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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented a structurally matched custom graph-regression track and trained baseline and influence-adaptive message-passing systems end-to-end with the bench protocol. The trained-model mechanism signature was confirmed: observed unsaturated gain 6.413 versus predicted mean xi 6.638 (3.4% relative error), messages stayed bounded at 1.0, and influence-to-xi correlation was strongly negative. However, the idea significantly worsened test MSE, 0.006315 versus 0.002778, with paired delta +0.003537 and permutation p=0.0081; therefore it is not a win.", "metrics": { "baseline": "Test MSE 0.0027779446 mean over 8 seeds; best baseline sweep lr=0.01.", "idea": "Test MSE 0.0063154194 mean over 8 seeds; best tested configuration kappa=1.0, lr=0.01.", "paired_delta": "idea - baseline = +0.0035374748; idea wins 0/8; permutation p=0.0081.", "mechanism_signature": "confirmed=true; predicted mean xi=6.6376091; observed trained-model unsaturated gain=6.4134456; relative gain error=0.0337717; maximum absolute message=1.0; mean saturation fraction=0.8371; influence-xi correlations approximately -0.98." }, "bench_report": { "bench_version": 1, "track": "influence_graph_regression", "model": "custom_graph_message_net", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01, "kappa": 2.0 }, "sweep": [ { "cfg": { "lr": 0.001, "kappa": 2.0 }, "mean": 0.00361063884338364 }, { "cfg": { "lr": 0.003, "kappa": 2.0 }, "mean": 0.0033815143979154527 }, { "cfg": { "lr": 0.01, "kappa": 2.0 }, "mean": 0.0028661651886068285 } ], "full": { "mean": 0.00277794455178082, "std": 0.0006137955939218627, "per_seed": [ 0.002771663246676326, 0.001872004708275199, 0.0030535804107785225, 0.0037674123886972666, 0.0020138039253652096, 0.0023955816868692636, 0.003390558995306492, 0.0029589510522782803 ], "n": 8 } }, "idea": { "cfg": { "lr": 0.01, "kappa": 1.0 }, "mean": 0.0063154193630907685, "std": 0.0032907033594589473, "per_seed": [ 0.005231841467320919, 0.00240378943271935, 0.007344594690948725, 0.011935936287045479, 0.003391814883798361, 0.0029857298359274864, 0.010524877347052097, 0.006704770959913731 ], "n": 8 }, "comparison": { "delta_mean": 0.0035374748113099486, "idea_wins": 0, "n_pairs": 8, "per_seed_diffs": [ 0.0024601782206445932, 0.0005317847244441509, 0.004291014280170202, 0.008168523898348212, 0.0013780109584331512, 0.0005901481490582228, 0.0071343183517456055, 0.0037458199076354504 ], "p_value": 0.0081, "mde": 0.0024180377401689383, "mde_rel_pct": 87.04413263464309, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "confirmed": true, "predicted_mean_xi": 6.637609124183655, "observed_mean_gain": 6.413445591926575, "relative_gain_error": 0.03377172830238511, "mean_saturation_fraction": 0.8370833333333333, "max_bounded_message": 1.0 }, "custom_track": { "name": "influence_graph_regression", "file": "graph_strategic_bench.py", "domain": "graph-nn" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 graph_strategic_bench.py", "files": [ "graph_strategic_bench.py", "bench_report.json" ], "limitations": "The custom benchmark is a small synthetic fixed-topology graph regression task rather than a real-world graph dataset. The tested intervention uses the differentiable clipped decoder, not the hard or relaxed adaptive interval quantizer, and communication throughput or FLOP savings were not measured.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }