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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a custom geometric graph regression track and matched residual message-passing systems, using d^2 edge weights for the idea versus unweighted edges for the baseline. Across 8 paired seeds, the idea reduced MSE from 0.0848644 to 0.0712046 with delta -0.0136598, but the permutation p-value was 0.0868, so it was not a significant win. The trained-model mechanism signature was not confirmed: weighted propagation had lower observed gradient influence and lower predicted-observed correlation.", "metrics": { "baseline": "Best lr=0.003, alpha=0.5; full 8-seed MSE 0.0848644 ± 0.0295812.", "idea": "Best lr=0.001, alpha=0.5; full 8-seed MSE 0.0712046 ± 0.0237890; 6/8 paired wins; delta=-0.0136598; permutation p=0.0868.", "mechanism_signature": "Baseline observed gradient influence L2=0.267737, predicted-observed correlation=0.878935; idea observed gradient influence L2=0.244880, correlation=0.384099; confirmed=false." }, "bench_report": { "bench_version": 1, "track": "geometric_graph_diffusion", "model": "GeometricMessageNet", "metric_direction": "lower is better", "n_seeds": 8, "comparison": { "delta_mean": -0.013659819029271603, "idea_wins": 6, "n_pairs": 8, "p_value": 0.0868, "verdict": "no significant win", "system_worked": false }, "baseline": { "best_cfg": { "lr": 0.003, "alpha": 0.5 }, "full_mean": 0.08486439986154437, "full_std": 0.029581161321461158 }, "idea": { "best_cfg": { "lr": 0.001, "alpha": 0.5 }, "full_mean": 0.07120458083227277, "full_std": 0.023789005397093055 }, "custom_track": { "name": "geometric_graph_diffusion", "file": "graph_track.py", "domain": "graph-neural-networks" }, "protocol_notes": { "paired_seeds": [ 0, 1, 2, 3, 4, 5, 6, 7 ], "baseline_and_idea_same_architecture": true, "only_intervention": "edge weight d^2 versus unit edge weight", "device": "cuda" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_graph.py", "files": [ "graph_track.py", "bench_graph.py", "artifacts/bench_report.json", "artifacts/bench_stdout.json" ], "limitations": "The custom task uses small fixed 24-node kNN geometric graphs rather than a real Delaunay or hyperbolic benchmark. Capacity regularization, shell-capacity estimation during training, curvature sweeps, and external citation or molecular graph datasets were not tested.", "system_verdict": "failed", "practical_verdict": "no_effect", "mechanism_ok": null, "system_judged": true }