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

(универсальный)

Ответ агента:

{
  "worked": false,
  "confidence": 9,
  "verdict": "Implemented the residual-tightened safety loss on the structurally matched dynamics track using paired rnn_small systems and ran 8 seeds with parity-complete learning-rate coverage plus a kappa sweep. The trained-model mechanism signature was confirmed quantitatively, with residual-to-margin correlation 0.9318, but the idea significantly worsened test MSE: 0.071180 versus baseline 0.000548, paired delta +0.070632 and permutation p=0.0081. Therefore the idea did not win on this standard benchmark.",
  "metrics": {
    "baseline": "rnn_small MSE mean 0.0005480458, std 0.0001978719; best lr=0.01; full per-seed values [0.00047382, 0.00055789, 0.00041418, 0.00092531, 0.00045364, 0.00026837, 0.00067770, 0.00061346]",
    "idea": "Residual-tightened loss, best cfg lr=0.001 and kappa=0.25; MSE mean 0.07118007, std 0.00696685; full per-seed values [0.06354859, 0.07997369, 0.08213101, 0.06680076, 0.07302123, 0.06840987, 0.0638487, 0.0717067]",
    "comparison": "paired delta idea-baseline=+0.07063202; idea wins 0/8; permutation p=0.0081; verdict=idea worse (significant)",
    "mechanism_signature": "Observed residual-to-tightening-margin correlation=0.93184498; confirmed=true"
  },
  "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": 0.0018504196486901492
        },
        {
          "cfg": {
            "lr": 0.003
          },
          "mean": 0.00105226032610517
        },
        {
          "cfg": {
            "lr": 0.01
          },
          "mean": 0.0005928000318817794
        }
      ],
      "full": {
        "per_seed": [
          0.000473822612548247,
          0.0005578889395110309,
          0.00041418071486987174,
          0.0009253078605979681,
          0.0004536386113613844,
          0.0002683700295165181,
          0.0006777027156203985,
          0.000613455253187567
        ],
        "mean": 0.0005480458421516232,
        "std": 0.00019787191330725143
      }
    },
    "idea": {
      "best_cfg": {
        "lr": 0.001,
        "kappa": 0.25
      },
      "sweep": [
        {
          "cfg": {
            "lr": 0.001,
            "kappa": 0.25
          },
          "mean": 0.07311351224780083
        },
        {
          "cfg": {
            "lr": 0.001,
            "kappa": 0.5
          },
          "mean": 0.11853109858930111
        },
        {
          "cfg": {
            "lr": 0.001,
            "kappa": 1.0
          },
          "mean": 0.24801216274499893
        },
        {
          "cfg": {
            "lr": 0.003,
            "kappa": 0.25
          },
          "mean": 0.08233587071299553
        },
        {
          "cfg": {
            "lr": 0.003,
            "kappa": 0.5
          },
          "mean": 0.10741026885807514
        },
        {
          "cfg": {
            "lr": 0.003,
            "kappa": 1.0
          },
          "mean": 0.17653513327240944
        },
        {
          "cfg": {
            "lr": 0.01,
            "kappa": 0.25
          },
          "mean": 0.0779394619166851
        },
        {
          "cfg": {
            "lr": 0.01,
            "kappa": 0.5
          },
          "mean": 0.10071836225688457
        },
        {
          "cfg": {
            "lr": 0.01,
            "kappa": 1.0
          },
          "mean": 0.23662616263084413
        }
      ],
      "full": {
        "per_seed": [
          0.06354859471321106,
          0.07997369021177292,
          0.08213100582361221,
          0.06680075824260712,
          0.07302123308181763,
          0.06840986758470535,
          0.06384869664907455,
          0.07170670479536057
        ],
        "mean": 0.07118006888777018,
        "std": 0.006966848239001737
      }
    },
    "comparison": {
      "delta_mean": 0.07063202304561855,
      "idea_wins": 0,
      "n_pairs": 8,
      "per_seed_diffs": [
        0.06307477210066281,
        0.07941580127226189,
        0.08171682510874234,
        0.06587545038200915,
        0.07256759447045624,
        0.06814149755518883,
        0.06317099393345416,
        0.071093249542173
      ],
      "p_value": 0.0081,
      "verdict": "idea worse (significant)",
      "system_worked": false
    },
    "mechanism_signature": {
      "residual_margin_correlation": 0.9318449762336007,
      "confirmed": true
    }
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 bench_residual_shield.py",
  "files": [
    "bench_residual_shield.py",
    "bench_report.json"
  ],
  "limitations": "The standard dynamics track evaluates supervised rollout-state MSE rather than closed-loop violation probability, action deviation, reward, or inference latency. The implementation tests a residual-tightened training loss rather than the paper's full online action projection shield, because the built-in rnn_small benchmark has no explicit policy/action environment interface.",
  "system_verdict": "partial",
  "practical_verdict": "harms",
  "mechanism_ok": 1,
  "system_judged": true
}