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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built conditional OT barycenter feature augmentation with gated source masses, Sinkhorn transport, detached latent barycenters, and transported soft labels. On the structurally matched custom three-source domain-generalization track, the best OT system had lower test error than the tuned ERM baseline, but the paired result was not significant (p=0.39075), so this is not a demonstrated win. The mechanism signature was confirmed at neural-network scale: observed consensus variance was approximately 0.30 of pooled source feature variance, close to the predicted one-third.", "metrics": { "baseline": "ERM shared_mlp_tiny; best sweep cfg lr=0.006, baseline full mean error=0.0775000, std=0.0108253, per-seed=[0.0775,0.0725,0.0750,0.0650,0.0825,0.0625,0.0875,0.0975]", "idea": "Conditional OT; best cfg lr=0.012, eta=0.15, mean error=0.0725000, std=0.0124373, per-seed=[0.0850,0.0675,0.0825,0.0525,0.0550,0.0775,0.0725,0.0875]", "paired_delta": "-0.0050000 error, 5/8 paired wins, permutation p=0.39075, verdict=no significant win", "mechanism_signature": "predicted consensus variance=0.2850805; observed trained-model proxy=0.2627102; observed reduction ratio=0.3000; confirmed=true" }, "bench_report": { "bench_version": 1, "track": "conditional_ot_domains", "model": "shared_mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.006, "eta": 0.15 }, "full": { "mean": 0.07749999780207872, "std": 0.010825316902066397, "per_seed": [ 0.07750000059604645, 0.07249999791383743, 0.07499999552965164, 0.06499999761581421, 0.08249999582767487, 0.0625, 0.08749999850988388, 0.09749999642372131 ], "n": 8 }, "sweep": "9 configurations: lr in [0.003,0.006,0.012] x eta in [0.15,0.35,0.6]; baseline evaluated on sweep seeds [0,1,2,3] and best configuration reevaluated on all 8 seeds" }, "idea": { "best_cfg": { "lr": 0.012, "eta": 0.15 }, "mean": 0.07249999837949872, "std": 0.012437343083642742, "per_seed": [ 0.08500000089406967, 0.06749999523162842, 0.08249999582767487, 0.05249999836087227, 0.054999999701976776, 0.07750000059604645, 0.07249999791383743, 0.08749999850988388 ], "n": 8 }, "comparison": { "delta_mean": -0.004999999422580004, "idea_wins": 5, "n_pairs": 8, "per_seed_diffs": [ 0.007500000298023224, -0.005000002682209015, 0.007500000298023224, -0.01249999925494194, -0.02749999612569809, 0.015000000596046448, -0.015000000596046448, -0.009999997913837433 ], "p_value": 0.39075, "mde": 0.011824999342373263, "mde_rel_pct": 15.258064100301292, "verdict": "no significant win", "system_worked": false }, "mechanism_signature": { "prediction": "three-source latent consensus has approximately one-third of pooled feature variance", "predicted": 0.2850805396835009, "observed": 0.26271023021803963, "observed_reduction_ratio": 0.3, "confirmed": true, "n_behavior_models": 8 }, "custom_track": { "name": "conditional_ot_domains", "file": "conditional_ot_track.py", "domain": "domain_generalization" }, "protocol": { "paired_seeds": [ 0, 1, 2, 3, 4, 5, 6, 7 ], "sweep_seeds": [ 0, 1, 2, 3 ], "grid_union": "lr=[0.003,0.006,0.012], eta=[0.15,0.35,0.6]", "baseline_tuned_and_reevaluated": true, "task_match": "multi-source domain generalization with latent feature consensus" } }, "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_ot_bench.py", "files": [ "conditional_ot_track.py", "stage2_ot_bench.py", "bench_report.json", "stage2_run_corrected.log" ], "limitations": "The custom track is a small synthetic domain-generalization benchmark, not PACS or Office-Home. Compute overhead and domain-classifier accuracy were not measured separately. The mechanism signature uses a variance-reduction proxy from trained latent features rather than directly estimating a full barycenter covariance; no end-to-end-versus-detached ablation was run.", "system_verdict": "partial", "practical_verdict": "inconclusive", "mechanism_ok": 1, "system_judged": true }