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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented the nonlinear hydrodynamic optimizer as a local modification of the tabular mlp_tiny training loop and ran 8 paired seeds with a shared three-value learning-rate sweep. The idea had slightly lower mean test MSE than tuned Adam, but the paired permutation test was not significant, so there is no validated benchmark win. The trained-model mechanism signature confirmed conservative allocation mass preservation to numerical precision, but this does not establish an optimization benefit.",
  "metrics": {
    "baseline": "Adam, mlp_tiny, tuned lr=0.01: mean test MSE 7.1135499477, std 0.4462617644, 8 seeds; baseline sweep means: lr=0.001 -> 17.0920653343, lr=0.003 -> 11.1259500980, lr=0.01 -> 6.9834923744.",
    "idea": "Nonlinear hydrodynamic allocation with density-dependent diffusivity and mobility-derived noise, best lr=0.01 and noise=0: mean test MSE 7.0933622718, std 0.4524122206, paired delta -0.0201876760, p=0.258, 3/8 paired wins."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "tabular",
    "model": "mlp_tiny",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 17.09206533432007
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 11.12595009803772
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 6.983492374420166
        }
      ],
      "full": {
        "mean": 7.1135499477386475,
        "std": 0.44626176436687165,
        "per_seed": [
          6.864531517028809,
          7.168848514556885,
          7.027814865112305,
          6.872774600982666,
          6.30631685256958,
          7.218708515167236,
          7.5617499351501465,
          7.887654781341553
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 7.093362271785736,
      "std": 0.45241222062673386,
      "per_seed": [
        6.777974605560303,
        7.191386699676514,
        7.057932376861572,
        6.801158428192139,
        6.306606292724609,
        7.222118854522705,
        7.4917168617248535,
        7.898004055023193
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": -0.020187675952911377,
      "idea_wins": 3,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.08655691146850586,
        0.022538185119628906,
        0.030117511749267578,
        -0.07161617279052734,
        0.0002894401550292969,
        0.00341033935546875,
        -0.07003307342529297,
        0.010349273681640625
      ],
      "p_value": 0.258,
      "mde": 0.03972406104311891,
      "mde_rel_pct": 0.5584280891391918,
      "verdict": "no measurable effect",
      "system_worked": false
    },
    "mechanism_signature": {
      "predicted_mass_error": 0.0,
      "observed_mass_error": 4.440892098500626e-16,
      "predicted_conservative": true,
      "observed_mean_activity_variance": 1346.4940232817462,
      "density_mean": 1.0,
      "density_std": 0.6109128074216446,
      "confirmed": true
    },
    "idea_sweep": {
      "0.001": {
        "mean": 17.594895243644714,
        "std": 1.455041940026395,
        "per_seed": [
          15.97562313079834,
          17.117998123168945,
          18.321380615234375,
          16.85783576965332,
          15.77338981628418,
          17.865934371948242,
          18.211532592773438,
          20.635467529296875
        ],
        "n": 8
      },
      "0.003": {
        "mean": 10.802422881126404,
        "std": 0.9665300052415943,
        "per_seed": [
          9.816262245178223,
          11.428475379943848,
          10.502289772033691,
          10.368227005004883,
          9.419925689697266,
          10.578962326904296,
          11.818642616271973,
          12.486598014831543
        ],
        "n": 8
      },
      "0.01": {
        "mean": 7.093362271785736,
        "std": 0.45241222062673386,
        "per_seed": [
          6.777974605560303,
          7.191386699676514,
          7.057932376861572,
          6.801158428192139,
          6.306606292724609,
          7.222118854522705,
          7.4917168617248535,
          7.898004055023193
        ],
        "n": 8
      }
    },
    "protocol": {
      "paired_seeds": [
        0,
        1,
        2,
        3,
        4,
        5,
        6,
        7
      ],
      "shared_lr_union": [
        0.001,
        0.003,
        0.01
      ],
      "track_rationale": "tabular is the built-in structural match for optimizer interventions"
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_hydro.py",
  "files": [
    "bench_hydro.py",
    "bench_report.json",
    "bench_output.txt"
  ],
  "limitations": "Only the structurally matched tabular track was tested; vision, sequence, and dynamics were not tested. The benchmark used 12 epochs and 1000/400 train/test examples rather than larger budgets. The selected best hydrodynamic configuration used zero injected noise; no broader sweep over alpha, diffusivity, beta, or noise was performed. The report confirms conservation but does not quantify a matched-loss reduction in block-activity variance against baseline.",
  "system_verdict": "partial",
  "practical_verdict": "no_effect",
  "mechanism_ok": 1,
  "system_judged": true
}