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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 8,
  "verdict": "Implemented an end-to-end Minkowski-additive convex latent network and a deterministic custom regression track matching the idea's structure. Across 8 paired seeds, the idea achieved mean test MSE 0.001009 versus 0.020459 for the baseline, winning all 8 pairs with permutation p=0.0081; therefore the benchmark verdict is idea better (significant).",
  "metrics": {
    "baseline": "mean test MSE 0.0204591077, std 0.0058978452, best lr=0.01",
    "idea": "mean test MSE 0.0010090163, std 0.0002896170, paired delta=-0.0194500914, 8/8 wins, p=0.0081; mechanism observed mean additivity error=0.1063277, median amplification=1.3048688, p90=1.5228297, confirmed=true"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "minkowski_composition",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01,
        "weight_decay": 0.0
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "weight_decay": 0.0
          },
          "mean": 0.11922426894307137
        },
        {
          "cfg": {
            "lr": 0.003,
            "weight_decay": 0.0
          },
          "mean": 0.057413769885897636
        },
        {
          "cfg": {
            "lr": 0.01,
            "weight_decay": 0.0
          },
          "mean": 0.022922257892787457
        }
      ],
      "full": {
        "mean": 0.020459107705391943,
        "std": 0.0058978451511714595,
        "per_seed": [
          0.02743070386350155,
          0.02446400746703148,
          0.017893968150019646,
          0.021900352090597153,
          0.011625968851149082,
          0.025349821895360947,
          0.010963048785924911,
          0.02404499053955078
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.0010090162832057104,
      "std": 0.0002896170011232185,
      "per_seed": [
        0.00107665010727942,
        0.0011185563635081053,
        0.0006614169105887413,
        0.0014436655910685658,
        0.0006137294112704694,
        0.0012164852814748883,
        0.0007038944750092924,
        0.0012377321254462004
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.019450091422186233,
      "idea_wins": 8,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.02635405375622213,
        -0.023345451103523374,
        -0.017232551239430904,
        -0.020456686499528587,
        -0.011012239439878613,
        -0.02413333661388606,
        -0.01025915431091562,
        -0.02280725841410458
      ],
      "p_value": 0.0081,
      "mde": 0.005072785876795095,
      "mde_rel_pct": 24.79475620267728,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "custom_track": {
      "name": "minkowski_composition",
      "file": "minkowski_track.py",
      "domain": "structured_convex_composition"
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.001,
          "weight_decay": 0.0
        },
        "mean": 0.002880843123421073
      },
      {
        "cfg": {
          "lr": 0.003,
          "weight_decay": 0.0
        },
        "mean": 0.0020782598585356027
      },
      {
        "cfg": {
          "lr": 0.01,
          "weight_decay": 0.0
        },
        "mean": 0.001075072243111208
      }
    ],
    "mechanism_signature": {
      "quantity": "trained additive output error and local perturbation amplification",
      "predicted": {
        "componentwise_addition": "nonnegative weighted sum",
        "amplification_bound": "approximately alpha+beta"
      },
      "observed_mean_additivity_error": 0.10632766596972942,
      "observed_median_local_amplification": 1.3048688173294067,
      "observed_p90_local_amplification": 1.5228296875953675,
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "minkowski_track.py",
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the custom 2D ellipse-support-tuple regression track was tested; built-in tabular, vision, sequence, and dynamics tracks were not structurally appropriate. The experiment used 8 directions and did not test projected-gradient cone-margin enforcement, 3D objects, longer learned composition chains, or generic polygon-hull latency.",
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 1,
  "system_judged": true
}