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

(универсальный)

Ответ агента:

{
  "worked": true,
  "confidence": 8,
  "verdict": "Implemented the Agnostic Geometry-Prior Mixer and ran the full 8-seed tabular benchmark with a parity-matched learning-rate sweep. The idea achieved test MSE 1.7247 versus baseline 3.4863, with all 8 paired wins and permutation p=0.0081, satisfying the benchmark's significant-win criterion. The trained-model signature showed a nonconstant learned gate, though the mixer has more parameters and the branch errors indicate the gain should not be treated as isolated proof of the analytic prior itself.",
  "metrics": {
    "baseline": "Friedman#1 tabular mlp_tiny, best lr=0.006, 30 epochs: mean test MSE 3.4863 ± 0.6332; per-seed [2.9687, 2.9661, 3.3915, 2.9757, 4.9135, 3.1062, 3.9271, 3.6418].",
    "idea": "GeometryMixer, best lr=0.006, 30 epochs: mean test MSE 1.7247 ± 0.2112; per-seed [1.7298, 1.7004, 1.6195, 1.3495, 1.6963, 1.6350, 2.0825, 1.9848].",
    "paired_delta": "-1.7616 MSE; 8/8 wins; permutation p=0.0081.",
    "mechanism_signature": "mean_alpha=0.2709, alpha_std=0.1770, alpha-support correlation=-0.0181, free-branch MSE=16.0480, geometry-branch MSE=71.3140, mixture test MSE=1.7298, confirmed=true"
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.006,
        "epochs": 30
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "epochs": 30
          },
          "mean": 6.278503656387329
        },
        {
          "cfg": {
            "lr": 0.003,
            "epochs": 30
          },
          "mean": 3.3817883133888245
        },
        {
          "cfg": {
            "lr": 0.006,
            "epochs": 30
          },
          "mean": 3.0755263566970825
        }
      ],
      "full": {
        "mean": 3.486333578824997,
        "std": 0.633233116371069,
        "per_seed": [
          2.968747138977051,
          2.9661331176757812,
          3.391489028930664,
          2.975736141204834,
          4.913453578948975,
          3.106180191040039,
          3.927138328552246,
          3.6417911052703857
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 1.7247163504362106,
      "std": 0.2112279864426216,
      "per_seed": [
        1.729775071144104,
        1.700377106666565,
        1.6194583177566528,
        1.3495277166366577,
        1.6963016986846924,
        1.6350337266921997,
        2.0824673175811768,
        1.9847898483276367
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -1.7616172283887863,
      "idea_wins": 8,
      "n_pairs": 8,
      "per_seed_diffs": [
        -1.2389720678329468,
        -1.2657560110092163,
        -1.7720307111740112,
        -1.6262084245681763,
        -3.2171518802642822,
        -1.4711464643478394,
        -1.8446710109710693,
        -1.657001256942749
      ],
      "p_value": 0.0081,
      "mde": 0.5248752320909208,
      "mde_rel_pct": 15.055221200830132,
      "verdict": "idea better (significant)",
      "system_worked": true
    },
    "idea_sweep": [
      {
        "cfg": {
          "lr": 0.001,
          "epochs": 30
        },
        "mean": 3.4459262788295746,
        "std": 0.2142069786440496
      },
      {
        "cfg": {
          "lr": 0.003,
          "epochs": 30
        },
        "mean": 1.7938710153102875,
        "std": 0.18505377667547435
      },
      {
        "cfg": {
          "lr": 0.006,
          "epochs": 30
        },
        "mean": 1.7247163504362106,
        "std": 0.2112279864426216
      }
    ],
    "matched_structure": "Friedman#1 tabular regression; regularization/MLP intervention",
    "hyperparameter_parity": "baseline and idea both evaluated lr={0.001,0.003,0.006}, epochs=30",
    "selected_idea_cfg": {
      "lr": 0.006,
      "epochs": 30
    },
    "mechanism_signature": {
      "trained_test_mse": 1.729775071144104,
      "free_branch_mse": 16.04800796508789,
      "geom_branch_mse": 71.31401824951172,
      "mean_alpha": 0.2708725333213806,
      "alpha_std": 0.17698463797569275,
      "alpha_support_corr": -0.01807535826279521,
      "initial_alpha_at_beta_minus4": 0.01798621006309986,
      "predicted": {
        "learned_gate_nonconstant": true,
        "prior_use_should_be_suppressible": true
      },
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_experiment.py",
  "files": [
    "bench_experiment.py",
    "bench_report.json"
  ],
  "limitations": "Only the structurally matched built-in tabular track was tested; vision, sequence, and dynamics tracks were not applicable. The idea has a larger parameter count than mlp_tiny, no parameter-matched control or wall-clock/FLOP normalization was run, and the fixed geometry prior was not swept over shifted or irrelevant variants in stage 2.",
  "system_verdict": "worked",
  "practical_verdict": "helps",
  "mechanism_ok": 1,
  "system_judged": true
}