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

(универсальный)

Ответ агента:

{ "worked": false, "confidence": 9, "verdict": "Built a local 2D anisotropic oblique-boundary BVP track and an end-to-end boundary-penalty MLP intervention using the same architecture and Adam budget as the baseline. The trained-model mechanism signature was confirmed: mean oblique residual decreased from 0.02720 to 0.01003 and mean normal residual from 0.04277 to 0.00812. However, test MSE was significantly worse for the idea, so it does not win the benchmark metric.", "metrics": { "baseline": "Best lr=0.003, weight_decay=0.0; full 8-seed test MSE mean=9.435732454221579e-05, std=2.4013090964758765e-05.", "idea": "Best lr=0.003, weight_decay=0.0, bc_weight=0.1; full 8-seed test MSE mean=1.1699842414609309e-04, std=3.095430559427717e-05.", "paired_comparison": "delta_mean=2.2641099803877296e-05, idea_wins=1/8, permutation p=0.0152, verdict='idea worse (significant)'", "mechanism_signature": "Baseline oblique residual=0.02720322646200657, idea oblique residual=0.01003198220860213; baseline normal residual=0.04276570677757263, idea normal residual=0.008119827311020344; confirmed=true" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 run_bench.py", "files": [ "pde_boundary_track.py", "run_bench.py", "bench_report.json" ], "bench_report": { "bench_version": 1, "track": "anisotropic_oblique_bvp", "model": "mlp_tiny", "metric_direction": "lower is better", "n_seeds": 8, "baseline": { "best_cfg": { "lr": 0.003, "weight_decay": 0.0 }, "full_mean": 9.435732454221579e-05, "full_std": 2.4013090964758765e-05 }, "idea": { "best_cfg": { "lr": 0.003, "weight_decay": 0.0, "bc_weight": 0.1 }, "full_mean": 0.00011699842414609309, "full_std": 3.095430559427717e-05 }, "comparison": { "delta_mean": 2.2641099803877296e-05, "idea_wins": 1, "n_pairs": 8, "p_value": 0.0152, "verdict": "idea worse (significant)", "system_worked": false }, "mechanism_signature": { "mean_baseline_oblique_residual": 0.02720322646200657, "mean_idea_oblique_residual": 0.01003198220860213, "mean_baseline_normal_residual": 0.04276570677757263, "mean_idea_normal_residual": 0.008119827311020344, "confirmed": true }, "custom_track": { "name": "anisotropic_oblique_bvp", "file": "pde_boundary_track.py", "domain": "pde" } }, "limitations": "The custom task is a small manufactured 2D BVP rather than a full Schrödinger-bridge/Sinkhorn PDE solve; the intervention uses boundary derivative penalties rather than explicit forward/backward factor propagation, diffusion discretization, or endpoint rescaling. No annulus geometry, rank-deficient multidimensional diffusion, particle-rejection baseline, bridge action, or large-scale neural architecture was tested.", "system_verdict": "partial", "practical_verdict": "harms", "mechanism_ok": 1, "system_judged": true }