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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented and ran the Fisher observability regularizer on the registered dynamics track with the matched rnn_small architecture. The trained-model signature confirmed a small observability improvement, but the best idea test MSE was worse than baseline and the paired permutation test was non-significant.",
  "metrics": {
    "baseline": "Mean test MSE 0.0198293367 over 8 seeds; tuned baseline LR=0.01 from sweep [0.001, 0.003, 0.01].",
    "idea": "Best idea alpha=0.0001 at LR=0.01; mean test MSE 0.0223195173 over 8 seeds. Paired delta=+0.0024901805, p=0.5164, 3/8 wins; mechanism signature confirmed but task metric worsened."
  },
  "bench_report": {
    "bench_version": 1,
    "track": "dynamics",
    "model": "rnn_small",
    "metric_direction": "lower is better",
    "n_seeds": 8,
    "baseline": {
      "best_cfg": {
        "lr": 0.01
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001
          },
          "mean": 0.3787969872355461
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.07372267078608274
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.020728168543428183
        }
      ],
      "full": {
        "mean": 0.01982933672843501,
        "std": 0.009734209738742424,
        "per_seed": [
          0.03869374096393585,
          0.017570657655596733,
          0.019120153039693832,
          0.017528541386127472,
          0.024672934785485268,
          0.026707936078310013,
          0.007166414521634579,
          0.007174315396696329
        ],
        "n": 8
      }
    },
    "idea": {
      "mean": 0.022319517272990197,
      "std": 0.006939298939271748,
      "per_seed": [
        0.021349677816033363,
        0.01931118033826351,
        0.024120593443512917,
        0.02635839208960533,
        0.02215566113591194,
        0.03315800800919533,
        0.00711813336238265,
        0.024984491989016533
      ],
      "n": 8
    },
    "comparison": {
      "delta_mean": 0.0024901805445551872,
      "idea_wins": 3,
      "n_pairs": 8,
      "per_seed_diffs": [
        -0.01734406314790249,
        0.0017405226826667786,
        0.005000440403819084,
        0.00882985070347786,
        -0.002517273649573326,
        0.006450071930885315,
        -4.828115925192833e-05,
        0.017810176592320204
      ],
      "p_value": 0.5164,
      "mde": 0.008493995040048899,
      "mde_rel_pct": 42.8354975074311,
      "verdict": "no significant win",
      "system_worked": false
    },
    "mechanism_signature": {
      "type": "trained_model_fisher_signature",
      "values": {
        "baseline": {
          "test_mse_observed": 0.026402993127703667,
          "fisher_lambda_min_predicted": 0.002320722443982959,
          "fisher_lambda_max_predicted": 0.013881671242415905,
          "condition_predicted": 5.982430458068848
        },
        "idea": {
          "test_mse_observed": 0.026506217196583748,
          "fisher_lambda_min_predicted": 0.002401451813057065,
          "fisher_lambda_max_predicted": 0.014348627999424934,
          "condition_predicted": 5.977519512176514
        },
        "confirmed": true
      },
      "confirmed_definition": "true only when trained idea increases mean predicted lambda_min and lowers condition number"
    },
    "idea_sweep": [
      {
        "alpha": 0.0001,
        "full": {
          "mean": 0.022319517272990197,
          "std": 0.006939298939271748,
          "per_seed": [
            0.021349677816033363,
            0.01931118033826351,
            0.024120593443512917,
            0.02635839208960533,
            0.02215566113591194,
            0.03315800800919533,
            0.00711813336238265,
            0.024984491989016533
          ],
          "n": 8
        }
      },
      {
        "alpha": 0.0003,
        "full": {
          "mean": 0.022440086351707578,
          "std": 0.006982779445894687,
          "per_seed": [
            0.021398302167654037,
            0.019377948716282845,
            0.024245459586381912,
            0.02637545019388199,
            0.02218914031982422,
            0.03333896026015282,
            0.00717194564640522,
            0.025423483923077583
          ],
          "n": 8
        }
      },
      {
        "alpha": 0.001,
        "full": {
          "mean": 0.022734143771231174,
          "std": 0.0071032232805968085,
          "per_seed": [
            0.021538222208619118,
            0.01957051269710064,
            0.024685418233275414,
            0.026499632745981216,
            0.022289788350462914,
            0.034026920795440674,
            0.007357491180300713,
            0.02590516395866871
          ],
          "n": 8
        }
      }
    ],
    "protocol_notes": "Dynamics is the structural match: controlled pendulum multi-step rollout. Same rnn_small, data, epochs, batch, and baseline-selected LR; only Fisher loss differs."
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 stage2_bench.py",
  "files": [
    "stage2_bench.py",
    "bench_report.json"
  ],
  "limitations": "The MVP used 4 epochs and 240 training/240 test samples after the full higher-order Jacobian version exceeded the runtime budget. The regularizer is a two-sample Fisher proxy over hidden trajectory sensitivity to the first two input state coordinates, not a full RSSM latent-state Fisher matrix; larger datasets, longer training, alternate auxiliary channels, and broader hyperparameter searches were not tested.",
  "system_verdict": "failed",
  "practical_verdict": "inconclusive",
  "mechanism_ok": 0,
  "system_judged": true
}