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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Implemented the positive-measure span regularizer as an auxiliary loss on the shared mlp_tiny encoder and ran the full 8-seed tabular benchmark with a matched three-point learning-rate sweep. The idea produced a negligible MSE improvement (delta=-0.001512, permutation p=0.69225) and only a tiny effective-rank increase (1.014278 to 1.014433), so there is no significant task-metric win; the mechanism direction was observed but not substantively strong.", "metrics": { "baseline": "best lr=0.01; mean test MSE=8.0089843", "idea": "best shared lr=0.01; mean test MSE=8.0074723; paired delta=-0.0015120; permutation p=0.69225; mean held-out effective rank 1.014278 -> 1.014433" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_bench.py", "files": [ "run_bench.py", "bench_report.json" ], "limitations": "Only the structurally matched built-in tabular track was tested; vision, sequence, and dynamics tracks were not applicable to this encoder regularizer. Local neighborhoods were approximated by shuffled minibatches rather than true k-nearest-neighbor regions, and the experiment used a compact 400/200 tabular split and 20 epochs. Representation diagnostics were stored separately from task metrics.", "bench_report": { "bench_version": 1, "track": "tabular", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.01 }, "sweep": [ { "cfg": { "lr": 0.001 }, "mean": 18.315088629722595 }, { "cfg": { "lr": 0.003 }, "mean": 13.52312970161438 }, { "cfg": { "lr": 0.01 }, "mean": 8.00898426771164 } ], "full": { "per_seed": [ 7.518021106719971, 7.098003387451172, 7.579931259155273, 8.855631828308105, 7.726285457611084, 7.675883769989014, 9.252959251403809, 8.365158081054688 ], "mean": 8.00898426771164 } }, "idea": { "per_seed": [ 7.499464988708496, 7.094777584075928, 7.588492393493652, 8.855938911437988, 7.727814674377441, 7.682166576385498, 9.250493049621582, 8.36063003540039 ], "mean": 8.007472276687622 }, "comparison": { "delta_mean": -0.001511991024017334, "idea_wins": 4, "n_pairs": 8, "per_seed_diffs": [ -0.01855611801147461, -0.0032258033752441406, 0.008561134338378906, 0.0003070831298828125, 0.0015292167663574219, 0.006282806396484375, -0.0024662017822265625, -0.004528045654296875 ], "p_value": 0.69225, "verdict": "no measurable effect", "system_worked": false }, "idea_grid": [ { "lr": 0.001, "mean": 18.320167779922485 }, { "lr": 0.003, "mean": 13.5223867893219 }, { "lr": 0.01, "mean": 8.007472276687622 } ], "mechanism_signature": { "prediction": "span regularization increases held-out effective rank", "predicted_direction": "idea rank > baseline rank", "baseline_mean_rank": 1.0142778903245926, "idea_mean_rank": 1.0144332945346832, "baseline_mean_sv_ratio": 0.0, "idea_mean_sv_ratio": 0.0, "confirmed": true } }, "system_verdict": "partial", "practical_verdict": "no_effect", "mechanism_ok": 1, "system_judged": true }