "baseline": "mean test error=0.01015625; per-seed=[0.0125,0.00625,0,0.01875,0.00625,0.01875,0,0.01875]; best lr=0.01; permutation variance=4.22e-17", "idea": "mean test error=0.00937500; per-seed=[0.0125,0.00625,0,0.0125,0.00625,0.01875,0,0.01875]; best lr=0.01; permutation variance=1.26e-17; Sinkhorn=35 iterations; epsilon=0.1median relational row cost", "paired_delta": "-0.00078125 (idea lower/better)", "permutation_p_value": 1.0, "idea_wins": 1, "n_pairs": 8 }, "bench_report": { "bench_version": 1, "track": "relational_graph_classification", "model": "graphnet", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.003 }, "mean": 0.012500000302679837 }, { "cfg": { "lr": 0.01 }, "mean": 0.009375000256113708 }, { "cfg": { "lr": 0.03 }, "mean": 0.009375000256113708 } ], "full": { "mean": 0.010156250325962901, "std": 0.007614683392424964, "per_seed": [ 0.012500000186264515, 0.0062500000931322575, 0.0, 0.01875000074505806, 0.0062500000931322575, 0.01875000074505806, 0.0, 0.01875000074505806 ], "n": 8, "signature_variance": 4.222490175236095e-17 } }, "idea": { "per_seed": [ 0.012500000186264515, 0.0062500000931322575, 0.0, 0.012500000186264515, 0.0062500000931322575, 0.01875000074505806, 0.0, 0.01875000074505806 ], "mean": 0.009375000256113708, "signature_variance": 1.260543858543509e-17, "lr": 0.01 }, "comparison": { "delta_mean": -0.0007812500698491931, "idea_wins": 1, "n_pairs": 8, "per_seed_diffs": [ 0.0, 0.0, 0.0, -0.006250000558793545, 0.0, 0.0, 0.0, 0.0 ], "p_value": 1.0, "mde": 0.0018476564151933418, "mde_rel_pct": 18.19230873494808, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": "alignment should reduce permutation prediction variance", "observed_baseline_variance": 4.222490175236095e-17, "observed_idea_variance": 1.260543858543509e-17, "confirmed": true, "sinkhorn_iterations": 35, "alignment_epsilon": "0.1median relational row cost" }, "idea_sweep": [ { "lr": 0.003, "mean": 0.011718750349245965 }, { "lr": 0.01, "mean": 0.009375000256113708 }, { "lr": 0.03, "mean": 0.009375000256113708 } ], "custom_track": { "name": "relational_graph_classification", "file": "graph_track.py", "domain": "graph-nn" } }, "how_to_run": "cd /home/maxwelhelp/all/math2nn/experiments/exp2140_submetry-lifted-relational-alignment && /home/maxwelhelp/main/bin/python3 bench_run.py", "files": [ "graph_track.py", "bench_run.py", "bench_report.json", "run_output.txt" ], "limitations": "The custom benchmark is small (400 training and 160 test graphs, 8 nodes each) and uses a simple GraphNet rather than MUTAG/ZINC or a deeper GNN. The relational alignment compares dense relational rows and hidden node states, not a full edge-attribute prediction head; differentiating through Sinkhorn, annealing, weighted nodes, FLOP profiling, and larger graph sizes were not tested.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }