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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Built Complete Log-Barrier Natural Gradient for the bench tabular/Friedman regression track, modifying only optimization of the first-layer 64-dimensional bias block while keeping the mlp_tiny system otherwise matched. The exact radial intrinsic-gradient bound held (maximum squared norm 0.999999, zero violation), and inverse-metric scaling strongly attenuated near-boundary steps. On 8 paired seeds, the idea had slightly lower MSE (6.8770 vs 6.9116; delta -0.0345; 6/8 wins), but the permutation p-value was 0.46775, so there was no significant improvement and worked=false.",
  "metrics": {
    "baseline": "Best Adam baseline lr=0.009; full 8-seed mean test MSE 6.911551, std 0.251027. Sweep means: lr=0.001 -> 15.798457, lr=0.003 -> 9.297731, lr=0.009 -> 6.843065.",
    "idea": "Barrier natural-gradient block update lr=0.009, R=2.5; full 8-seed mean test MSE 6.877023, std 0.213014. Paired delta -0.034528, 6/8 wins, permutation p=0.46775; verdict no measurable effect.",
    "math": "max intrinsic barrier-gradient squared norm 0.9999989999995, max bound violation 0, radial formula error 3.33e-16, inverse-metric step ratio at radius 0.9999 versus center 0.00066894.",
    "mechanism": "Across trained models, mean inverse diagonal metric was 1.705785 at final block points versus 3.125 at the center; final boundary q ranged from 0.44985 to 0.60837, confirming finite interior behaviour and attenuation, though no task-metric win."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.009
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 15.798457145690918
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 9.297731161117554
        },
        {
          "cfg": {
            "lr": 0.009
          },
          "mean": 6.843065023422241
        }
      ],
      "full": {
        "mean": 6.911551356315613,
        "std": 0.25102731009725415,
        "per_seed": [
          6.589434623718262,
          6.794177055358887,
          7.121844291687012,
          6.866804122924805,
          6.845246315002441,
          7.470037460327148,
          6.813384056091309,
          6.791482925415039
        ],
        "n": 8
      }
    },
    "idea": {
      "config": {
        "lr": 0.009,
        "epochs": 15,
        "R": 2.5,
        "block": "first_layer_bias",
        "metric": "mse"
      },
      "per_seed": [
        6.712672233581543,
        6.749656677246094,
        6.953096389770508,
        6.8154120445251465,
        6.669680118560791,
        7.3650336265563965,
        6.754833221435547,
        6.995800971984863
      ],
      "mean": 6.877023160457611,
      "std": 0.2130142060373052,
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.03452819585800171,
      "idea_wins": 6,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.12323760986328125,
        -0.04452037811279297,
        -0.1687479019165039,
        -0.0513920783996582,
        -0.1755661964416504,
        -0.10500383377075195,
        -0.05855083465576172,
        0.20431804656982422
      ],
      "p_value": 0.46775,
      "mde": 0.11208041708387258,
      "mde_rel_pct": 1.6216390692294593,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "prediction": "inverse barrier metric decreases toward boundary; bounded intrinsic barrier gradient",
      "predicted_at_center": 3.125,
      "observed_mean_inverse_metric_at_trained_points": 1.705785185098648,
      "confirmed": true
    },
    "math_check": {
      "max_intrinsic_grad_g_squared": 0.9999989999995,
      "max_bound_violation": 0.0,
      "max_radial_formula_error": 3.3306690738754696e-16,
      "step_inverse_metric_ratio_r0.9999": 0.0006689383838711185
    },
    "runtime_sec": 30.761850833892822
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "Only the structurally appropriate built-in tabular optimizer track was tested; no vision, sequence, or dynamics transfer was run. The intervention used a diagonal Hessian metric on one 64-dimensional bias block rather than full/block metrics for all parameters, and no wall-clock overhead comparison was recorded. The mechanism signature confirms attenuation at trained parameter points but does not establish a boundary-proximity sweep across neural training trajectories.",
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}